jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: James P. Lewis | Pengju Ren | Xiaodong Wen | Yongwang Li | GuanHua Chen
Source: Frontiers in Quantum Science and Technology (article, 2023-08-31)
Field: Materials Science — Materials Chemistry

Machine learning meets quantum mechanics in catalysis Over the past decade many researchers have applied machine learning algorithms with computational chemistry and materials science tools to explore properties of catalysts. There is a rapid increase in publications demonstrating the use of machine learning for rational catalyst design. In our perspective, targeted tools for rational catalyst design will continue to make significant contributions. However, the community should focus on developing high-throughput simulation tools that utilize molecular dynamics capabilities for thorough exploration of the complex potential energy surfaces that exist, particularly in heterogeneous catalysis. Catalyst-specific databases should be developed to contain enough data to represent the complex multi-dimensional space that defines structure-function relationships. Machine learning tools will continue to impact rational catalyst design; however, we believe that more sophisticated pattern recognition algorithms would yield better understanding of structure-function relationships for heterogeneous catalysis. Rational design ; Function (biology) ; Computer science ; Perspective (graphical) ; Throughput ; Nanotechnology ; Artificial intelligence ; Biochemical engineering ; Data science ; Machine learning ; Management science ; Materials science ; Engineering Machine Learning in Materials Science ; Topic Modeling ; Catalysis and Oxidation Reactions
Authors: Puck van Gerwen | Ksenia R. Briling | Charlotte Bunne | Vignesh Ram Somnath | Rubén Laplaza | Andreas Krause | Clémence Corminbœuf
Source: arXiv (Cornell University) (preprint, 2023-12-13)
Field: Materials Science — Materials Chemistry

3DReact: Geometric Deep Learning for Chemical Reactions Geometric deep learning models, which incorporate the relevant molecular symmetries within the neural network architecture, have considerably improved the accuracy and data efficiency of predictions of molecular properties. Building on this success, we introduce 3DReact, a geometric deep learning model to predict reaction properties from three-dimensional structures of reactants and products. We demonstrate that the invariant version of the model is sufficient for existing reaction datasets. We illustrate its competitive performance on the prediction of activation barriers on the GDB7-22-TS, Cyclo-23-TS and Proparg-21-TS datasets in different atom-mapping regimes. We show that, compared to existing models for reaction property prediction, 3DReact offers a flexible framework that exploits atom-mapping information, if available, as well as geometries of reactants and products (in an invariant or equivariant fashion). Accordingly, it performs systematically well across different datasets, atom-mapping regimes, as well as both interpolation and extrapolation tasks. Extrapolation ; Equivariant map ; Sensitivity (control systems) ; Artificial neural network ; Property (philosophy) ; Computer science ; Biological system ; Atom (system on chip) ; Algorithm ; Chemistry ; Artificial intelligence ; Computational chemistry ; Mathematics ; Engineering ; Pure mathematics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Mantas Vaškevičius | Jurgita Kapočiūtė-Dzikienė | Arnas Vaškevičius | Liudas Šlepikas
Source: PeerJ Computer Science (article, 2023-08-18)
Field: Materials Science — Materials Chemistry

Deep learning-based automatic action extraction from structured chemical synthesis procedures This article proposes a methodology that uses machine learning algorithms to extract actions from structured chemical synthesis procedures, thereby bridging the gap between chemistry and natural language processing. The proposed pipeline combines ML algorithms and scripts to extract relevant data from USPTO and EPO patents, which helps transform experimental procedures into structured actions. This pipeline includes two primary tasks: classifying patent paragraphs to select chemical procedures and converting chemical procedure sentences into a structured, simplified format. We employ artificial neural networks such as long short-term memory, bidirectional LSTMs, transformers, and fine-tuned T5. Our results show that the bidirectional LSTM classifier achieved the highest accuracy of 0.939 in the first task, while the Transformer model attained the highest BLEU score of 0.951 in the second task. The developed pipeline enables the creation of a dataset of chemical reactions and their procedures in a structured format, facilitating the application of AI-based approaches to streamline synthetic pathways, predict reaction outcomes, and optimize experimental conditions. Furthermore, the developed pipeline allows for creating a structured dataset of chemical reactions and procedures, making it easier for researchers to access and utilize the valuable information in synthesis procedures. Computer science ; Scripting language ; Artificial intelligence ; Transformer ; Pipeline (software) ; Machine learning ; Classifier (UML) ; Natural language processing ; Bridging (networking) ; Programming language ; Voltage Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Muratahan Aykol | Amil Merchant | Simon Batzner | Jennifer N. Wei | Ekin D. Cubuk
Source: arXiv (Cornell University) (preprint, 2023-10-02)
Field: Materials Science — Materials Chemistry

Predicting emergence of crystals from amorphous matter with deep learning Crystallization of the amorphous phases into metastable crystals plays a fundamental role in the formation of new matter, from geological to biological processes in nature to synthesis and development of new materials in the laboratory. Predicting the outcome of such phase transitions reliably would enable new research directions in these areas, but has remained beyond reach with molecular modeling or ab-initio methods. Here, we show that crystallization products of amorphous phases can be predicted in any inorganic chemistry by sampling the crystallization pathways of their local structural motifs at the atomistic level using universal deep learning potentials. We show that this approach identifies the crystal structures of polymorphs that initially nucleate from amorphous precursors with high accuracy across a diverse set of material systems, including polymorphic oxides, nitrides, carbides, fluorides, chlorides, chalcogenides, and metal alloys. Our results demonstrate that Ostwald's rule of stages can be exploited mechanistically at the molecular level to predictably access new metastable crystals from the amorphous phase in material synthesis. Crystallization ; Metastability ; Amorphous solid ; Nucleation ; Chemical physics ; Polyamorphism ; Materials science ; Crystal (programming language) ; Amorphous metal ; Phase (matter) ; Ab initio ; Nanotechnology ; Crystallography ; Chemistry ; Computer science ; Organic chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions
Authors: Junwu Chen | Philippe Schwaller
Source: arXiv (Cornell University) (preprint, 2023-12-20)
Field: Materials Science — Materials Chemistry

Molecular Hypergraph Neural Networks Graph neural networks (GNNs) have demonstrated promising performance across various chemistry-related tasks. However, conventional graphs only model the pairwise connectivity in molecules, failing to adequately represent higher-order connections like multi-center bonds and conjugated structures. To tackle this challenge, we introduce molecular hypergraphs and propose Molecular Hypergraph Neural Networks (MHNN) to predict the optoelectronic properties of organic semiconductors, where hyperedges represent conjugated structures. A general algorithm is designed for irregular high-order connections, which can efficiently operate on molecular hypergraphs with hyperedges of various orders. The results show that MHNN outperforms all baseline models on most tasks of OPV, OCELOTv1 and PCQM4Mv2 datasets. Notably, MHNN achieves this without any 3D geometric information, surpassing the baseline model that utilizes atom positions. Moreover, MHNN achieves better performance than pretrained GNNs under limited training data, underscoring its excellent data efficiency. This work provides a new strategy for more general molecular representations and property prediction tasks related to high-order connections. Hypergraph ; Pairwise comparison ; Computer science ; Artificial neural network ; Baseline (sea) ; Conjugated system ; Theoretical computer science ; Graph ; Property (philosophy) ; Artificial intelligence ; Chemistry ; Mathematics ; Combinatorics ; Polymer Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Conducting polymers and applications
Authors: Hirofumi Tsuruta | Yukari Katsura | Masaya Kumagai
Source: arXiv (Cornell University) (preprint, 2023-09-07)
Field: Materials Science — Materials Chemistry

DeepCrysTet: A Deep Learning Approach Using Tetrahedral Mesh for Predicting Properties of Crystalline Materials Machine learning (ML) is becoming increasingly popular for predicting material properties to accelerate materials discovery. Because material properties are strongly affected by its crystal structure, a key issue is converting the crystal structure into the features for input to the ML model. Currently, the most common method is to convert the crystal structure into a graph and predicting its properties using a graph neural network (GNN). Some GNN models, such as crystal graph convolutional neural network (CGCNN) and atomistic line graph neural network (ALIGNN), have achieved highly accurate predictions of material properties. Despite these successes, using a graph to represent a crystal structure has the notable limitation of losing the crystal structure's three-dimensional (3D) information. In this work, we propose DeepCrysTet, a novel deep learning approach for predicting material properties, which uses crystal structures represented as a 3D tetrahedral mesh generated by Delaunay tetrahedralization. DeepCrysTet provides a useful framework that includes a 3D mesh generation method, mesh-based feature design, and neural network design. The experimental results using the Materials Project dataset show that DeepCrysTet significantly outperforms existing GNN models in classifying crystal structures and achieves state-of-the-art performance in predicting elastic properties. Delaunay triangulation ; Convolutional neural network ; Tetrahedron ; Computer science ; Artificial neural network ; Graph ; Artificial intelligence ; Crystal structure ; Crystal (programming language) ; Deep learning ; Algorithm ; Machine learning ; Materials science ; Theoretical computer science ; Mathematics ; Crystallography ; Geometry ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Concrete and Cement Materials Research
Authors: Astrid Klipfel | Olivier Peltre | Najwa Harrati | Yaël Fregier | Adlane Sayede | Zied Bouraoui
Source: arXiv (Cornell University) (preprint, 2023-02-01)
Field: Materials Science — Materials Chemistry

Equivariant Message Passing Neural Network for Crystal Material Discovery Automatic material discovery with desired properties is a fundamental challenge for material sciences. Considerable attention has recently been devoted to generating stable crystal structures. While existing work has shown impressive success on supervised tasks such as property prediction, the progress on unsupervised tasks such as material generation is still hampered by the limited extent to which the equivalent geometric representations of the same crystal are considered. To address this challenge, we propose EMPNN a periodic equivariant message-passing neural network that learns crystal lattice deformation in an unsupervised fashion. Our model equivalently acts on lattice according to the deformation action that must be performed, making it suitable for crystal generation, relaxation and optimisation. We present experimental evaluations that demonstrate the effectiveness of our approach. Equivariant map ; Computer science ; Artificial neural network ; Message passing ; Lattice (music) ; Crystal (programming language) ; Property (philosophy) ; Action (physics) ; Artificial intelligence ; Theoretical computer science ; Mathematics ; Physics ; Distributed computing ; Pure mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Neural Networks and Applications
Authors: Astrid Klipfel | Zied Bouraoui | Olivier Peltre | Yaël Fregier | Najwa Harrati | Adlane Sayede
Source: Proceedings of the AAAI Conference on Artificial Intelligence (article, 2023-06-26)
Field: Materials Science — Materials Chemistry

Equivariant Message Passing Neural Network for Crystal Material Discovery Automatic material discovery with desired properties is a fundamental challenge for material sciences. Considerable attention has recently been devoted to generating stable crystal structures. While existing work has shown impressive success on supervised tasks such as property prediction, the progress on unsupervised tasks such as material generation is still hampered by the limited extent to which the equivalent geometric representations of the same crystal are considered. To address this challenge, we propose EPGNN a periodic equivariant message-passing neural network that learns crystal lattice deformation in an unsupervised fashion. Our model equivalently acts on lattice according to the deformation action that must be performed, making it suitable for crystal generation, relaxation and optimisation. We present experimental evaluations that demonstrate the effectiveness of our approach. Equivariant map ; Computer science ; Artificial neural network ; Message passing ; Crystal (programming language) ; Lattice (music) ; Property (philosophy) ; Artificial intelligence ; Action (physics) ; Theoretical computer science ; Mathematics ; Physics ; Distributed computing Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Neural Networks and Applications
Authors: Yixuan Che | Dan Wang | Haifeng Lv | Xiaojun Wu
Source: Materials Today Chemistry (article, 2023-08-08)
Field: Materials Science — Materials Chemistry

Crystal system and space group prediction of two-dimensional materials from chemical formula via deep neural networks Stacking ; Group (periodic table) ; Artificial neural network ; Crystal (programming language) ; Space (punctuation) ; Generality ; Chemical space ; Artificial intelligence ; Biological system ; Crystal structure ; Materials science ; Mathematics ; Computer science ; Algorithm ; Pattern recognition (psychology) ; Crystallography ; Chemistry ; Organic chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; 2D Materials and Applications
Authors: Anton Ashuiev | A. Nobile | D. Trummer | Daniel Klose | С. А. Гуда | Оlga V. Safonova | Christophe Copéret | Alexander A. Guda | Gunnar Jeschke
Source: Angewandte Chemie (article, 2023-11-16)
Field: Materials Science — Materials Chemistry

Active Sites in Cr(III)‐Based Ethylene Polymerization Catalysts from Machine‐Learning‐Supported XAS and EPR Spectroscopy** Abstract The ethylene polymerization Phillips catalyst has been employed for decades and is central to the polymer industry. While Cr(III) alkyl species are proposed to be the propagating sites, there is so far no direct experimental evidence for such proposal. In this work, by coupling Surface organometallic chemistry, EPR spectroscopy, and machine learning‐supported XAS studies, we have studied the electronic structure of well‐defined silica‐supported Cr(III) alkyls and identified the presence of several surface species in high and low‐spin states, associated with different coordination environments. Notably, low‐spin Cr(III) sites are shown to participate in ethylene polymerization, indicating that similar Cr(III) alkyl species could be involved in the related Phillips catalyst. X-ray absorption spectroscopy ; Polymerization ; Electron paramagnetic resonance ; Ethylene ; Alkyl ; Chemistry ; Catalysis ; Polymer chemistry ; Organometallic chemistry ; Polymer ; Spectroscopy ; Photochemistry ; Absorption spectroscopy ; Physical chemistry ; Organic chemistry ; Nuclear magnetic resonance Machine Learning in Materials Science ; Organometallic Complex Synthesis and Catalysis ; Catalysis and Oxidation Reactions
Authors: Motoji Sakai | Mitsunori Kaneshige | Koji Yasuda
Source: Journal of Computational Chemistry (article, 2023-10-25)
Field: Materials Science — Materials Chemistry

Learning organo‐transition metal catalyzed reactions by graph neural networks Chemical reaction outcome prediction presents a fundamental challenge in synthetic chemistry. Most existing machine learning (ML) approaches focus on chemical reactions of typical elements. We developed a simple ML model focused on organo-transition metal-catalyzed reactions (OMCRs). Instead of overall reactions observed in experiments, we let the ML model learn the sequence of simplified elementary reactions. This drastically reduced the complexity of the model and helped it find common patterns from distinct reactions. We let a graph neural network learn the reactivity index of a pair of atoms. The model was able to learn a wide variety of OMCRs, and the accuracy of reaction prediction reached 97%, even though the model has extremely fewer learnable parameters than other standards. The learned reactivity indices of bonds nicely summarize the knowledge of reactions in the dataset. Reactivity (psychology) ; Artificial neural network ; Catalysis ; Graph ; Chemical reaction ; Computer science ; Chemistry ; Variety (cybernetics) ; Molecular graph ; Computational chemistry ; Machine learning ; Artificial intelligence ; Theoretical computer science ; Organic chemistry Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Computational Drug Discovery Methods
Authors: Congsheng Xu | Yi Lu | Xiaomei Deng | Peiyuan Yu
Source: Chinese Journal of Chemistry (article, 2023-08-03)
Field: Materials Science — Materials Chemistry

Prediction of Molecular Conformation Using Deep Generative Neural Networks Abstract The accurate prediction of molecular conformations with high efficiency is crucial in various fields such as materials science, computational chemistry and computer‐aided drug design, as the three‐dimensional structures accessible at a given condition usually determine the specific physical, chemical, and biological properties of a molecule. Traditional approaches for the conformational sampling of molecules such as molecular dynamics simulations and Markov chain Monte Carlo methods either require an exponentially increasing amount of time as the degree of freedom of the molecule increases or suffer from systematic errors that fail to obtain important conformations, thus presenting significant challenges for conformation sampling with both high efficiency and high accuracy. Recently, deep learning‐based generative models have emerged as a promising solution to this problem. These models can generate a large number of molecular conformations in a short time, and their accuracy is comparable and, in some cases, even better than that of current popular open‐source and commercial software. This Emerging Topic introduces the recent progresses of using deep learning for predicting molecular conformations and briefly discusses the potential and challenges of this emerging field. Chemistry ; Molecular dynamics ; Field (mathematics) ; Artificial intelligence ; Sampling (signal processing) ; Force field (fiction) ; Markov chain ; Markov chain Monte Carlo ; Deep learning ; Computer science ; Statistical physics ; Machine learning ; Computational chemistry ; Bayesian probability ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Katsuaki Tanabe
Source: Journal of Nuclear Engineering (article, 2023-04-14)
Field: Materials Science — Materials Chemistry

Machine-Learning-Based Composition Analysis of the Stability of V–Cr–Ti Alloys Machine learning methods allow the prediction of material properties, potentially using only the elemental composition of a molecule or compound, without the knowledge of molecular or crystalline structures. Herein, a composition-based machine learning prediction of the material properties of V–Cr–Ti alloys is demonstrated. Our machine-learning-based prediction of the stability of the V–Cr–Ti alloys is qualitatively consistent with the composition-dependent experimental data of the ductile–brittle transition temperature and swelling. Furthermore, our computational results suggest the existence of a composition region, Cr+Ti ~ 60 wt.%, at a significantly low ductile–brittle transition temperature. This outcome contrasts with a reportedly low Cr+Ti content of less than 10 wt.% in conventional V–Cr–Ti alloys. Machine-learning-based numerical stability prediction is useful for the design and analysis of metal alloys, particularly for multicomponent alloys such as high-entropy alloys, to develop materials for nuclear fusion reactors. High entropy alloys ; Materials science ; Brittleness ; Composition (language) ; Stability (learning theory) ; Fusion ; Titanium alloy ; Metallurgy ; Thermodynamics ; Machine learning ; Alloy ; Computer science ; Physics Nuclear Materials and Properties ; Machine Learning in Materials Science ; Advanced Materials Characterization Techniques
Authors: Richard A. Messerly | Shuhao Zhang | Małgorzata Z. Makoś | Ryan B. Jadrich | Elfi Kraka | Kipton Barros | Benjamin Nebgen | Sergei Tretiak | Olexandr Isayev | Nicholas Lubbers | Justin S. Smith
Source: Research Square (preprint, 2023-06-28)
Field: Materials Science — Materials Chemistry

Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential Abstract Reactive chemistry atomistic simulation has a broad range of applications from drug design to energy to materials discovery. Machine learning interatomic potentials (MLIPs) have become an efficient alternative to computationally expensive quantum chemistry simulations. In practice, developing reactive MLIPs requires prior knowledge of reaction networks to generate fitting data and refitting to extensive datasets for each new application. For this reason, many fields of chemistry would greatly benefit from a general reactive MLIP, i.e., an MLIP that is applicable to a broad range of reactive chemistry such that it can be applied to new systems without the need for retraining. In this work, we develop a general reactive MLIP through unbiased active learning with an atomic configuration sampler inspired by nanoreactor molecular dynamics. The resulting potential (ANI-1xnr) is then applied to study five distinct condensed-phase reactive chemistry systems: carbon solid-phase nucleation, graphene ring formation from acetylene, biofuel additives, combustion of methane and the spontaneous formation of glycine from early-earth small molecules. In all studies, ANI-1xnr closely matches experiment and/or previous studies using traditional model chemistry methods. As such, ANI-1xnr proves to be a highly general reactive MLIP for C, H, N, and O elements that does not need to be refit for each application, enabling high-throughput in silico reactive chemistry experimentation. Reactive intermediate ; Chemistry ; Nanotechnology ; Materials science ; Catalysis ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics