jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Andrei Buin | Hung Yi Chiang | S. Andrew Gadsden | Faraz Alderson
Source: arXiv (Cornell University) (preprint, 2023-10-26)
Field: Materials Science — Materials Chemistry

De-novo Chemical Reaction Generation by Means of Temporal Convolutional Neural Networks We present here a combination of two networks, Recurrent Neural Networks (RNN) and Temporarily Convolutional Neural Networks (TCN) in de novo reaction generation using the novel Reaction Smiles-like representation of reactions (CGRSmiles) with atom mapping directly incorporated. Recurrent Neural Networks are known for their autoregressive properties and are frequently used in language modelling with direct application to SMILES generation. The relatively novel TCNs possess similar properties with wide receptive field while obeying the causality required for natural language processing (NLP). The combination of both latent representations expressed through TCN and RNN results in an overall better performance compared to RNN alone. Additionally, it is shown that different fine-tuning protocols have a profound impact on generative scope of the model when applied on a dataset of interest via transfer learning. Recurrent neural network ; Computer science ; Scope (computer science) ; Autoregressive model ; Generative grammar ; Transfer of learning ; Artificial intelligence ; Convolutional neural network ; Deep learning ; Representation (politics) ; Causality (physics) ; Artificial neural network ; Generative model ; Mathematics Machine Learning in Materials Science ; Topic Modeling
Authors: Wangjian Cheng | Dongjie Liao | Chenhe Feng | Fubing Gao | Minjie Li | Xiawei Zhang | Lingyu Deng | Chuanhao Xu | Baoyun Ye | Chongwei An
Source: Research Square (preprint, 2023-05-30)
Field: Materials Science — Polymers and Plastics

Development of robust and rapid self-healing polymers for the repair of microcracks in energetic composite materials Abstract Adhesives with superior toughness and self-healing properties are critical for the practical application of energetic composite materials (ECMs). However, most self-healing polymers exhibit low creep resistance and toughness, which makes simultaneous optimization a challenge. To overcome this, we introduced asymmetric alicyclic and bent biphenyl ring structures into the hard domain units of soft polymers, which resulted in adhesives with robust mechanical properties. The synergistic effect of dynamic disulfide and hydrogen bonding in the adhesive allows for excellent self-healing efficiency. We then conducted a comprehensive investigation into the structure, thermal stability, self-healing, mechanical properties, rheology, and adhesion properties of the synthesized disulfide bond-containing self-healing polyurethanes (PUDS). Our study demonstrates that PUDS films can achieve a toughness of 20.93 MJ m − 3 , and when cut in half and reassembled, they recover to more than 90% of their original toughness within 20 minutes, showcasing impressive mechanical properties and self-healing efficiency. Our experimental measurements and molecular dynamics simulations reveal that the interfacial interaction with 1,3,5-trinitro-2,4,6-triaminobenzene (TATB) is stronger when the hard segment content of the adhesive is higher. Furthermore, even after accidental mechanical damage, cracked ECMs can be effectively healed within 24 hours at 60℃. Self-healing and highly resilient adhesives offer promising avenues for enhancing the safety and longevity of energy-containing composites, with potential military and civil applications. Materials science ; Self-healing ; Toughness ; Adhesive ; Composite material ; Self-healing material ; Composite number ; Polymer ; Layer (electronics) Polymer composites and self-healing ; Flame retardant materials and properties ; Polydiacetylene-based materials and applications
Authors: Michael J. Statt | Brian A. Rohr | Dan Guevarra | Ja'Nya Breeden | Santosh K. Suram | John M. Gregoire
Source: ChemRxiv (preprint, 2023-04-19)
Field: Materials Science — Materials Chemistry

The Materials Experiment Knowledge Graph Materials knowledge is inherently hierarchical. While high-level descriptors such as composition and structure are valuable for contextualizing materials data, the data must ultimately be considered in the context of its low-level acquisition details. Graph databases offer an opportunity to represent hierarchical relationships among data, organizing semantic relationships into a knowledge graph. Herein, we establish a knowledge graph of materials experiments whose construction encodes the complete provenance of each material sample and its associated experimental data and metadata. Additional relationships among materials and experiments further encode knowledge and facilitate data exploration. We illustrate the Materials Experiment Knowledge Graph (MEKG) using several use cases, demonstrating the value of modern graph databases for the enterprise of data-driven materials science. Computer science ; Metadata ; Knowledge graph ; Graph ; Graph database ; ENCODE ; Data science ; Information retrieval ; Data mining ; Theoretical computer science ; World Wide Web Machine Learning in Materials Science ; Graph Theory and Algorithms ; Advanced Graph Neural Networks
Authors: Kyle Noordhoek | Christopher J. Bartel
Source: arXiv (Cornell University) (preprint, 2023-12-18)
Field: Materials Science — Materials Chemistry

Accelerating the prediction of inorganic surfaces with machine learning interatomic potentials The surface properties of solid-state materials often dictate their functionality, especially for applications where nanoscale effects become important. The relevant surface(s) and their properties are determined, in large part, by the materials synthesis or operating conditions. These conditions dictate thermodynamic driving forces and kinetic rates responsible for yielding the observed surface structure and morphology. Computational surface science methods have long been applied to connect thermochemical conditions to surface phase stability, particularly in the heterogeneous catalysis and thin film growth communities. This review provides a brief introduction to first-principles approaches to compute surface phase diagrams before introducing emerging data-driven approaches. The remainder of the review focuses on the application of machine learning, predominantly in the form of learned interatomic potentials, to study complex surfaces. As machine learning algorithms and large datasets on which to train them become more commonplace in materials science, computational methods are poised to become even more predictive and powerful for modeling the complexities of inorganic surfaces at the nanoscale. Surface (topology) ; Interatomic potential ; Nanoscopic scale ; Stability (learning theory) ; Computer science ; Nanotechnology ; Phase (matter) ; Machine learning ; Materials science ; Artificial intelligence ; Chemistry ; Molecular dynamics ; Computational chemistry ; Mathematics ; Geometry Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Catalytic Processes in Materials Science
Authors: Lukas Morand | Tarek Iraki | Johannes Dornheim | Stefan Sandfeld | Norbert Link | Dirk Helm
Source: arXiv (Cornell University) (preprint, 2023-12-22)
Field: Materials Science — Materials Chemistry

Machine learning for structure-guided materials and process design In recent years, there has been a growing interest in accelerated materials innovation in the context of the process-structure-property chain. In this regard, it is essential to take into account manufacturing processes and tailor materials design approaches to support downstream process design approaches. As a major step into this direction, we present a holistic and generic optimization approach that covers the entire process-structure-property chain in materials engineering. Our approach specifically employs machine learning to address two critical identification problems: a materials design problem, which involves identifying near-optimal material microstructures that exhibit desired properties, and a process design problem that is to find an optimal processing path to manufacture these microstructures. Both identification problems are typically ill-posed, which presents a significant challenge for solution approaches. However, the non-unique nature of these problems offers an important advantage for processing: By having several target microstructures that perform similarly well, processes can be efficiently guided towards manufacturing the best reachable microstructure. The functionality of the approach is demonstrated at manufacturing crystallographic textures with desired properties in a simulated metal forming process. Process (computing) ; Computer science ; Reinforcement learning ; Identification (biology) ; Industrial engineering ; Property (philosophy) ; Task (project management) ; Artificial intelligence ; Systems engineering ; Engineering Machine Learning in Materials Science ; Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Yiting Jing | Sufang Chen | Zejun Xu | Tingcheng Li | Daohong Zhang
Source: eXPRESS Polymer Letters (article, 2023-01-01)
Field: Materials Science — Polymers and Plastics

Low-temperature supramolecular adhesives based on hyperbranched polyester Adhesives have been widely applied in various fields.However, most industrial adhesives contain organic solvents, which are toxic and difficult to clean after use.Importantly, most adhesives are used in a relatively small temperature range (0-50 C), which is due to the fragmentation of the morphology and properties of supramolecular polymer gels at low temperatures.Most of the supramolecular adhesives need to be heated during the bonding process, which cannot meet the requirements of low-temperature adhesion in daily life and industry.In this study, based on the concept of using deep eutectic solvents (DESs) as a platform, cyclodextrin (-CD) and carboxyl terminated hyperbranched polyester (DCHP) were used to prepare solvent-free supramolecular polymer gel (CD-DCHP), which was non-toxic and easy to clean.Due to the joint action of deep eutectic solvent and supramolecular polymer structure, CD-DCHP showed excellent mechanical properties, super strong adhesion (up to 4.55 MPa on glass surface), and excellent low-temperature performance (up to 1.64 MPa at -60 C).This supramolecular polymer gel also showed excellent tolerance to acids, alkalis, and various organic solvents.It greatly expanded the application of DES and provided a new approach to the development of supramolecular adhesives. Materials science ; Polyester ; Adhesive ; Polymer science ; Supramolecular chemistry ; Composite material ; Polymer chemistry ; Organic chemistry ; Molecule Dendrimers and Hyperbranched Polymers ; Synthesis and properties of polymers ; Advanced Polymer Synthesis and Characterization
Authors: Yan Zhang | Hao Hao | Xiao He | Shuanhu Gao | Aimin Zhou
Source: arXiv (Cornell University) (preprint, 2023-10-08)
Field: Chemistry — Organic Chemistry

Evolutionary Retrosynthetic Route Planning Molecular retrosynthesis is a significant and complex problem in the field of chemistry, however, traditional manual synthesis methods not only need well-trained experts but also are time-consuming. With the development of big data and machine learning, artificial intelligence (AI) based retrosynthesis is attracting more attention and has become a valuable tool for molecular retrosynthesis. At present, Monte Carlo tree search is a mainstream search framework employed to address this problem. Nevertheless, its search efficiency is compromised by its large search space. Therefore, this paper proposes a novel approach for retrosynthetic route planning based on evolutionary optimization, marking the first use of Evolutionary Algorithm (EA) in the field of multi-step retrosynthesis. The proposed method involves modeling the retrosynthetic problem into an optimization problem, defining the search space and operators. Additionally, to improve the search efficiency, a parallel strategy is implemented. The new approach is applied to four case products and compared with Monte Carlo tree search. The experimental results show that, in comparison to the Monte Carlo tree search algorithm, EA significantly reduces the number of calling single-step model by an average of 53.9%. The time required to search three solutions decreases by an average of 83.9%, and the number of feasible search routes increases by 1.38 times. The source code is available at https://github.com/ilog-ecnu/EvoRRP. Retrosynthetic analysis ; Monte Carlo tree search ; Monte Carlo method ; Search algorithm ; Tree (set theory) ; Computer science ; Combinatorial search ; Artificial intelligence ; Beam search ; Machine learning ; Algorithm ; Mathematics ; Statistics Chemical Synthesis and Reactions ; Chemistry and Chemical Engineering ; Computational Drug Discovery Methods
Authors: Sergei V. Kalinin | Yongtao Liu | Arpan Biswas | Gerd Duscher | Utkarsh Pratiush | Kevin M. Roccapriore | Maxim Ziatdinov | Rama K. Vasudevan
Source: arXiv (Cornell University) (preprint, 2023-10-08)
Field: Materials Science — Materials Chemistry

Human-in-the-loop: The future of Machine Learning in Automated Electron Microscopy Machine learning methods are progressively gaining acceptance in the electron microscopy community for de-noising, semantic segmentation, and dimensionality reduction of data post-acquisition. The introduction of the APIs by major instrument manufacturers now allows the deployment of ML workflows in microscopes, not only for data analytics but also for real-time decision-making and feedback for microscope operation. However, the number of use cases for real-time ML remains remarkably small. Here, we discuss some considerations in designing ML-based active experiments and pose that the likely strategy for the next several years will be human-in-the-loop automated experiments (hAE). In this paradigm, the ML learning agent directly controls beam position and image and spectroscopy acquisition functions, and human operator monitors experiment progression in real- and feature space of the system and tunes the policies of the ML agent to steer the experiment towards specific objectives. Workflow ; Software deployment ; Computer science ; Artificial intelligence ; Human-in-the-loop ; Machine learning ; Segmentation ; Computer vision ; Dimensionality reduction ; Software engineering Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Scientific Computing and Data Management
Authors: Emmanuel Noutahi | Cristian Gabellini | Michael Craig | Jonathan S. C. Lim | Prudencio Tossou
Source: arXiv (Cornell University) (preprint, 2023-10-16)
Field: Materials Science — Materials Chemistry

Gotta be SAFE: A New Framework for Molecular Design Traditional molecular string representations, such as SMILES, often pose challenges for AI-driven molecular design due to their non-sequential depiction of molecular substructures. To address this issue, we introduce Sequential Attachment-based Fragment Embedding (SAFE), a novel line notation for chemical structures. SAFE reimagines SMILES strings as an unordered sequence of interconnected fragment blocks while maintaining compatibility with existing SMILES parsers. It streamlines complex generative tasks, including scaffold decoration, fragment linking, polymer generation, and scaffold hopping, while facilitating autoregressive generation for fragment-constrained design, thereby eliminating the need for intricate decoding or graph-based models. We demonstrate the effectiveness of SAFE by training an 87-million-parameter GPT2-like model on a dataset containing 1.1 billion SAFE representations. Through targeted experimentation, we show that our SAFE-GPT model exhibits versatile and robust optimization performance. SAFE opens up new avenues for the rapid exploration of chemical space under various constraints, promising breakthroughs in AI-driven molecular design. Computer science ; Fragment (logic) ; Notation ; Embedding ; Chemical space ; Artificial intelligence ; Theoretical computer science ; Programming language ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Rebecca M. Neeser | Bruno E. Correia | Philippe Schwaller
Source: arXiv (Cornell University) (preprint, 2023-12-20)
Field: Materials Science — Materials Chemistry

FSscore: A Machine Learning-based Synthetic Feasibility Score Leveraging Human Expertise Determining whether a molecule can be synthesized is crucial in chemistry and drug discovery, as it guides experimental prioritization and molecule ranking in de novo design tasks. Existing scoring approaches to assess synthetic feasibility struggle to extrapolate to new chemical spaces or fail to discriminate based on subtle differences such as chirality. This work addresses these limitations by introducing the Focused Synthesizability score~(FSscore), which uses machine learning to rank structures based on their relative ease of synthesis. First, a baseline trained on an extensive set of reactant-product pairs is established, which is then refined with expert human feedback tailored to specific chemical spaces. This targeted fine-tuning improves performance on these chemical scopes, enabling more accurate differentiation between molecules that are hard and easy to synthesize. The FSscore showcases how a human-in-the-loop framework can be utilized to optimize the assessment of synthetic feasibility for various chemical applications. Chemical space ; Generalizability theory ; Computer science ; Machine learning ; Artificial intelligence ; Prioritization ; Training set ; Ranking (information retrieval) ; Set (abstract data type) ; Generative grammar ; Graph ; Drug discovery ; Theoretical computer science ; Management science ; Bioinformatics ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Paula Carracedo-Reboredo | Eider Aranzamendi | Shan He | Sonia Arrasate | Cristian R. Munteanu | Carlos Fernández-Lozano | Nuria Sotomayor | Esther Lete | Humberto González‐Díaz
Source: Research Square (preprint, 2023-03-03)
Field: Chemistry — Inorganic Chemistry

MATEO: InterMolecular α-Amidoalkylation Theoretical Enantioselectivity Optimization. Online Tool for Selection and Design of Chiral Catalysts and Products Abstract The enantioselective Brønsted acid-catalyzed α-amidoalkylation reaction is a useful procedure is for the production of new drugs and natural products. In this context, Chiral Phosphoric Acid (CPA) catalysts are versatile catalysts for this type of reactions. The selection and design of new CPA catalysts for different enantioselective reactions has a dual interest because new CPA catalysts (tools) and chiral drugs or materials (products) can be obtained. However, this process is difficult and time consuming if approached from an experimental trial and error perspective. In this work, an Heuristic Perturbation-Theory and Machine Learning (HPTML) algorithm was used to seek a predictive model for CPA catalysts performance in terms of enantioselectivity in α-amidoalkylation reactions with R 2 = 0.91 in training and validation series. It involved a Monte Carlo sampling of &gt; 100,000 pairs of query and reference reactions. In addition, the computational and experimental investigation of a new set of intermolecular α-amidoalkylation reactions using BINOL-derived N -triflylphosphoramides as CPA catalysts is reported as a case of study. The model was implemented in a web server called MATEO: InterMolecular Amidoalkylation Theoretical Enantioselectivity Optimization, available online at: https://cptmltool.rnasa-imedir.com/CPTMLTools-Web/mateo. This new user-friendly online computational tool would enable sustainable optimization of reaction conditions that could lead to the design of new CPA catalysts along with new organic synthesis products. Enantioselective synthesis ; Catalysis ; Intermolecular force ; Chemistry ; Computer science ; Combinatorial chemistry ; Context (archaeology) ; Computational chemistry ; Organic chemistry ; Molecule Asymmetric Hydrogenation and Catalysis ; Chemical Synthesis and Analysis ; Machine Learning in Materials Science
Authors: Marko Petković | Pablo Romero-Marimon | Vlado Menkovski | Sofı́a Calero
Source: arXiv (Cornell University) (preprint, 2023-04-04)
Field: Chemistry — Inorganic Chemistry

Equivariant Parameter Sharing for Porous Crystalline Materials Efficiently predicting properties of porous crystalline materials has great potential to accelerate the high throughput screening process for developing new materials, as simulations carried out using first principles model are often computationally expensive. To effectively make use of Deep Learning methods to model these materials, we need to utilize the symmetries present in the crystals, which are defined by their space group. Existing methods for crystal property prediction either have symmetry constraints that are too restrictive or only incorporate symmetries between unit cells. In addition, these models do not explicitly model the porous structure of the crystal. In this paper, we develop a model which incorporates the symmetries of the unit cell of a crystal in its architecture and explicitly models the porous structure. We evaluate our model by predicting the heat of adsorption of CO$_2$ for different configurations of the mordenite zeolite. Our results confirm that our method performs better than existing methods for crystal property prediction and that the inclusion of pores results in a more efficient model. Homogeneous space ; Porosity ; Porous medium ; Crystal (programming language) ; Symmetry (geometry) ; Materials science ; Computer science ; Process (computing) ; Equivariant map ; Zeolite ; Property (philosophy) ; Mathematics ; Chemistry ; Geometry ; Pure mathematics ; Composite material Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Ferda Mindivan | Meryem Göktaş
Source: DergiPark (Istanbul University) (article, 2023-03-21)
Field: Materials Science — Polymers and Plastics

Grafen türevleri dolgulu PCL kompozit filmlerin sentezi, yapısal özellikleri ve biyobozunurluğu Polikaprolakton (PCL), yavaş biyobozunurluğu nedeniyle ilaç salınımsistemlerinde ve doku mühendisliğinde sınırlı kullanıma sahiptir. PCL’nin bozunma sürecini üzerine çalışmalar devam etmektedir. Bu çalışma,grafen oksit (GO), indirgenmiş grafen oksit (RGO) ve grafennanoplatelet (GNP) dolgu maddelerinin PCL kompozit filmlerin yapısalve biyobozunurluk özellikleri üzerindeki etkilerini belirlemek amacıylayapılmıştır. GO, RGO ve GNP dolgularının aynı miktarları (ağ.% 0.5)kullanılarak sıvı faz ultrasonik karıştırma yöntemi ile PCL kompozitfilmleri hazırlanmıştır. Filmlerin karakterizasyon çalışmaları X-IşınıKırınımı (XRD), Fourier dönüşümlü Infrared spektroskopisi (FTIR),Optik mikroskop (OM) ve yüzey pürüzlülüğü testleri ile analiz edilmiştir.Tüm dolgu türlerinin polimer matrikse homojen dağıldığı ve polimerdolgu etkileşimlerinin sağlandığı tespit edilmiştir. Grafen türevlerininçekirdeklenme üzerinde farklı etkiler gösterdiği ve PCL/RGO filminin enince taneli yapıya sahip olduğu tespit edilmiştir. En yüksek gözeneklilikoranıyla (%23.49) ve 12.33 µm yüzey pürüzlülüğü değeri ile PCL/GOfilminde en yüksek ağırlık kaybı gerçekleşmiştir. Bu çalışma oksijeniçeren fonksiyonel gruplara sahip grafen türevlerinin (GO ve RGO) PCLmatriksine ilave edilmesi ile biyobozunurluk artışının PCL’ nin bozunmasürecini kontrol etmek için uygun bir yol olduğunu önermektedir. Materials science Textile materials and evaluations