Retrieval-Retro: Retrieval-based Inorganic Retrosynthesis with Expert Knowledge
Authors: Heewoong Noh
Namkyeong Lee
Gyoung S. Na
Chanyoung Park
Venue: NeurIPS 2024 poster (2024)
Keywords: Inogranic Synthesis
Inorganic Retrosynthesis
Material Science
Primary area: machine_learning_for_other_sciences_and_fields
TL;DR: Inorganic Retrosynthesis

Abstract: While inorganic retrosynthesis planning is essential in the field of chemical science, the application of machine learning in this area has been notably less explored compared to organic retrosynthesis planning. In this paper, we propose Retrieval-Retro for inorganic retrosynthesis planning, which implicitly extracts the precursor information of reference materials that are retrieved from the knowledge base regarding domain expertise in the field. Specifically, instead of directly employing the precursor information of reference materials, we propose implicitly extracting it with various attention layers, which enables the model to learn novel synthesis recipes more effectively.
Moreover, during retrieval, we consider the thermodynamic relationship between target material and precursors, which is essential domain expertise in identifying the most probable precursor set among various options. Extensive experiments demonstrate the superiority of Retrieval-Retro in retrosynthesis planning, especially in discovering novel synthesis recipes, which is crucial for materials discovery.
The source code for Retrieval-Retro is available at https://github.com/HeewoongNoh/Retrieval-Retro.

Retrieval-Retro: Retrieval-based Inorganic Retrosynthesis with Expert Knowledge
Heewoong Noh Namkyeong Lee', Gyoung S. Na²*, Chanyoung Park
KAIST *KICT
(heewoongnoh, mamkyeong96, cy- ngs00krict.re.. re.
Abstract
While inorganic retrosynthesis planning is essential in the field of chemical science, the application of machine learning in this area has been notably less explored compared to organic retrosynthesis planning. In this paper, we propose Retrieval- Retro for inorganic retrosynthesis planning, which implicitly extracts the precursor information of reference materials that are retrieved from the knowledge base regarding domain expertise the field. Specifically, instead of directly employing the precursor information of reference materials, we propose implicitly extracting it with various attention layers, which enables the model learn novel synthesis recipes more effectively. Moreover, during retrieval, we consider the thermo- dynamic relationship between target material and precursors, which is essential domain expertise in identifying the most probable precursor set among various options. Extensive experiments demonstrate the superiority of Retrieval- retrosynthesis planning, especially in discovering novel synthesis recipes, which is crucial for materials discovery. The source code for Retrieval-Retro is available at Retro
in
https:///itthn com httpaa/..w...eeooooooooeeeeeeeee
Introduction
Discovering new materials is a fundamental problem in materials science 128 4 251
providing
innovative options in various industry fields, such as semiconductors and batteries 37
36 On the
other hand, it is also important to establish synthetic routes for newly discovered materials 21,, i.e., retrosynthesis planning, as the ability to synthesize these materials essential for their successful commercialization beyond mere discovery.
For organic materials, retrosynthesis planning approaches identify valid and efficient synthetic routes [8] by breaking down complex target molecules into smaller molecules that are commercially available and easily synthesizable. During the process, they focus on the structural information of target molecules, such as functional groups and reaction centers, that are related widely known organic reaction mechanisms 21 7 22 23 Following the practice of organic retrosynthesis, machine learning (ML) based approaches utilizing the molecular structure expressed SMILES strings or molecular graphs have been extensively studied [45 33
However, unlike organic retrosynthesis, using the atomic structural information of inorganic materials for retrosynthesis presents significant challenges due to the high computational load incurred by the larger number of atoms compared to organic molecules 77] [). and 2) the failure of traditional physical theories for the atomic structure computation caused the inclusion of diverse and unusual elements [T Therefore, a chemical composition-bass basdd approach is essential for retrosynthesis planning of inorganic materials. Besides the challenges of using the atomic structural information, there a lack of a clear and general theory regarding the mechanisms of inorganic synthesis reactions
"oorresponiing Author.
38th Conference on Neural Information Processing Systems (NeurIPS 2024).

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(+) Subset case
Shared Precursors PH, BI, CaCO, PH, 03
(c) Statistics 90.3%
Target Material:
CayBi(PO,)) J)s BiPO,
Reference Material:
New
New Precursors MgHt LiH, Mg. Mgü, La CO2, V2O,
Target Material:
LigM
Reference Material: Ll, Mgg
(d) Thermodynamic Forces
Precursor Sets
Target Material: A,,,
AO, AO2, RCD
400 meV
Subset
9.7%
20 BO2 100 meV
Figure 1:
An example case where (a) the target material shares a subset of precursors with reference material, and (b) the target material has an entirely new set of precursors, without sharing any subset of precursors with reference material. (c) The proportion of subset cases and new cases among the materials newly synthesized from 2017 to 2020. (d) The target material is more likely to be synthesized using the precursor set that exhibits more negative driving force.
34.. For these reasons, inorganic retrosynthesis planning is a more challenging task compared to organic retrosynthesis planning.
Given these challenges inherent in the retrosynthesis planning of inorganic materials, applying existing ML-based organic retrosynthesis methods for inorganic purposes is infeasible. Consequently, there has been limited research into inorganic retrosynthesis planning compared to that for organic materials, As a pioneering work, CVAE [17] generates synthesis variables for target materials using a generative model, and ElemwiseRetro [88] reformulates the precursor prediction task as multi- class classification problem with dozens of curated precursor templates. However, these approaches overlook the common practices in conventional inorganic material synthesis, where chemists identify reference materials similar to the target material and consult established synthesis recipes [12 13
More specifically, due to the aforementioned inherent challenges, chemists often engage in a costly trial-and-error method by referencing precedent recipes of reference materials from prior literature 12. Therefore, as illustrated in Figure (c), the majority of the discovered synthetic routes for a target material share a common set of precursors with the corresponding reference material from previous studies (Figure (a)), whereas only small number of these routes involve completely new set of precursors (Figure (b)). Inspired by the common practice, He et al. [12 propose to retrieve reference materials that are similar to a target inorganic material from a knowledge base of previous studies, and leverage their precursor information for retrosynthesis planning of the target material.
Although the method proposed by He et al. [2] has proven to be effective by emulating standard practices in the field, it faces two significant limitations. The first limitation is that the model's predictive capability is limited to the precursor sets of retrieved reference material, thus inhibiting its capacity to deduce novel synthetic pathway. This is due to the heavy reliance on the precursor sets of reference materials. However, despite a small fraction of materials being synthesized with entirely new synthetic recipes as shown in Figure| (c), it is widely known that discovering novel synthetic routes with entirely new precursors can accelerate the inorganic material synthesis process [29 (2) and facilitate the discovery of cost-effective recipes (26] 30). Thus, despite a large number of materials being synthesized through slight alterations to previously known synthesis recipes due to the complexities of inorganic material synthesis, it remains critical to identify new synthetic pathways
that extend beyond the commonly known synthetic recipes.
The second limitation is that it neglects the widely known domain expertise in the field 17 18 12
the greater (more negative) thermodynamic driving force (AG) between the target material and the precursor set, the more feasible it is to actually form the target material through the precursor set [31 (35) As an example in Figure I (d), given a target material A₂ a precursor set (AO, AO2, BCO) exhibits significantly greater AG compared to another set (A2CO,, BOJ. making more probable that the target material will be synthesized from the first precursor set. Therefore, by analyzing the thermodynamic relationships between the target material and various precursor sets, we can identify which combinations of precursors most feasible for material synthesis. For example, considering such relationship enables the selection of precursor sets that are likely effective starting materials for synthesizing the target material, thereby facilitating successful synthesis. However, previous works [17 18 [2] overlook this crucial domain expertise, leading to their inability to identify these optimal precursor sets.
2

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To this end, we propose novel inorganic retrosynthesis planning approach by implicitly extracting the precursor information with the domain expertise-enharn enhanced reference material retriever. Specifically, instead of directly utilizing precursor information as in He et al. that is, explicitly incorporating precursors from retrieved materials for prediction-we propose implicitly extract this information from reference materials using various attention layers that are designed to enhance and extract the expect precursor details of the reference materials. By providing the model with greater flexibility, to discover novel synthesis recipes that go beyond existing ones. Moreover, to determine which material should be referenced, we utilize well-established domain expertise in the field, i.e., the thermodynamic relationships between the target material and potential precursors, with a novel Neural Reaction Energy retriever. With a novel retriever, our model effectively identifies which material refer to for inorganic retrosynthesis planning of the target material. Our extensive experiments demonstrate the effectiveness of Retrieval-Ren in inorganic retrosynthesis planning, especially discovering novel synthetic recipes, demonstrating the potential applicability of Retrieval-Retro in real-world materials discovery.
In this study, we make the following contributions:
We propose to implicitly integrate the precursor information of reference materials, which enables the model to more effectively discover novel synthetic recipes of inorganic materials.
Furthermore, we introduce novel retriever inspired by domain expertise, which assists the model in effectively determining which material to reference during inorganic retrosynthesis planning.
Extensive experiments demonstrate the effectiveness of Retrieval-Retro in various scenarios, particularly in the year split, which poses a more realistic and challenging environment. Additionally, its exceptional capability in uncovering new synthetic recipes for inorganic materials highlights its potential for practical application in real-world material discovery.
2 Preliminaries
2.1 Problem setup
Inorganic Retrosynthesis Planning with Chemical Composition. In inorganic retrosynthesis planning, determining the atomic structure of inorganic materials poses significant challenges and demands costly computational efforts. As a result, both chemists (21] and prior ML methodologies
18 17 12 depend exclusively composition information. In line with previous studies, also base our approach on the composition information of inorganic materials instead of structural data.
Notations. An inorganic material can be quantitatively described by a composition vector x E Rdd where d represents the total number of unique chemical elements. Fach element in the vector represents the proportion of each chemical element that constitutes the material. As an example, consider the material with chemical formula SiO2, where Si and O correspond to element numbers 14 and 6, respectively. It can be represented as x (I1, T2,..... Ta), where I14 = 3.4 Tg 2 with all other values being zero. Moreover, following previous work (10), we construct fully connected composition graph G (E, A), where the set of elements associated with the nonzero components of x, and A E {1] [1]nxn is the fully connected adjacency matrix with indicating the number of nonzero entries in x. We initialize the initial feature e; of each element e, in with Matscholar 444, whose element embedding is obtained from the vast amount of scientific literature.
Task: Precursor Prediction. Following previous work [12, we formulate precursor prediction as lulti-label classification problem. Given fully connected graph G (E, A) representing an inorganic material, our objective is to train a model J. that predicts the possible precursors for the material, i.e., y F(G), where y E {0, } indicates the label vector with each element signifying the predefined I precursors. That is, each element y; in the label vector y indicates whether i-th precursor is necessary (y,- 1) not (34 0) for synthesizing the target material G.
2.2 Composition Graph Encoder
To begin with, we briefly introduce the compositional graph encoder, which is used to encode the material representation in the paper. Specifically, given a composition graph G = (E, A) ) of a material, we obtain the material representation g as follows:
(I) where "Pooling" refers to the sum pooling of the node representations within the composition graph G, which are derived from GNN encoder. The detailed architecture used in the paper is provided in
g
(GNO(EE, A)),
3

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(a) Training MPC Retriever
(b) Training NRE Retriever
Material: Ag
Complete
Material: A,800,
Step1. Pre
P2
BB
Attention
GNN
DFT-calouated Formation
Erbudting Mi
Step2.
FI
Perturbation
Experimental
Procursor Masking (V)
6A-
Composition
GNN
Formation
Figure 2: (a) Training process of the Masked Precursor Completion (MPC) retriever. (b) Training process of the Neural Reaction Energy (NRE) retriever.
Appendix A. Moreover, we examine whether the proposed framework consistently improve in various GNN architectures in Appendix E.T
Proposed Method: Retrieval-Retro
In this section, we introduce our proposed method Netrieval-Retro a novel inorganic retrosynthesis planning approach that implicitly extracts the precursor information of reference materials retrieved with two complementary retrievers, i.e., Masked Precursor Completion (MPC) Retriever and Neural Reaction Energy (NRE) Retriever. The overall framework of Retro is shown in Figure
3.1 Reference Material Retrieval
Before we extract precursor information from the reference materials, it is essential to decide which material should be referenced for the extraction. To elaborately determine which materials to reference, we employ two complementary retrievers: the Masked Precursor Completion (MPC) retriever and the Neural Reaction Energy (NRE) retriever.
Masked Precursor Completion (MPC) Retriever. MPC retriever identifies the reference materials sharing similar precursors with the target material by learning dependencies between precursors. Specifically, given chemical composition vector x of target material, we obtain its representation m (x), where .M R Rdd' indicates two layered MLPs with non- non-linearity. We define
learnable precursor embedding matrix P Ixxd' whose i-th row p; E Rd represents a learnable generate randomly perturbed precursor vector ÿ from the provided precursor information y. and create perturbed precursor matrix P by applying the perturbed precursor vector y as a mask to the precursor matrix P. Specifically, p; is masked if ÿ: = 0, and is left unchanged otherwise. We then integrate the representation and the perturbed precursor matrix P with cross-attention to form precursor conditioned representation of the material s. Then, the model trained to reconstruct the original precursor vector y from the precursor conditioned representation s and precursor matrix P. by representing probability for each precursor as follows (s p.). The overall training procedure of MPC retriever in Figure (a).
embedding vector for the i-th precursor. Concurrently,
By doing so, enables the retriever to learn dependencies among precursors and the correlation between the precursors and the target material. With the MPC retriever, we calculate the cosine similarity between the representation of the target material and all materials in the knowledge base obtained through M, and retrieve the top K materials that are similar to the target material.
Neural Reaction Energy (NRE) Retriever. Although the MPC retriever effectively identifies reference materials with potentially similar sets synthesis precursors, it overlooks widely recognized domain expertise in the field, i.e., the thermodynamic relationships between materials, which is
essential for the inorganic synthesis process, particularly in selecting appropriate precursors [35] 27
More specifically, the thermodynamic driving force between the target material and precursor set can be quantified by Gibbs free energy (AG), which is measure of the material's 's thermodynamic stability. Under constant pressure and temperature, a negative AG indicates that the energy of the target material is lower than that of the precursor set, signifying that the synthesis reaction can occur widely known that the more negative AG, the more precursor set is likely to synthesize the target material.
spontaneously 31 35). As a result,
Based on this knowledge, it would be beneficial to retrieve materials that have the precursor set capable inducing favorable reactions with the target material by considering the thermodynamic force. AG can be approximated by the difference AH between the enthalpy of the target and

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B
Classifien
Target Material Zo
MPC Retrieved
Cross
TeO:
GNN
Attention
Attention
Referance Materials Retrieval
Retrieved
Mo0;
ZnT ToyMo0g
ZaTeO;
ZnTe Te ToyMos
NRE
Attention
Attention
ZnO
Knowledge
Implicit Precursor Extraction
Figure 3: The overall framework of Retrieval-Rest
precursor set AH follows:
AG RJ AH HTagget H Precursor set:
where the Harggt and Precursor set represent the formation energy of the target and precursor set. Therefore, straightforward solution for calculating AH is to utilize the formation energy of the target material and the precursor set that can directly obtained from the extensive database of structure-bart based DFT-calculated formation energy. However, it is widely known that DFT-calculated values frequently diverge from experimental data, while actual material synthesis occurs real- world wet lab settings [16]. Even worse, there is no guarantee that these databases encompass all materials of interest in inorganic retrosynthesis planning. Consequently, it is essential to develop composition- based formation energy predictor that is specifically designed for experimental data.
To this end, we propose a learnable Neural Reaction Energy (NRE) retriever, which is pre-trained on abundant DFT-calculated formation energy data and then fine-tuned on experimental formation energy data as shown in Figure (b) [66] Specifically. we initially pre-train the NRE retriever using the Materials Project database [4], training the model to predict DFT-calculated formation energy from representations derived from the composition graph encoder (see Section 2.2) Subsequently, we fine-tune the retriever using experimental formation energy data (2), which allows the model to adapt to experimental data. We demonstrate the effectiveness of the training mechanism in Appendix E.2 Finally, given a trained NRE retriever, we can compute the formation energies of the target material and the precursor set of reference materials in the knowledge base. We then retrieve K reference materials that exhibit the most negative AG, selecting from those whose precursors contain the same elements as the target material, along with other common elements such C, H, O, and N. Note that calculations are performed prior to training, so no additional training costs are incurred.
3.2 Implicit Precursor Extraction
Now, we discuss how to extract the precursor information from the reference materials elaborately selected in Section 3.1 While the previous work [22 directly utilizes the precursor information of the reference materials, this limits the model's ability to learn and deduce new synthetic recipes for the target material, which significantly accelerate the materials discovery and reduce the cost of material synthesis. Therefore, we propose to implicitly extract the precursor information from the retrieved material with various attention layers, i.e., self-attention and aatesnant layers, which aim to enhance the representation of reference materials by considering other reference materials and extract the implicit precursor information from the enhanced representation. respectively.
To do composition graphs 9 via the composition graph encoder introduced in Section [.2] As a result, we obtain the representation of target material g. E R.D and the K reference materials G, = [g...... K E xD where g. indicates the representation of the k-th reference material.
first encode the target material and K reference materials using their associated
Reference Enhancing with Self-Attention To effectively extract the precursor information from the reference materials, we first enhance the representation of these materials through self-attention mechanism (39 [24]. This approach encourages the model to selectively determine which information to extract from a particular reference material by considering the relationships among various refer- materials. To do so, first obtain
matrix for reference materials O ig'..... J
where g Φi (g. lg)) indicates the modified representation the k-th reference material regarding the target material, where denotes the concatenation operation, and Φ1 R22 RD is learnable MI.. Note that by modifying the representation of the reference material through concatenation with the target material, we allow the model to extract information pertaining the target material, rather than focusing solely on the reference materials. Then, we implement a self-attention mecha- nism to determine which information extract from the reference material, taking into account the
5

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relationships among the other reference materials:
G Self-Attention( Qg: 1, K 1, V Vor E EEXX
(3)
where 8 = -,...., S indicates the index of the self-attention layers. Different from the conventional self-attention layers 39) we directly utilize G²; as query Q key K and value V without any learnable parameters [24). By analyzing the relationships between the reference materials, the model improves the representations of these materials, thereby supplying more appropriate references to be extracted by the cross-attentie layer.
Reference Selection with Cross- Attention. Lastly, we extract the implicit precursor information by merging the representation of the target material with that of the enhanced reference materials via a cross-attention mechanism [8 42). With cross-attention layers, we expect the model to learn favorable synthesis recipes from reference materials by selectively learning from reference materials with attention weights. More formally, cross-attention layers are formulated as follows:
g: = Cross-Attentionn Qg.-t, Varg) E R
where c = 1,....C C' indicates the index of the reference material representation So and target material representation as inputs to the first cross- attention layer, i.e., _ = S and g? = gi- Moreover, we also utilize gf- as query Qgi-i, and the reference material representation G as key K and value Va identical to the self-attention layer, without any learnable parameters. By employing cross-attention layers between the target material and reference materials, rather than the precursor set of the reference materials, the model effectively accesses the synthetic recipes of reference materials without explicitly using precursor information, thus enabling the discovery of novel synthetic recipes for the target material. We employ this implicit precursor extraction process for the reference materials gathered using both the MPC retriever and the NRE retriever, resulting in gÉMPC and giNRE, respectively.
layers. Note that we use an enhanced
3.3 Model Training
Finally, we compute the model prediction y as follows: ý = Octassiticr (eSMPP//EEERE)) where delassiferr R + g is an MLP with non-linearity. Note that each dimension in y, i.c., Ds. indicates the model's 'ss predicted probability of whether precursor i will be included or not. For model training, we adopt Binary Cross Entropy (BCE) loss, which is commonly used for multi-label classification
learning 5 477.
as: L = + Ei= [y, log(ū)) + (1 y.) log(1 9.)].
4
Experiments
4.1 Experimental Setup
Datasets. We use 33,343 inorganic material synthesis recipes extracted from 24,304 materials science papers 20 following prior studies [2 [8. Due to the lack of an extensive database containing
inorganic synthesis recipes (20). we use the training set as the knowledge base, following a previous
work (12. Additional details about datasets are provided in the Appendix B
Baseline Methods. We compare Retrieval-Retro with two inorganic retrosynthesis methods (i.e., He et al. [2] and EletwiseRetro [1]), two composition- based representation learning methods (i.e., Roost [0] and CrabNet 4T). and three newly proposed baselines (i.c., Composition MLP and Graph Network (3). Graph Network + MPC) to demonstrate the effectiveness of Retrieval-Retro. The first newly introduced baseline is called Composition MLP, which does not retrieve reference materials but instead relies on the composition vector of the material. He et al. [12] conducts inorganic retrosynthesis planning by using the MPC retriever access reference materials based solely the material's composition vector. ElemwiseRetro [18] acquires precursor information through fully connected graph that represents the constituent elements within the material. Furthermore, two composition- based material representation learning approaches, namely Roost [0O] and CrabNet 41 explore the intricate interactions among elements within materials using message passing and self-attention, respectively. Although these methods are initially designed for property prediction, we have adapted prediction heads so that they can be effectively used for inorganic retrosynthesis planning. We also evaluate two new baselines. Graph Network [3] and Graph Network + MPC. The former predicts precursors without retricving reference materials, while the latter does so after retrieving references. As these methods utilize the same backbone GNN structure as in approach,

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Table 1: Overall model performance in (a) Year split and (b) Random split. "Int." denotes whether the model accounts for interactions among constituent elements, while "Retr." indicates whether the model retrieves reference materials. Bold indicates the best performance, while undeline represents the second best performance.
Year Split
(b) Random Split
Model
Top-K Accuracy
Recall
Top-K Accuracy
Recall
Tap-1
Top-3
Top-5
Top-10
Macro
Micro
Top-1
Top-3
Top-5
Top-10
Micro
Composition MLP
31.60
35.22
36.56
31.42
51.44
55.35
18.1631
(0000)
03.80:
03:35)
09.0091
(143)
00:31
ctal.
(1.81)
11380
49.13
11:771
51.09
(150)
46.72
(1.33
44.75
(1300
(1.11
(1.48)
67.46
68.54
(1.38)
58.55
11.88
59.35
(1:34)
ElemeiseRetr
53.45
57.07
60.84
53.12
53.19
80.93
81.57
72.33
73.26
00.81
18.53
(3.71
10770
01.01
1964
16.70
(1.51
(167)
29.41
(9:9)
Rocel
54.38
57.82
58.82
52.01
$4.04
92.32
83.07
73.38
74.46
0935)
(8S0)
21.00
11:5)
09.5)
18.70)
(0901)
(9:11
10,50)
(9.88)
[8.40
(1:2)
CrabNet
57.15
61
62.44
64.14
56.79
56.87
78.69
81.62
82.27
83.35
73.27
74.28
.517)
(8:5)
(9871
09771
(8777)
(0.75)
(9:11)
(0,7))
(3.50
13:31)
(9:49
Graph Netwerk
58.95
63,10
64.07
65.30
58.54
58.61
77.91
81.55
82.37
$3.50
72,95
73.83
(040
23:48
10A2
01421
(841)
11.30)
03.98)
(092)
00.98
11.50
(1:99
Graph Network + MPC
60.01
64.15
65.15
67.19
59.61
$9.66
79.09
$2.95
83.82
84,97
73.86
74.81
(1.32)
(1.10)
(1.37)
(305)
(1.00)
(1.00)
(1223)
(1:33)
(1.99)
(0.44)
13.34)
(1:23)
Rotrioval Ratro
61.16
65.92
67.18
69.45
60.97
61.06
79.81
83.62
84.46
55,70
74.01
75.49
(1:34)
(0.7)
(1.79
0167)
(0.77)
(0/80)
(3:71
00.70)
(0.88)
1998)
(3.94)
they can be viewed as ablated versions of Retrieval-Retro. We provide further details about the compared baseline methods in Appendix C
We perform evaluations under two distinct settings, namely, random split and year split. Following prior studies, under the random split setting, we randomly split the dataset into train/valid/test of 80/10/10% On the other hand, under the year split setting [[2], the training set includes synthesis recipes from papers published up to 2014, the validation set includes recipes from papers published in 2015 and 2016, and the test set includes recipes from papers published between 2017 and 2020. This setup closely replicates the real-world material discovery conditions, allowing for the evaluation of model performance without the need for costly wet-lab experiments.
Evaluation Protocol.
Following previous works in retrosynthesis planning 45 40 18 9]. accuracy to evaluate the effectiveness of Retrieval-Retro. In addition, we also employ Macro and Micro Recall, which are commonly used as metrics in the multi-label classification problem We provide further details on evaluation protocol in Appendix D
we adopt Top-K exact match
4.2 Empirical Results
Effectiveness of Retrieval-Retro in Inorganic Retrosynthesis Planning. In Table 1 we have the following observations: 1) Modeling the interaction among the constituent elements (Int. v) proves more effective than simply representing the material composition vector (Int. X). This demonstrates the importance of modeling the interaction between the composition elements not only for the material property prediction task [10 41) but also for inorganic retrosynthesis planning. 2) By comparing the methods that utilize retrieval (Retr. // with those that do not (Retr. X), it becomes apparent that using precursor information from reference materials contained in synthesis literature knowledge base enhances the precursor prediction performance. This underscores the significance of reference materials, which is also a standard practice in the traditional material synthesis process. 3) We also find that Eatrieval-Retro surpasses all baseline models, indicating that our method successfully facilitates inorganic retrosynthesis planning. Furthermore, there is a significant performance enhancement over baseline methods in the year split setting (Table (a)), which is a more realistic and challenging scenario, proving Retrieval-Retroo 's efficacy in real-world inorganic material synthesis processes.
Discovering Novel Synthesis Recipes. As shown in the previous section, the precursor information of reference materials is beneficial for the inorganic retrosynthesis planning of the target material. This observation raises natural question: How we effectively integrate the information from reference materials into synthesis recipes, particularly for novel synthesis recipes? To answer this question, we evaluate how each component in the model affects the model's capability of deducing novel synthetic recipes for the target material. To do so, we first divide the test set of the year split dataset into Subset case and New case. As shown in Figure I the subset case includes target materials that share common comprises target materials that have an entirely new set of precursors. Moreover, we mainly compare Retrieval-Retrr with "Graph Network MPC" in Table I since the only difference between the models lies in whether the model directly utilizes the precursor information from reference materials. Then, we separately assess the model's performance in the subset case and the new case.
of precursors with those in the training set, while the new case
7

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Table 2: Overall model performance in subset case and new case. "Refer." indicates whether the model explicitly or implicitly uses the precursor information from reference materials.
Retriever
Case
Model
Refer.
MPC
Top
Top-3
Top
Top 10
Top-1
Top-3
Tup-5
Top-
X
63,95
67.95
70,83
16:37
22.00
27:93
Graph Network
Explicit
65.01
18343
69.06
.0:3
69.96
(0.58
72.03
(831)
17.63
1.90)
22.45
(3.4%)
24.22
(3.40)
26:22
11:50
11.10
(1.37)
(1.23)
(192)
0246)
(2.30)
(2.53)
(2.7g)
Retrievai-Fetro
Implicit
x
66.00
65.07
1950
70.51
69.44
70.41
71.76
(1.30)
73.92
72:47
(1:90
20.15
19:70
01180
27.04
24.52
1.43)
26.37
26. 30
(1.78)
31.56
30.15
(2.55
10:30
(2.00
(090)
0..2%
(.59)
(2.65)
(3.40)
In T we have following observations: 1) By comparing the Graph Network with and without the MPC retriever, we find that incorporating the precursor information from reference materials enhances model performance in subset cases. One interesting observation is that, this information negatively impacts performance in Top-10 new case, demonstrating that it can hinder the model's Retrieval-Retr,, which implicitly
ability to deduce novel synthetic pathways. 2) Conversely, integrates the precursor information, consistently shows performance improvements in both the subset and new cases, particularly widening the performance gap in the new case- a more realistic and challenging scenario. These findings illustrate the importance of how precursor information from reference materials should be integrated, particularly in identifying new synthetic pathways, which can speed up the material discovery process and reduce synthesis costs. 3) Interestingly, we find that the NRE retriever also consistently enhances the model performance, benefit likely stemming from its complementary relationship with the MPC retriever. Specifically, while the MPC retriever captures dependencies between precursors and the target material based on previously observed data, novel synthesis recipes might diverge from the existing synthesis patterns documented in the literature. On the other hand, the NRE retriever employs domain expertise that is independent of these existing patterns, thus filling gaps that the MPC retriever might overlook. By accessing such complementary reference materials, the model is able to acquire additional new precursor information, which contributes the observed performance improvements. In Table 4 analysis of the materials retrieved by each retriever and examine their impact on model predictions.
we conduct qualitative
In conclusion, we find that each element of Retrieval-Retro plays an effective role in inorganic retrosynthesis planning, particularly in identifying new synthesis recipes, showcasing its potential influence in the field of materials discovery.
4.3 Model Analysis
Effects of Retriever. To verify the effect of the retrievers. we conduct ablation studies by removing the retriever modules. In Table we have following observations: 1) When reference materials are randomly retrieved without using trained retrievers, the model extracts pre- cursor information that is irrelevant to the synthesis of the target material, leading deterioration in performance. 2) Furthermore, using just one of the retrievers (i.c., either MPC or NRE) leads to complemen-
Ablation Studies:
underperforms the case when both retrievers are used, showing that the two retrievers tary to each other as discussed in Section 4.2] In conclusion, we contend that both MPC and NRE retrievers should be employed to provide informative reference materials to the model, facilitating the extraction of valuable information in inorganic retrosynthesis planning. We provide further ablation
studies in Appendix E.3
Sensitivity Analysis: Size of Knowledge Base. We evaluate how the size of the knowledge base affects the model performance. More precisely, from the original database, we sample various sizes of knowledge bases, such as 20%, 40%, and up to 100% (Full) of the size of the original database, and then retrieve reference materials from these sampled subsets. In Figure (a), as expected, the larger the knowledge base, the more accurate the model's 's predictions. These findings illuminate potential avenues for further development of our model, particularly more inorganic synthetic recipes are uncovered in the future.
Sensitivity Analysis: Number of Reference Materials. Moreover, we investigate how the varying number of reference materials K affects the model performance. In Figure we notice that model performance improves with an increase in the number of references up to a certain point, specifically K = 3. This reaffirms the importance of incorporating precursor information from reference materials for inorganic retrosynthesis planning. However, increasing the number of reference materials beyond K 3 does not further enhance model performance, likely due to the introduction of irrelevant or
8

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Top-1
Top-3
Top-5
Top-10
Table 3: Effect of retrievers on model performance.
68-
Top-K Accuracy
Recall
66
Retriever
Top-1
Top-3
Top-5
Top-10
Macro
Miero
Acco
(1:44)
(6:51:
21:5
1868
13:44:
62
MPC only
$9.96
64.49
63.57
68.14
59.57
59.64
12:9
0029)
21.111
12.01
NRE only
60.28
64:70
65.75
68.00
59,88
59.95
1bt Num. Reference Materials
Retrieval-Retro (MPC+ NRF)
Figure 4: Sensitivity Analysis results. KB refers to the knowledge base.
(2.36)
(9.7)
(6.70)
(1.39)
(8.3)
(0.03)
noisy precursor information that does not pertain to the target material. Thus, extracting precursor information from a suitable number of retrieved materials essential for optimal performance.
Qualitative Analysis. We present a qualitative analysis of our proposed method in retrosynthesis planning for the target material pbg Lix(P20r)>(Pa0n))) sor set: [LigCOg. N2,H2PO,, Pb As shown in Table when only the MPC retriever is used, the method fails predict the entire precursor set due insulficient extraction of precursor information from the retrieved materials. However, when the NRE retriever used alongside the MPC retriever, our method successfully predicts the complete precursor set for the target material. This success is attributed the NRE retriever, which complements the model by providing complementary retrieved materials whose precursors are feasible for synthesizing the target material, thereby enabling the extraction of diverse precursor information. For instance, the NRE retriever allows our method to extract precursor information from (P.(DO)). which contains the essential precursor PbO, a direct precursor for the target material. Due to the complementary nature of the retrievers, our method can effectively extract precursor information from informative reference materials, leading to enhanced predictions.
which can be synthesized from the precur-
Table 4: Qualitative Analysis
(Trrget Material: Pby (1(i((P())))))p)0))))))
Model
Retriever
Retrieved Material
Corresponding Precursor Sets
Predicted Precursor Set
Answer
LiNaPbPO
(Li,CO,, H,PO,, Na, Pb,O.) (Li,CO, II,, NaPO,) [A). V2Og. L2O,,,LLHHH,0.
Only MPC
MPC
[Li,CO,.NH,I1,PO, NH,,I,PO,)
x
Lig LigV
LiNaPhPO
(L,,CO, HgPO,, NayCO,, Ph₂O,) (LyCO, NH, HH,H, NaPO,) [Al, V,O,, LiH,PO..)
МPC
MPC NRE
LigVi oaPon
(L&CO,, NH,H,PO,, P60)
(Ours)
Pby(PO.)a LigP
(P5O, NH.H.PO.)
NRE
[i) Pb)
5
Related Works
5.1 Machine Learning for Inorganic Retrosynthesis
Owing to the intricate nature of inorganic retrosynthesis, the production of inorganic materials entails a mix of numerous synthetic variables, leading to a variety of subtasks: 1) Learning favorable reaction pathways with thermodynamic variables when target and precursor set are provided 22 27 33),
2) Employing regression models to predict conditions of the reaction pathway, such as heating temperature and time [3), and 3) Predicting possible precursor sets for a target material. In this work, we focus on the precursor selection task, which aims to predict feasible sets of precursors for synthesizing a target material 17 18 12).
Precursor Prediction Task. As pioneering work, CVAE 17 generates synthesis actions and precursors through a generative model, i.e., conditional variational autoencoder (19]. However, frequently produces chemically invalid precursors by relying on the generative model. ElemwiseRetro [8] alleviates the issue by formulating the precursor prediction task as multi-class classification with dozens of manually created precursor templates. While it successfully suggests chemically valid precursors, it fails to incorporate knowledge from synthesis literature, which is common practice in the field of material science. More recently, inspired by the chemists' synthesis practice, He et al. [12] propose use the precursor information of materials that are similar to the target material, which is retrieved from the related literature. This approach utilizes the precursors of the retrieved material explicit conditions for predicting the precursors of the target material. However, such

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explicit utilization of precursor information can inhibit the model's capability in providing novel synthetic routes for target material. Different from the previous work, we propose implicitly utilize precursor information of similar material rather than directly using it.
5.2 Composition-based representation learning for Inorganic materials
While most machine learning approaches for inorganic materials predominantly utilize structural information, in real-world material discovery, structural data is often unavailable due to the high costs of computational resources. Consequently, some ML methods opt to use compositional information of inorganic materials instead of structural details. For example. ElemNet [15] proposes to learn the representation of material with compositional information using deep neural networks (DNNs). Roost 10 learns material representation by building fully connected graphs based the composition to model interactions between elements by graph neural networks (GNNs). Additionally, CrabNet 41 successfully applies a self-attention mechanism to the clement-derived matrix to accurately predict the properties of materials. Although these methods were originally designed for predicting material properties, they can also be applied to inorganic retrosynthesis as they similarly rely on the composition information of materials.
5.3 Difference between Inorganic Retrosynthesis and Organic Retrosynthesis
Both organic and inorganic retrosynthesis are challenging tasks that predict the synthesis of mate- rials by breaking down the target material into simpler precursors. However, there are significant differences between organic and inorganic retrosynthesis. Organic retrosynthesis 45 40 33
deals with organic compounds, which are molecules primarily composed of elements such as carbon, hydrogen, oxygen, nitrogen, and sulfur. These compounds are represented using molecular structure graphs or SMILES strings. In contrast, inorganic retrosynthesis involves inorganic compounds, which can include a wider variety of elements, often including metals, and have structures that periodically repeat in unit cells. Another key difference lies in the use of structural information during retrosynthesis planning. Organic retrosynthesis utilizes structural information such as functional groups and reaction centers of organic compounds, which indicate the properties of material and its reactivity with other molecules, to predict simpler molecules (precursors) into which the target molecule can be broken down. Inorganic compounds, however, have relatively unexplored generalized synthesis mechanisms compared to organic compounds, and calculating their structures is expensive. Therefore, it is challenging to directly use structural information for retrosynthesis planning. Instead, inorganic retrosynthesis [12] 17 [8] often relies solely on the chemical composition of the materials, distinguishing it from organic retrosynthesis.
6 Limitations
We aim to identify favorable precursor sets by considering the thermodynamic relationships between materials and their precursor set. However, in the actual synthesis process. the phase changes of materials are influenced by synthesis temperature, synthesis time, pressure condition and pairwise reactions between precursors. Taking these factors into account would enable more accurate precursor set predictions. Nevertheless, in situations where experimental data (such as temperature and pressure) are unavailable, we estimate the reaction energy solely from the formation energy calculated under consistent temperature and pressure conditions using a trained predictor, derived from the composition of materials. Considering multiple synthesis conditions can lead to more precise predictions of precursor sets.
7 Conclusion
In this study, we introduce Retrieval-Retro novel method for inorganic retrosynthesis planning by extracting the precursor information of retrieved reference material implicitly. To do so, we employ various attention layers that enhance and extract the information from the reference material. Moreover, we design neural reaction energy (NRE) retriever that provides complementary reference material to the MPC retriever, allowing Retrieval-Retro to integrate precursor information from a broader range of reference materials through domain expertise. Through extensive experiments, including assessments in realistic scenarios, we demonstrate the effectiveness of implicit extraction of precursor information and NRE retriever in discovering novel synthesis recipes of target material, demonstrating the potential impact of Retrieval- Retro in the field.
10

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Acknowledgements
This study was supported by Korea Research Institute of Chemical Technology (No.: KK2351-10), the National Research Foundation of Korea(NRF) grant funded by the Korea government(MIII) (RS- 2024-00406985,, and NRF grant funded by Ministry of Science and ICT (NRF-202MM3J6 1063021).
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Supplementary Material for
Retrieval-Retro: Retrieval-based Inorganic Retrosynthesis with Expert Knowledge
Implementation Details
15
Composition Graph Encoder
15
A.2 Training Details
15
B Dataset
15
Baseline Methods
16
D Evaluation Protocol
17
Additional Experiments
17
Pertormance on Various Backbone Architecture
17
Neural Reaction Retriever
18
Ablation Study Attention Layer
18
Model Training and Inference Time
19
Broader Impacts
19
Pseudo Code
19
14

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A Implementation Details
In this section, we provide implementation details of PestrevallRRttre
A.1 Composition Graph Encoder
As a composition graph encoder, we mainly consider graph network [3], which is the generalized version of various graph neural networks. Building on prior research, our graph neural networks are divided into two components: the encoder and the processor. The encoder is responsible for learning the initial representation of elements, while the processor manages message passing between elements. To describe this more formally, for an element e; and the edge di,j connecting element e; to element ej. the node encoder Prode and the edge encoder Gedge generate initial representations of the element follows:
cf = @node 2
(a:i),
(5)
where e; is the initial feature of element i and a,j is the concatenated feature of element and j. i.c. a. = (c: (e;])ee) Using the initial representations of elements and edges, the processor is designed to facilitate message passing among the elements and to update the representations of both elements and edges in the following manner:
V (e), aly) e! = Wnode (e),
==1==hhode
Σaft" JEE
(6)
where & represents element set comprising the material, and a two-layer MLP with non-linearity, Note that we use three message passing layers within the processor, i.e., = O,...,
A.2 Training Details
Model Training. Our method is implemented on Python 3.8.13, and Torch-geometric 2.0.4. In all our experiments, we use the Adam W optimizer for model optimization. We train the model for 500 epochs across all tasks, while the model is early stopped if there is no improvement in the best validation Top-5 Accuracy for consecutive epochs. All experiments are conducted on a 48 GB NVIDIA RTX A6000.
plyeepparamtees. We have detailed the hyperparameter specifications in the table 5 For Retrieval- Retro, we adjust the hyperparameters within specific ranges as follows: number of message passing layers in GNN I,' in (2, 3), number of cooss-attnttio layers C' in (1,2), size of hidden dimension D in (256), number of self-attention layers S in (1,2), learning rate rj in (0.0001, 0.0005, (.001),bate
size B in (32, 64, 128) and number of retrieved materials K in [1,2,3,4,5,6 We present the test performance based on the best results obtained on the validation set.
Table 5: Hyperparameter specifications Retrieval-
Dataset Split
Hyperparameters
Year Split
Random Split
Message Passing
Layers (T.))
3
3
Self-Attentinn Layers (5)
1
1
A Cross Attenion Layers (C)
2
2
Hidden Dim. (17)
256
256
Learning Rate
0.0001
0.0001
Batch Size (F)
128
32
of Reterence Materials (K)
3
3
B Dataset
In this section, we provide further details on the dataset used for experiments.
15

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Following previous study [2], we use 33,343 inorganic solid-state synthesis extracted
from 24,304 materials science papers [20] obtained from the github repository| [f previous work 12]. Following the preprocessing step of previous work, we obtain a total number of 28,5 885998 target materials, which have diverse number of possible precursor other words, single target material can have multiple corresponding ground-trutt precursor sets. Furthermore, in experiments, exclude materials from the validation and test sets that contain precursors not present in the training set, as the classifier would not be trained on those absent precursors. Consequently, for the year split, we use 24,034 entries for the training set, 1,842 for the validation set, and 2,558 for the test set. For the random split, the number of target materials varies across five different trials due to the differing compositions of the training, validation, and test sets in each trial.
shown in Table 6 In
Table 6: The distribution of data based on the number of ground truth precursor sets.
Precursor sets
2
3
5
6
10
11
12
13
14
17
23
28
29
34
Total
26,944
1,168
257
108
42
2
28,598
we propose to pre-train the Neural Reaction Energy (NRE) retriever with density functional theory (DFT) calculated formation energy and then fine-tune to experimental data.
In Section 3.1
For DFT-calculated data, we use Materials Project [4] database which is an openly accessible database that provides various material properties calculated using DFT. From the database, we have collected 80,162 unique compositions along with their respective formation energies. It is important to note that when a composition is associated with multiple structures and formation energies, we only consider the lowest formation energy for the material, as it is the most likely to exist.
For experimental data 22 we download experimental formation energy data from previous work's repository [16 which aim to develop robust material property prediction models via transfer learning. We then filter the data down to 1,637 entries using the same preprocessing methods as those applied to DFT-calculated data.
For calculating the Gibbs free energy, we the formation energy from the Materials Project that we used is calculated at 0 K and 0 atm using DFT and the experimental formation energy is taken from [16]. which reports measurements at 298.15 K and atm.
C Baseline Methods
In this section, we provide detailed explanations of the baseline methods compared Section 4 We first provide details on the two previous inorganic retrosynthesis planning methods as follows:
He al. [12] introduces a new approach inorganic retrosynthesis planning by retrieving reference target material. Initially, it proposes using a masked precursor completion (MPC) retriever, described in detail in Section 3.1 which depends solely the composition vector x of the inorganic material.
materials that share similar properties with the
ReemwiseRetro [8 introduces a method for inorganic retrosynthesis planning that represents the target material as a fully connected composition graph and predicts the likelihood of various precursor sets from the available precursor templates.
Furthermore, as outlined in Section 5.2 there are existing studies that develop material representations based on the composition information of inorganic materials. Although these studies were originally aimed at predicting material properties, we have adapted the prediction heads make them suitable for inorganic retrosynthesis planning. Here, we provide details on the methods as follows:
Roost [10 suggests employing GNNs learn representations inorganic materials by representing their composition as fully connected graph, with nodes representing the unique elements within the
http::///eaaeeteapppeeeeeeeerrer chermodata/ssub.dde
16

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composition. This enables the model to explore the complex relationships between the constituent elements, thus capturing physically significant properties and interactions.
CrabNet 41 proposes to model the complex interaction between constituent elements with Transformer self-attention mechanism 33 to adaptively learn the representation of elements based on their chemical environment.
In addition to existing studies, we introduce further baseline models that can support our claims as described below:
Composition MLP aims to predict the set of precursors based the composition vector of the inorganic material as follows:
MLM(PPP
The sole distinction between Composition MIP and He al. 12 is whether the model re- trieves reference materials, highlighting the effectiveness of the retrieval mechanism in inorganic retrosynthesis planning.
Graph Network [3 aims to predict the set of precursors based on the composition graph g of the inorganic material as follows:
y = Graph Network(g).
(8)
Graph Network + MPC improves upon He et al. [12 by replacing its material encoder, originally an MI.P that processed the composition vector x, with graph network. Since the primary distinction between Graph Network and Graph Network + MPC is whether the model retrieves reference materials, comparing these methods allows us to evaluate the effectiveness of using reference materials in inorganic retrosynthesis planning. Furthermore, given that Graph Network serves the backbone architecture for Retrieval-Rette Retro, comparing Graph Network + MPC with Retrieval-Reetr enables the assessment of the effectiveness the implicit fusion and NRE retriever.
Evaluation Protocol
We use Top-K exact match accuracy, Macro recall, and Micro Recall for evaluating the capability of Retrieval-Retro in the inorganic retrosynthesis task. To begin evaluation, we first define the number of precursors of each material. Following many multi-label classification works (5 47], the precursor labels are considered to be positive their probabilities ii are greater than 0.5. We set the number of positive precursor labels, L as the number of precursor that each material has.
Top-K exact match accuracy. For Top-K exact match accuracy. we select sets which has L precursors, where each set is chosen based on the highest product of probabilities of its T. precursors. Then, for each of the K sets, if there exists scored as I; otherwise, is scored as O. The average is then calculated over all test matcrials.
that exactly matches the correct precursor set, is
Mícro and Macro Recall. Note that a material can have varied number of possible precursor sets, as shown in Table 6 We use Micro and Macro Recall evaluate such cases. We select the same number precursor sets as the material has in the same way as we evaluate for Top-K exact match accuracy. We then calculated the macro recall by comparing each obtained set with the correct sets for each material. a set matched exactly, was scored as 1; otherwise, it was scored as 0. These scores were averaged for each test material. For micro recall, we calculated the overall average across all test materials.
E
Additional Experiments
E.1
Performance on Various Backbone Architecture
Since Retrieval-Retro is agnostic the various composition graph encoder architectures, validate the effectiveness of Retrieval-Retrro within a variety of GNN architectures. In Figure we observe performance improvements across all GNN architectures tested, confirming that Retrieval- Retro framework can consistently improve vanilla GNN architecture for inorganic retrosynthesis planning.
17

---

Top-1
Top-3
Top-5
Top-10
70
GPV
65
65
65
661
GATReerrood rootl-rroollllttt
Accuracy(5)
Grepht heteook
Grapn Networkc Rottiers
60
55
55
50
50
GAr
Gopib Natwork
Gopah Network
GN
Groph
Gooph
Figure Performance Improvements across Various GNN Backbones
E.2 Neural Reaction Retriever
As described in Section 3.1 we develop a composition- formation energy predictor tailored for experimental data. To do so, we initially pre-train the GNN predictor on extensive DFT-calculated data [4] and then fine-tune whether the pre-train and fine-tune strategy is effective in predicting formation energies. To do so, we divide the entire DFT dataset into three parts: 80% for training, 10% for validation, and 10% for testing. The model is trained over 1,000 epochs, employing early stopping if there is no improvement in the validation loss for 50 consecutive epochs. Similarly, we partition the experimental dataset into 80/10/10% splits, following the same training approach begins training experimental data using pre-trained checkpoints from a model trained on DFT data, also conduct comparisons with model trained exclusively experimental data (Ablation in Table ()). Table / shows that pre- pre-training with DFT-calculated dataset enhances model performance, indicating that the NRE retriever efficiently retrieves reference materials related to formation energies.
experimental formation energy data 32 In this section, demonstrate
the DFT data. While our model
Table 7: Formation energy predictor performance on experimental formation energy data.
DFT
Exp.
MAÉ (eVatoom)
Ablation
,
0.0761
Ours
0.0643
E.3 Ablation Study on Attention Layer
We conduct an ablation study on the self-attention layer Retrieval-Retro. By removing the self-attention layer, Retrieval-Rette representation of the target material without the enhanced reference materials through cross- attention mechanism. As shown in Table this results in a noticeable decline in model performance, underscoring the importance of extracting precursor information from the material's enhanced representation via self-attention. However, even with just the cross-altention mechanism, Retrieval- Retro still outperforms the basic Graph Network.
extracts implicit precursor information by integrating the
Table Effect of attention layers on model performance.
Attention Layers
Year Split
Model
Self-Attention
Cros--Attentinn
Top-1
Top-3
Top-5
Top-10
58.95
63.10
64.07
66.30
Graph Network
(0.1))
(0.53)
(0.68)
(0.62)
Retrieval-Retrr
60.34
65.08
66.40)
68.99
(0.4)
(1.58)
(0.60)
(0.60)
Retrieval-Reetr
61.16
65.92
67.18
69.45
18

---

E.4 Model Training and Inference Time
In this section, we provide time and inference analysis verify the efficiency of Retrieval-Retro as shown in Table We observe that methods using retrievers have slightly longer training times compared to those that do not retrievers. However, since preprocess the materials to retrieve using a pretrained retriever prior to training, the training time for the model utilizing retrievers remains manageable. Additionally, Roost and CrabNet exhibit significantly longer training times compared to other methods, which can be attributed their distinctive modeling interaction mechanisms.
Table 9: Training and inference time per epoch (sec/epoch).
Model
Retriever
Training Time
Inference Time
Composition MLP
0.4129
0.0007
Hc 12
1,6622
0,0103
Reeto
X 0,7528
0.0012
Roost
2,6382
13.6702
CrabNet
9,7238
0,1123
Gruph Network
X
0.6917
0.0019
Gruph Network MPC
2,4361
0.0107
Retrieval- Retro
3,2601
0.0116
F
Broader Impacts
Potential Positive Scientific Impacts. Our work, Retrieval-Retro explores the automation of
precursor prediction, process traditionally dependent chemist's expertise. We have developed model that learns novel synthesis recipes by implicitly extracting precursor information from retrieved materials. This enables Retrieval-Rette to effectively recommend precursor sets for target materials and facilitates its use in real autonomous material synthesis processes [55
Potential Negative Societal Impacts. Although this work demonstrate good predictive capabilities for precursor prediction, it lacks uncertainty estimation. Therefore, it is essential to use this model collaboratively with chemists for effective precursor prediction.
G
Pseudo Code
Algorithm 1:
Pseudocode of Retrieval-Retto.
Input: Composition hased fully connected graph 9 = (X, A), Retrieved reference material graph from MPC retriever G. MPC (Xa MPC, Ar MPC). Retrieved reference material sets from NRE retriever Gr nrF. = (Xr NRF, A NRF), Number of self attention layer s, Number cross attention layer c, The number of Retrieved Material K, Graph Network GNN
g: +
GNN(X, A)
for
1 to K do
gh:PPC GNN Xr. MPC, Ar. MPC)
end
G'P
Self-Attention g
Cross-Attentiongg" 1-1 G c) // Repeat above process line 2 6 for NRE
gf f
retriever
y Octssueer (g: seeeell g
Binary Cross Entropy
19

---

Algorithm 2: Pseudocode of MPC Retriever.
Input:A chemical composition of material A learnale precursor embeddings P. An embedding for the i-th precursor p; of P, Ground truth Precursor y, MLP M, Knowledge BaseKB
m f M(x)
Construct a perturbed P by applying randomly perturbed ý as mask. (p, masked ğ and left unchanged otherwise.) // Masking embeddings
3 Closrroosttttt Proos-AAtention(us P.P)
Gta((s p.) // Calculate the probability of each precursor
Lrraining Binary Cross Entropy(y.
Calculate the cosine similarity between the target material and all materials the KB using M
Retrieve the top K materials and save the retrieved material sets. // Construct retrieved material Sets
Algorithm 3: Pseudocode NRE Retriever.
Input:Commnn
Composition based fully connected graph (material from DFT calculated data)
GDFT (XDFT, ADFT), Composition based fully connected graph (material from experimental data) G = (X Frry), Ground truth Formation energy from DFT calculated data HDFT, Ground truth Formation energy from experimental data H Graph Network GNN, Knowledge BaseKB.
ApPT- + GNN(X ADFT)
DFT,
Preetrran <- MAE(HDFT, HHPP) Pre-train using DFT calculated data Initialize the GNN with weigths pre-trained GNN
HEup + GNN(X Eap. A Exp)
LFine-tune +- MA Eap: Form. Form.EEsp) // Fine-tune using Experimental data
Calculate the formation energy (H) of the target material in the dataset and the precursor set of all materials
in the knowledge base (KB) using a fine-tuned GNN. // Calculate A AH
Calculate AG following AG x AH HTerget H Precursor
// Calculate AG Retrieve K materials with most negative AG from KB per target material, then save the retrieved material // Construct Retrieved Material Sets
20

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Reviews:
[{"review_id": "R3q0DRNrio", "reviewer": "Reviewer_moYU", "summary": "The paper introduces RetroPLEX, a method for inorganic retrosynthesis planning. It extracts precursor information from retrieved reference materials implicitly. The authors use attention layers to extract information from the reference material and design a neural reaction energy (NRE) retriever to provide complementary reference materials. Extensive experiments demonstrate the effectiveness of implicit extraction of precursor information and NRE retriever in discovering novel synthesis recipes.", "questions": "How does RetroPLEX handle situations where there are multiple possible synthesis routes for a target material?\n\nHow do the authors plan to address the potential issue of overfitting, given the complexity of the model?", "limitations": "The authors acknowledge limitations of their work, such as the importance of incorporating precursor information from a broader range of reference materials.", "rating": 7, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "Originality: RetroPLEX presents a new approach to inorganic retrosynthesis planning by implicitly extracting precursor information from retrieved reference materials. This deviates from traditional methods that rely on explicit utilization of precursor information.\n\nQuality: The authors provide a comprehensive evaluation of RetroPLEX, including assessments in realistic scenarios, which demonstrates its effectiveness in discovering novel synthesis recipes.\n\nClarity: The paper is well-written and easy to follow, with clear explanations of the methodology and results.\n\nSignificance: The proposed method has significant implications for material science, as it can aid in the discovery of new materials and their synthesis routes.", "weaknesses": "No specific complaint. The work is highly praised for its comprehensiveness, thoroughness, and originality. It is supported by ample evidence that attests to its quality. The following section will include questions related to the work.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your high praise and acknowledgment of the novelty of our work in inorganic retrosynthesis. We are more than willing to address your two questions in detail.\n\n---\n**[Q1]**\n\nAs the reviewer noted, actual material synthesis processes often involve multiple possible synthesis routes for a target material based on synthesis conditions, making it crucial to address such scenarios. However, considering the increased complexity involved in synthesis conditions, we opted to follow previous work [1] and focus on a single synthetic route for each target material and precursor set. Specifically, each target material is associated with multiple synthesis routes, gathered from various literature sources, each with distinct experimental conditions. Among the multiple synthesis routes, we selected the most frequently occurring synthesis routes among those with varying conditions, allowing the model to learn the most likely precursor set for the input target material.\n\nOn the other hand, recognizing the importance of synthesis conditions, we are planning future work to incorporate these conditions into our modeling. To achieve this, we intend to develop a separate model to predict the sequence of synthesis steps and corresponding conditions from an inorganic synthesis dataset. This approach will enable us to handle unseen materials without synthesis condition information by learning both the target material and synthesis conditions jointly, allowing the model to manage multiple possible synthesis routes.\n\n[1] He, Tanjin, et al. \"Precursor recommendation for inorganic synthesis by machine learning materials similarity from scientific literature.\" Science advances 9.23 (2023): eadg8180.\n\n---\n\n**[Q2]**\n\nRetroPLEX initially trains two retrievers (i.e., the MPC retriever and the NRE retriever) in advance to simplify the complexity of the model by leveraging the materials retrieved from these trained retrievers. However, as the reviewer pointed out, the model may still face potential overfitting issues. To address this, we applied traditional techniques in machine learning such as L2 regularization and early stopping during the training process.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "xpLHDQgVrC", "reviewer": "Reviewer_N6yS", "summary": "The authors address the domain of inorganic retrosynthesis by better leveraging existing inorganic retrosynthesis data. They employ attention learning techniques to establish relationships between chemical formulas and precursor formulas. Additionally, they utilize a neural reaction energy predictor to forecast the Gibbs free energy of chemical reactions, thereby refining the candidate list. The integration of these two models significantly enhances the accuracy of the prediction results. This approach has strong applications in the exploration of material synthesis.", "questions": "1. The Gibbs free energy data used by the authors is used to train a prediction model, and the authors fine-tuned the model to align with experimental values. However, Gibbs free energy depends on specific conditions such as temperature and pressure during experiments or calculations. It's unclear what the specific conditions of reactions were during this transfer process.\n\n2. In section 3.1, the authors mentioned \"The overall training procedure of MPC retriever is in Figure 1 (a).\" It seems this should actually refer to Figure 2.\n\n3. In Table 8, under Qualitative Analysis, the authors predict the precursors for Na3Dy(PO4)2 using MPC to find a similar compound Na3Y(PO3)4. However, this compound cannot be found in online materials databases. Moreover, the claimed corresponding precursor sets do not seem capable of synthesizing Na3Y(PO3)4. Please provide the data source and specific synthesis literature for this compound.\n\n4. The authors use chemical formulas as input to their model. However, in practical synthesis tasks, different conditions can yield different structures with the same chemical formula. The authors should clarify how they addressed the issue of duplicate chemical formulas during the data preprocessing stage.\n\n5. In section 4.3, the authors note that \"reference materials are randomly retrieved without using trained retrievers\" and still achieve a top-1 accuracy of 58%. This high accuracy is surprising and raises questions about the effectiveness of the trained retrievers. The authors should explain why random selection of reference materials performs nearly as well as the trained retrievers and discuss the underlying reasons for this result.", "limitations": "The authors use Gibbs free energy to evaluate the synthesis of reference materials. However, predicting Gibbs free energy can be challenging, especially when only the chemical formula is available without specific structures, synthesis conditions, or information about gas release during synthesis. This limitation suggests an area for improvement in making energy predictions more accurate.\n\nAdditionally, the method of synthesizing inorganic materials—whether through heating and calcination, microwave, or other techniques—can also affect the choice of precursor compounds. Incorporating these synthesis methods and their specific impacts on precursor selection would further enhance the model's accuracy and applicability. We hope the authors can consider these factors in future improvements to their approach.", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Gibbs free energy is essential for determining if a chemical reaction can occur spontaneously and is more likely to happen. Considering this factor is necessary and has been overlooked in previous works. The inclusion of the neural reaction energy predictor significantly improves prediction results by providing a more accurate assessment of Gibbs free energy for chemical reactions.\n\n2. In the task of inorganic retrosynthesis, predicting the class of a precursor compound from its chemical formula is crucial. There are few models for this task starting from chemical formulas. The authors provide a detailed comparison with models like Roost and CrabNet and evaluate variables such as the number of precursors used. The experiments are thorough and well-documented.", "weaknesses": "1. Chemical formula representation issues, detailed in Question 4.\n2. Limited improvement over random selection, detailed in Question 5.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your valuable comments on our work and for recognizing our efforts to address inorganic retrosynthesis using thermodynamic factors! We are more than willing to address each of the specific weaknesses and questions in detail.\n\n---\n\n**[W1&Q4]**\n\nAs the reviewer pointed out, different conditions can yield different structures with the same chemical formula, resulting in duplicate chemical formulas. To consider materials with different structures but the same chemical formula, structural information is essential; however, the dataset we used [1], which was extracted from various publications containing synthesis recipes, lacks this information. This is because many material synthesis experiments are conducted referencing the synthesis recipes of past experiments, and in real-world scenarios, it is common to know only the chemical formula without knowing the structure. In the absence of structural information, we abstracted materials with different structures but the same chemical formula into a single reaction data for our model, so the concern about duplicate chemical formulas, as mentioned by the reviewer, does not exist.\nAdditionally, besides having different structures, materials can also have multiple precursor sets depending on the synthesis conditions. We selected the most frequently occurring synthesis routes among those with varying conditions, allowing the model to learn the most likely precursor set for the input target material.\n\n[1] Kononova, Olga, et al. \"Text-mined dataset of inorganic materials synthesis recipes.\" Scientific data 6.1 (2019): 203.\n\n---\n\n\n**[W2&Q5]**\n\nWe fully agree with the reviewer's comment that there needs to be an explanation and analysis for why the performance of the model with random retrievers has nearly the same high accuracy as the model with trained retrievers as shown in Table 3, and for the effectiveness of trained retrievers. We provide an analysis below.\n\nRecall that RetroPLEX employs Graph Network as the backbone encoder. Table 2 in the attached PDF shows that the Graph Network without a retriever (i.e., Graph Network(no retriever)) and the Graph Network with a random retriever (i.e., Graph Network(random)) perform similarly. This indicates that performance Graph Network(random) is mainly due to the Graph Network backbone itself, but not due to the random retriever. On the other hand, in the case of Graph Network(RetroPLEX), which incorporates our proposed trained retrievers, shows consistent improvement over both Graph Network(no retriever) and Graph Network(random). Such improvements of RetroPLEX are even observed with other backbone encoders as well. This confirms the effectiveness of our framework in implicitly extracting useful synthesis recipe information that aids in predicting the synthesis of the target material by utilizing the trained retrievers.\n\n---\n\n**[Q1]**\n\nFirstly, we apologize for not clearly specifying the temperature and pressure conditions for calculating the Gibbs free energy data. The formation energy from the Materials Project that we used is calculated at 0 K and 0 atm using DFT. The experimental formation energy data is taken from [1], which reports measurements at 298.15 K and 1 atm. \n\nWe fully agree with the reviewer's suggestion that using Gibbs free energy specific to each synthesis condition is essential, as material synthesis occurs under varying temperature and pressure conditions. However, calculating Gibbs free energy for every specific condition is impractical due to the considerable cost and time involved. Given that the appeal of AI in material science lies in its efficiency, we have opted to use an abstraction of the complex conditions.\n\nOn the other hand, even though the Gibbs free energy we used may not be the exact value for each synthesis condition, we believe it still reflects the trends in chemical reactions since both the formation energy data from the Materials Project and the experimental formation energy data are calculated under consistent temperature and pressure conditions. Nevertheless, we acknowledge the limitations of our proposed method and greatly appreciate the reviewer's valuable comment. We will explore efficient ways to calculate Gibbs free energy considering synthesis conditions and incorporate them into our modeling as part of future work.\n\n[1] Jha, Dipendra, et al. \"Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning.\" Nature communications 10.1 (2019): 5316.\n\n---\n\n**[Q2]**\n\nWe apologize for the confusion caused by the typo. As the reviewer correctly pointed out, it should refer to Figure 2(a). We appreciate your attention to detail and will make the necessary corrections promptly.\n\n---\n\n**[Q3]**\n\nFirst and foremost, we sincerely apologize for any confusion caused by our qualitative analysis. As the reviewer correctly pointed out, upon reviewing Table 8, we found that the correct similar compound retrieved for synthesizing Na3Dy(PO4)2 is NaY(PO3)4, not Na3Y(PO3)4. This critical error of writing Na3 instead of Na led to a misunderstanding. We regret this mistake and any resulting confusion. We have inserted the corrected table (Table 1) into the attached PDF.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "JRp5AxJCGE", "reviewer": "Reviewer_wQcK", "summary": "The manuscript presents a approach, RetroPLEX, for inorganic retrosynthesis planning. The authors propose RetroPLEX, a method that implicitly extracts precursor information from reference materials using attention layers. Additionally, they incorporate domain expertise by considering the thermodynamic relationships between target materials and potential precursors. The manuscript presents a approach, RetroPLEX, for inorganic retrosynthesis planning. The authors propose RetroPLEX, a method that implicitly extracts precursor information from reference materials using attention layers. Additionally, they incorporate domain expertise by considering the thermodynamic relationships between target materials and potential precursors.", "questions": "1.  Why were the MPC and NRE retrievers chosen? How do they complement each other?\n\n2. Given the apparent complexity of the proposed model, what are the computational requirements? Can the authors provide a detailed analysis of the time and resource requirements for training and using RetroPLEX? \n\n3. How does the model's performance scale with increasing dataset sizes or computation complexity?\n\n4. Can the authors provide qualitative examples of the importance of each retriever? For example, provide examples where each retriever plays a crucial role in identifying relevant reference materials or extracting precursor information.", "limitations": "The authors have discussed the limitations.", "rating": 5, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The idea of implicitly extracting precursor information from reference materials using retrieval and attention mechanisms is interesting and potentially beneficial for discovering new synthesis recipes.\n2. The incorporation of thermodynamic relationships (∆G) through the NRE retriever is a useful addition, reflecting domain knowledge in inorganic synthesis.\n3. The paper includes a variety of experiments comparing RetroPLEX to existing methods.", "weaknesses": "1. The method is pretty complex, including two retrievers, MPC and NRE. The complex structure, while powerful, could pose challenges in terms of computational efficiency and scalability, especially when deployed in resource-limited settings.\n\n2. The manuscript lacks clarity in several areas. For example, the explanation of the NRE retriever's training process is unclear.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your valuable comments on our work! We are more than willing to address each of the specific weaknesses and questions in a detailed manner.\n\n---\n\n**[W1&Q2]**\n\nIndeed, we have provided the complexity of the model in terms of model training and inference in Appendix E.5. Upon the reviewer’s request, we provide further details in terms of GPU memory and computation resources. Specifically, we used a CPU Intel Xeon Gold 6326 and a single NVIDIA GeForce A6000 (48GB) GPU. The maximum GPU memory required to train the RetroPLEX model is less than 4.5GB, so it is also possible to train the model on GPUs with smaller memory capacities, such as the NVIDIA GeForce Titan (12GB). We also measured the training time by  training the model using only a single CPU, and found that it takes 22.02 seconds per epoch, which is approximately 7 times longer than the 3.26 seconds per epoch when using a single GPU. Despite the longer training time when using a CPU, we validated that it is still possible to train the model in environments without a GPU.\nAdditionally, despite the apparent complexity of RetroPLEX, we predefine the reference materials for each target material before training. Consequently, as demonstrated in Appendix E.5., RetroPLEX exhibits only a marginal increase in training time compared to baseline methods.\n\n---\n\n**[W2]**\n\nWe apologize for the lack of a detailed explanation of the model's training process, which may have made it difficult for the reviewers to understand our method. To clarify our approach, we have included a pseudocode for both the NRE retriever and MPC retriever in the attached PDF. \n\nRegarding the NRE retriever, we developed a composition-based formation energy predictor tailored for experimental data. We initially pre-trained the GNN predictor on extensive DFT-calculated data and then fine-tuned it using experimental formation energy data. Using this fine-tuned GNN predictor, we calculate the formation energy of the target material in the dataset and the precursors of all materials in the knowledge base. Subsequently, we calculate the Gibbs free energy and retrieve the top K materials with the most negative Gibbs free energy for each target material. A more detailed training procedure and further analysis of the NRE retriever are provided in Appendix E.2 (page 16).\n\n\n---\n\n**[Q1]**\n\nThe MPC retriever is crucial for effectively leveraging the synthesis practices from the synthesis literature. It identifies reference materials that share similar synthesis recipes with the target material by learning the dependencies among precursors and the correlation between the precursors and the target material. However, despite its effectiveness in identifying reference materials with potentially similar precursor sets, it often misses the thermodynamic relationships between materials. To achieve this, we incorporate the NRE retriever, which identifies reference materials possessing the precursor sets capable of inducing favorable reactions with the target materials by considering thermodynamic forces. \nUtilizing both retrievers, our models can explore a wide range of reference materials that could provide potential recipes for the target materials. We also provide further explanation with qualitative analysis in Q4.\n\n---\n\n**[Q3]**\n\nUpon reviewer's request, we trained the model with varying dataset sizes: 20%, 40%, 60%, and 80%. As shown in Figure 1 in the attached PDF, as the dataset size increases, we observed a consistent improvement in model performance of Top-10 accuracy. This indicates that the model benefits from having access to more supervision, which likely leads to better generalization and more robust predictions. Moreover, it can be observed that RetroPLEX using only 80% of the training dataset outperforms the graph network using 100% of the training dataset, while our model demonstrates significantly higher performance when both models use 100% of the dataset.\nAdditionally, in terms of computational complexity, we tested the performance of the proposed model by reducing the message passing layers (L) of the backbone encoder, Graph Network from 3 to 2 (i.e., RetroPLEX (D:256, L:2)), and by reducing the hidden dimension (D) from 256 to 128 (i.e., RetroPLEX (D:128, L:3)). We observed a decrease in model performance due to the reduced representative power of the model with fewer message passing layers and smaller hidden dimensions. Nevertheless, even with reduced complexity, RetroPLEX (D:256, L:2) and RetroPLEX (D:128, L:3) outperform the Graph Network with 3 message passing layers and a hidden dimension of 256 (i.e., Graph Network (D:256, L:3)). This can be attributed to the information from retrieved materials, which helps the model achieve good results despite its lower complexity. In summary, these two experiments demonstrate that RetroPLEX can effectively perform precursor extraction through trained retrievers, achieving better performance compared to the Graph Network without retrievers. This is possible even with relatively smaller datasets (Figure 1(a)), smaller hidden dimensions (D), and fewer message passing layers (L) (Figure 1(b)).\n\n---\n\n**[Q4]**\n\nWe provide a qualitative analysis of our proposed method for retrosynthesis planning for the target material Na3Dy(PO4)2 in the Table 1 (attached PDF). When only the MPC retriever is used, the method fails to predict the complete precursor set due to insufficient extraction of precursor information from the retrieved materials. However, when both the NRE and MPC retrievers are used, our method successfully predicts the full precursor set for the target material. For example, the NRE retriever allows our method to extract precursor information from NaPO3, which includes a crucial precursor Na2CO3, a direct precursor for the target material. The complementary nature of the retrievers allows our method to effectively extract precursor information from relevant reference materials without explicitly using precursors.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "op0ZNy4B5z", "reviewer": "Reviewer_URJP", "summary": "This paper lies in the domain of AI for chemistry, and this paper proposes RetroPLEX for inorganic retrosynthesis planning. The proposed approach is comprised of two components: masked precursor completion retriever and neural reaction energy retriever.", "questions": "Can the proposed approach provide some insights into the AI methods for other domains, e.g., robotics and mixed integer linear programming?", "limitations": "The authors have discussed the potential limitations in the Appendix.", "rating": 5, "confidence": 1, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "The writing is mostly clear.\n\nThe experiment results are thoroughly discussed.", "weaknesses": "The model training subsection has not detailedly elaborated on the training process of the proposed approach. The authors are encouraged to provide the pseudocode of the proposed approach.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your valuable comments on our work! We are more than willing to address both the weakness and the question in detail.\n\n---\n\n**[W1]**\n\nWhile we have endeavored to thoroughly explain the training process of RetroPLEX, the limited space of the submission may not have allowed for complete clarity. At the reviewer's request, we have included the model's pseudocode in the attached PDF and will ensure it is incorporated into the Appendix in our final submission. We apologize for any confusion this may have caused and are happy to provide detailed answers if further clarification is needed.\n\n---\n\n**[Q1]**\n\nThe core idea of our proposed approach is to utilize a retriever tailored to the task at hand and to leverage the retrieved information through attention mechanisms. From the perspective of using retrievers, in robotics domains such as motion planning and autonomous navigation, appropriate information matching the situation and task can be effectively utilized through a retriever from a database. When using large language models (LLMs) for robotics control, the intrinsic issues of hallucination and lack of updated information in LLMs can be effectively handled through an online-retrieval approach. Furthermore, when solving a problem, if we can break it down into step-by-step instructions and retrieve appropriate action patterns for each step, we can expect satisfactory task performance compared to not using retrieval.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "gBwwPNLkiE", "reviewer": "Reviewer_X1nP", "summary": "I'm not an expert in this field. I'm familiar with organic retrosynthesis prediction but not familiar with inorganic retrosynthesis planning. \n\nThis paper first trains a retriever to determine which materials to reference. Then this paper trains a model for material selections.", "questions": "N/A", "limitations": "N/A", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. This inorganic retrosynthesis planning is different from organic retrosynthesis planning. The proposed method is novel in my opinion. Inorganic retrosynthesis planning operates step by step while this paper proposes the material within one step.\n\n2. Writing is clear. I can follow this paper.\n\n3. The proposed method achieves good performance.", "weaknesses": "1. Little discussion on the difference between inorganic retrosynthesis planning and organic retrosynthesis planning.\n\n2. Little discussion on computational complexity and the space of the material.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your valuable comments and for acknowledging the novelty of our work in inorganic retrosynthesis! We are more than willing to address any questions in detail.\n\n---\n**[W1]**  \n\nAs the reviewer suggested, describing the differences between organic retrosynthesis and inorganic retrosynthesis would make the manuscript easier to understand. Thank you for the valuable feedback. We plan to add this section to the manuscript in the future. \nBoth organic and inorganic retrosynthesis are challenging tasks that predict the synthesis of materials by breaking down the target material into simpler precursors. However, there are significant differences between organic and inorganic retrosynthesis. \n\nOrganic retrosynthesis deals with organic compounds, which are molecules primarily composed of elements such as carbon, hydrogen, oxygen, nitrogen, and sulfur. These compounds are represented using molecular structure graphs or SMILES strings. In contrast, inorganic retrosynthesis involves inorganic compounds, which can include a wider variety of elements, often including metals, and have structures that periodically repeat in unit cells. \n\nAnother key difference lies in the use of structural information during retrosynthesis planning. Organic compounds utilize structural information such as functional groups and reaction centers, which indicate the properties of the material and its reactivity with other molecules, to predict simpler molecules (precursors) into which the target molecule can be broken down. Inorganic compounds, however, have relatively unexplored generalized synthesis mechanisms compared to organic molecules, and calculating their structures is expensive. Therefore, it is challenging to directly use structural information for retrosynthesis planning. Instead, inorganic retrosynthesis often relies solely on the chemical composition of the materials, distinguishing it from organic retrosynthesis.\n\n---\n\n**[W2]**  \n\nRetroPLEX initially trains two retrievers in advance, the MPC retriever and the NRE retriever, to simplify the complexity of the model by leveraging the materials retrieved from these trained retrievers. The maximum GPU memory required to train the RetroPLEX model is less than 4.5GB, so it is also possible to train the model on GPUs with smaller memory capacities, such as the NVIDIA GeForce Titan (12GB). We also measured the training time by  training the model using only the CPU, and found that it takes 22.02 seconds per epoch, which is approximately 7 times longer than the 3.26 seconds per epoch when using the GPU. Since the model typically converges in around 150 epochs, the training time is manageable. Consequently, even with the notably longer training duration, it is still feasible to train the model in environments without a GPU.\n\nWe use a dataset of 33,343 inorganic solid-state synthesis records extracted from 24,304 material science papers [1]. After preprocessing, we utilize data for 28,434 target materials. Since the synthesis data was extracted from material science publications up to 2020, allowing us to consider relatively recent target materials, and given that this is the largest dataset among available material synthesis datasets, we believe it adequately captures the practices of actual material synthesis literature.\n\n[1] Kononova, Olga, et al. \"Text-mined dataset of inorganic materials synthesis recipes.\" Scientific data 6.1 (2019): 203.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:
Dear reviewers, thank you for your valuable comments on our work. We are more than willing to address each of the weaknesses and questions in detail. Additionally, we have attached a PDF file that includes a qualitative analysis, experiments, and pseudocode for the rebuttal.

Meta-review: This paper introduces RetroPLEX, an inorganic retrosynthesis planning method that implicitly extracts precursor information from retrieved reference materials. The authors utilize attention layers to extract relevant information and design a neural reaction energy predictor to estimate the Gibbs free energy of chemical reactions. Extensive experiments demonstrate the effectiveness of implicit precursor extraction and the NRE predictor in discovering novel synthesis routes.

The reviewers found this work universally interesting and timely, given the growing capabilities in exploring material synthesis. Despite some minor concerns, this is a strong paper that I recommend for acceptance to NeurIPS.
Decision: Accept (poster)