jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Debjyoti Bhattacharya | Harrison J. Cassady | Michael A. Hickner | Wesley F. Reinhart
Source: ChemRxiv (preprint, 2024-05-21)
Field: Materials Science — Materials Chemistry

Large Language Models as Molecular Design Engines The design of small molecules is crucial for technological applications ranging from drug discovery to energy storage. Due to the vast design space available to modern synthetic chemistry, the community has increasingly sought to use data-driven and machine learning approaches to navigate this space. Although generative machine learning methods have recently shown potential for computational molecular design, their use is hindered by complex training procedures, and they often fail to generate valid and unique molecules. In this context, pre-trained Large Language Models (LLMs) have emerged as potential tools for molecular design, as they appear to be capable of creating and modifying molecules based on simple instructions provided through natural language prompts. In this work, we show that the Claude 3 Opus LLM can read, write, and modify molecules according to prompts, with an impressive 97% valid and unique molecules. By quantifying these modifications in a low-dimensional latent space, we systematically evaluate the model’s behavior under different prompting conditions. Notably, the model is able to perform guided molecular generation when asked to manipulate the electronic structure of molecules using simple, natural-language prompts. Our findings highlight the potential of LLMs as powerful and versatile molecular design engines. Computer science Machine Learning in Materials Science ; Topic Modeling
Authors: Kai‐Le Jiang | Huai‐Qian Wang | Huifang Li | Shuwan Pan | Hao Zheng | Yong‐Hang Zhang | Jiaming Zhang
Source: Journal of Chemical Information and Modeling (article, 2024-12-30)
Field: Materials Science — Materials Chemistry

SchNet_IIA: Potential Energy Surface Fitting by Interatomic Interactions Attention Based on Transfer Learning Analysis Machine learning methods for fitting potential energy surfaces and molecular dynamics simulations are becoming increasingly popular due to their potentially high accuracy and savings in computational resources. However, existing application models often rely on basic architectures like artificial neural networks (ANNs) and multilayer perceptron (MLP), lagging behind cutting-edge technologies in the machine learning domain. Furthermore, the complexity of current machine learning frameworks leads to reduced interpretability and challenges for improvement. Herein, we developed a model analysis method based on the feature-representation-transfer approach to directly perform causal analysis on the model. The internal action characteristics of the SchNet framework were successfully analyzed by constructing different source tasks and we proposed interatomic interactions attention for the characterization of doped clusters. The accuracy was enhanced by 0.015 eV/atom compared to the original model. The ability to capture atomic environment characteristics was significantly improved. The activation function was smoothed resulting in a 23.47% increase in the convergence speed. Our SchNet_IIA model demonstrates superior performance in capturing interatomic interactions. Our present work is of distinctive value as it presents a novel transfer learning analysis method with the potential to evolve into a generalized model analysis approach, providing new perspectives and solutions for the field. Computer science ; Interpretability ; Artificial neural network ; Artificial intelligence ; Field (mathematics) ; Interatomic potential ; Machine learning ; Representation (politics) ; Feature (linguistics) ; Function (biology) ; Multilayer perceptron ; Molecular dynamics ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Neha Neha | Manya Aggarwal | Aashutosh Soni | Tarak Karmakar
Source: ChemRxiv (preprint, 2024-03-21)
Field: Chemistry — Physical and Theoretical Chemistry

Polymorph-Specific Solubility Prediction using Constant Chemical Potential Molecular Dynamics Molecular Dynamics (MD) simulations offer a robust approach to understanding material properties within a system. Solubility is defined as the analytical composition of a saturated solution expressed as a proportion of designated solute in a designated solvent, according to IUPAC. It is a critical property of compounds and holds significance across numerous fields. Various computational techniques have been explored for determining solubility, including methods based on chemical potential determination, enhanced sampling simulation, and direct coexistence simulation, and lately, machine learning-based methods have shown promise. In this investigation, we aim to find the solubility values of a compound through Constant Chemical Potential Molecular Dynamics, a method rooted in direct coexistence simulation. The primary purpose of using this method is to overcome the limitation of the direct simulation method by maintaining a constant chemical potential for a sufficiently long time. Urea is chosen as a prototypical system for our study, with a particular focus on three of its polymorphs. Our approach effectively discriminates between the polymorphs of urea based on their respective solubility values; polymorph III is found to have the highest solubility followed by form IV and I. Solubility ; Molecular dynamics ; Chemistry ; Solvent ; Thermodynamics ; Constant (computer programming) ; Molecule ; Computational chemistry ; Biological system ; Physical chemistry ; Computer science ; Organic chemistry ; Physics Crystallography and molecular interactions ; Machine Learning in Materials Science ; Analytical Chemistry and Chromatography
Authors: Haoke Qiu | Zhao‐Yan Sun
Source: ChemRxiv (preprint, 2024-06-06)
Field: Materials Science — Materials Chemistry

On-Demand Reverse Design of Polymers with PolyTAO The forward screening and reverse design of drug molecules, inorganic molecules, and polymers with enhanced properties are vital for accelerating the transition from laboratory research to market application. Specifically, due to the scarcity of large-scale datasets, the discovery of polymers via materials informatics is particularly challenging. Nonetheless, scientists have developed various machine learning models for polymer structure-property relationships using only small polymer datasets, thereby advancing the forward screening process of polymers. However, the success of this approach ultimately depends on the diversity of the candidate pool, and exhaustively enumerating all possible polymer structures through human imagination is impractical. Consequently, achieving on-demand reverse design of polymers is essential. In this work, we curate an immense polymer dataset containing nearly one million polymeric structure-property pairs based on expert knowledge. Leveraging this dataset, we propose a Transformer-Assisted Oriented pretrained model for on-demand polymer generation (PolyTAO). This model produces polymers with 99.27% chemical validity in top-1 generation mode (approximately 200k generated polymers), representing the highest reported success rate among polymer generative models. Additionally, the average R2 between the properties of the generated polymers and their expected values across 15 predefined properties is 0.96. To further evaluate the pretrained model's performance in generating polymers with additional user-defined properties for downstream tasks, we conduct fine-tuning experiments on three publicly available small polymer datasets using both semi-template and template-free generation paradigms. Through these extensive experiments, we demonstrate that our pretrained model and its fine-tuned versions are capable of achieving on-demand reverse design of polymers with specified properties, whether in semi-template generation or the more challenging template-free generation scenarios, showcasing its potential as a unified pretrained foundation model for polymer generation. Polymer ; Computer science ; Artificial intelligence ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Tairan Wang | Runhai Ouyang | Jianyu Hu | Yutao Wang | Shu Wu | Xuting Chai | Sulei Hu | Wei‐Xue Li
Source: ChemRxiv (preprint, 2024-04-08)
Field: Materials Science — Materials Chemistry

Nature of Metal-Support Interaction Discovered by Interpretable Machine Learning Metal catalysts supported on oxides play a paramount role in numerous industrial reactions. ‎Modulating metal-support interaction is a key strategy to boost ‎catalytic productivity and ‎stability; however, the nature of metal-support interaction and quantification remain major ‎unsolved problems. By ‎leveraging interpretable ‎machine learning, domain knowledge, and ‎experimental data available, we discover a physical metal-support interaction equation ‎applicable ‎to metal nanoparticles and adatoms on oxides, and oxide films on metals. Though ‎metal-oxygen interaction dominates metal-support interaction and determines the metal ‎composition effect, metal-metal interaction delineates the support effect. This ensures a principle ‎of strong metal-metal interaction for encapsulation of suboxide over metal ‎nanoparticles, ‎substantiated comprehensively by molecular dynamics simulations and ‎previous experiments. The ‎developed theory provides valuable insights and guidance in engineering the metal-support ‎systems. ‎ Artificial intelligence ; Computer science ; Machine learning ; Psychology ; Cognitive science Metal Alloys Wear and Properties ; Tunneling and Rock Mechanics ; Industrial Engineering and Technologies
Authors: Mingyu Li | Kun Song | Mingzhu Zhao | G.F. You | Jie Zhong | Mengxi Zhao | Arong Li | Yu Chen | Guobin Li | Ying Kong | Jiacheng Wei | Zhaofu Wang | Jiamin Zhou | Hongbing Yang | Shichao Ma | Hailong Zhang | Irakoze Loïca Mélita | Weidong Lin | Yuhang Lu | Zhengtian Yu | Xun Lu | Jixiao He | Yu‐Jun Zhao | Jian Zhang
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-12-21)
Field: Materials Science — Materials Chemistry

Electron-density informed effective and reliable <i>de novo</i> molecular design and lead optimization with ED2Mol Abstract Generative drug design opens new avenues for discovering novel compounds within the vast chemical space rather than conventional screening against limited compound libraries. However, the practical utility of the generated molecules is frequently constrained, as many designs prioritize a narrow range of pharmacological properties while neglecting physical reliability, which hinders the success rate of subsequent wet-lab evaluations. To address this, we propose ED2Mol, a deep learning-based approach that leverages fundamental electron density information to improve de novo molecular generation and lead optimization. The extensive evaluations across multiple benchmarks demonstrate that ED2Mol surpasses existing methods in terms of generation success rate and &gt;97% physical reliability. It also facilitates automated lead optimization that is not fully implemented by other methods using fragment-based strategies. Furthermore, ED2Mol exhibits generalizability to more challenging, unseen allosteric pocket benchmarks, attaining consistent performance in both de novo molecule generation and lead optimization. More importantly, ED2Mol has been applied to various real-world essential targets, successfully identifying wet-lab validated bioactive compounds, ranging from FGFR3 orthosteric inhibitors to CDC42 allosteric inhibitors and GCK allosteric activators. The directly generated binding modes of these compounds with target proteins are close to predictions through molecular docking and further validated via the X-ray co-crystal structure. All these results highlight ED2Mol’s potential as a useful tool in realistic drug design with enhanced effectiveness, physical reliability, and practical applicability. Lead (geology) ; Electron ; Nanotechnology ; Computer science ; Materials science ; Physics ; Biology ; Nuclear physics Machine Learning in Materials Science ; Electrochemical Analysis and Applications ; Electron and X-Ray Spectroscopy Techniques
Authors: Tomas Hardwick | Nisar Ahmed
Source: ChemRxiv (preprint, 2024-06-05)
Field: Chemistry — Electrochemistry

Automation in Electrifying Flow Organic Synthesis The automated chemicals flow in organic electrochemistry will accelerate the scale-up process. In traditional flow electrochemistry, the reaction parameters (concentration, current density, flow rate, temperature, pressure) can be precisely adjusted. However, control over these parameters in the automated electrochemical flow system will be innovative. Multi- objective algorithms that link hardware and digital chemical operations on software can establish the autonomous system for flow electro-organic reactions. Electrochemical transformations are controlled by the application of electrical current, so the chemical process in an electrochemical microreactor can be organised and started or stopped via computer- controlled power supply. Such control over chemical transformations is not possible in conventional chemical reactions. Due to advantage of this unique feature of electrochemical process, new methods for acquisition of real-time reaction data are possible with the help of online analysis and self-optimising machine learning algorithms. Herein, we build our opinions and describe the perspective of automation in flow organic electrochemistry. Automation ; Flow (mathematics) ; Continuous flow ; Computer science ; Process engineering ; Systems engineering ; Engineering ; Mechanical engineering ; Biochemical engineering ; Mechanics ; Physics Electrochemical Analysis and Applications ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion
Authors: Yinglong Hu | Ronghao Li | Shaolin Fang | Jian Luo | Hao Ma
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Chemistry — Organic Chemistry

Tunable Thermal Conductivity and Anisotropy of Two-Dimensional Fullerene Networks Controlled by Covalent Bonding Connections Covalent bond ; Thermal conductivity ; Anisotropy ; Fullerene ; Materials science ; Conductivity ; Thermal ; Condensed matter physics ; Nanotechnology ; Composite material ; Chemistry ; Physics ; Thermodynamics ; Optics ; Organic chemistry ; Physical chemistry Fullerene Chemistry and Applications ; Thermal properties of materials ; Graphene research and applications
Authors: Irene Caro-Campos | Marta María González-Barrios | Óscar J. Durá | Erik Fransson | José J. Plata | David Ávila‐Brande | Javier Fdez. Sanz | Jesús Prado‐Gonjal | Antonio M. Márquez
Source: ChemRxiv (preprint, 2024-05-07)
Field: Materials Science — Materials Chemistry

Challenges reconciling theory and experiments in the prediction of lattice thermal conductivity: the case of Cu-based sulvanites The exploration of large chemical spaces in search of new thermoelectric materials requires the integration of experiments, theory, simulations, and data science. The development of high-throughput strategies that combine DFT calculations with machine learning has emerged as a powerful approach for discovering new materials. However, experimental validation is crucial to confirm the accuracy of these workflows. This validation becomes especially important in understanding the transport properties that govern the thermoelectric performance of materials since they are highly influenced by synthetic, processing, and operating conditions. In this work, we explore the thermal conductivity of Cu-based sulvanites using a combination of theoretical and experimental methods. Previous discrepancies and significant variations in reported data for Cu3VS4 and Cu3VSe4 are explained using the Boltzmann Transport Equation for phonons and by synthesizing well-characterized defect-free samples. The use of machine learning approaches for extracting high-order force constants opens doors to charting the lattice thermal conductivity across the entire Cu-based sulvanite family—finding not only materials with Kl values below 2 W/mK at moderate temperatures but also rationalizing their thermal transport properties based on chemical composition. Thermal conductivity ; Lattice (music) ; Statistical physics ; Condensed matter physics ; Materials science ; Physics ; Thermodynamics Ferroelectric and Piezoelectric Materials ; Thermal Expansion and Ionic Conductivity ; Electrical and Thermal Properties of Materials
Authors: Cai-Yuan Ye | Hongming Weng | Quan-Sheng Wu
Source: arXiv (Cornell University) (preprint, 2024-03-19)
Field: Materials Science — Materials Chemistry

Con-CDVAE: A method for the conditional generation of crystal structures In recent years, progress has been made in generating new crystalline materials using generative machine learning models, though gaps remain in efficiently generating crystals based on target properties. This paper proposes the Con-CDVAE model, an extension of the Crystal Diffusion Variational Autoencoder (CDVAE), for conditional crystal generation. We introduce innovative components, design a two-step training method, and develop three unique generation strategies to enhance model performance. The effectiveness of Con-CDVAE is demonstrated through extensive testing under various conditions, including both single and combined property targets. Ablation studies further underscore the critical role of the new components in achieving our model's performance. Additionally, we validate the physical credibility of the generated crystals through Density Functional Theory (DFT) calculations, confirming Con-CDVAE's potential in material science research. Computer science Crystallization and Solubility Studies
Authors: Nathan Boulangeot | Florian Brix | Frédéric Sur | Émilie Gaudry
Source: Journal of Chemical Theory and Computation (article, 2024-08-19)
Field: Materials Science — Materials Chemistry

Hydrogen, Oxygen, and Lead Adsorbates on Al<sub>13</sub>Co<sub>4</sub>(100): Accurate Potential Energy Surfaces at Low Computational Cost by Machine Learning and DFT-Based Data Intermetallic compounds are promising materials in numerous fields, especially those involving surface interactions, such as catalysis. A key factor to investigate their surface properties lies in adsorption energy maps, typically built using first-principles approaches. However, exploring the adsorption energy landscapes of intermetallic compounds can be cumbersome, usually requiring huge computational resources. In this work, we propose an efficient method to predict adsorption energies, based on a Machine Learning (ML) scheme fed by a few Density Functional Theory (DFT) estimates performed on <i>n</i> sites selected through the Farthest Point Sampling (FPS) process. We detail its application on the Al<sub>13</sub>Co<sub>4</sub>(100) quasicrystalline approximant surface for several atomic adsorbates (H, O, and Pb). On this specific example, our approach is shown to outperform both simple interpolation strategies and the recent ML force field MACE [arXiv.2206.07697], especially when the number <i>n</i> is small, i.e., below 36 sites. The ground-truth DFT adsorption energies are much more correlated with the predicted FPS-ML estimates (Pearson R-factor of 0.71, 0.73, and 0.90 for H, O and Pb, respectively, when <i>n</i> = 36) than with interpolation-based or MACE-ML ones (Pearson R-factors of 0.43, 0.39, and 0.56 for H, O, and Pb, in the former case and 0.22, 0.35, and 0.63 in the latter case). The unbiased root-mean-square error (ubRMSE) is lower for FPS-ML than for interpolation-based and MACE-ML predictions (0.15, 0.17, and 0.17 eV, respectively, for hydrogen and 0.17, 0.25, and 0.22 eV for lead), except for oxygen (0.55, 0.47, and 0.46 eV) due to large surface relaxations in this case. We believe that these findings and the corresponding methodology can be extended to a wide range of systems, which will motivate the discovery of novel functional materials. Adsorption ; Interpolation (computer graphics) ; Density functional theory ; Computer science ; Mean squared error ; Hydrogen ; Materials science ; Algorithm ; Chemistry ; Physical chemistry ; Physics ; Computational chemistry ; Mathematics ; Artificial intelligence ; Statistics ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Chemical Physics Studies
Authors: Yuan‐Chao Hu | Tian Jian
Source: Qeios (preprint, 2024-12-10)
Field: Materials Science — Materials Chemistry

PyMatterSim: a Python Data Analysis Library for Computer Simulations of Materials Science, Physics, Chemistry, and Beyond Computer simulation has become one of the most important tools in scientific research in many disciplines. Benefiting from the dynamical trajectories regulated by versatile interatomic interactions, various material properties can be quantitatively characterized at the atomic scale. This greatly deepens our understanding of Nature and provides incredible insights supplementing experimental observations. Hitherto, a plethora of literature discusses the computational discoveries in studying glasses in which positional disorder is inherent in their configurations. Motivated by active research and knowledge sharing, we developed a data analysis library in Python for computational materials science research. We hope to help promote scientific progress and narrow some technical gaps for the wide communities. The toolkit mainly focuses on physical analyses of glassy properties from the open-source simulator LAMMPS. Nevertheless, the code design renders high flexibility, with functionalities extendable to other computational tools. The library provides data-driven insights for different subjects and can be incorporated into advanced machine-learning workflows. The scope of the data analysis methodologies applies not only to materials science but also to physics, chemistry, and beyond. Python (programming language) ; Workflow ; Computer science ; Scope (computer science) ; Data science ; Flexibility (engineering) ; Computational science ; Nanotechnology ; Programming language ; Materials science Material Dynamics and Properties ; Theoretical and Computational Physics ; Phase Equilibria and Thermodynamics
Authors: Ravi G. Patel | Cosmin Safta | Reese E. Jones
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Equivariant Graph Convolutional Neural Networks for the Rep- Resentation of Homogenized Anisotropic Microstructural Mechanical Response Equivariant map ; Graph ; Anisotropy ; Convolutional neural network ; Computer science ; Mathematics ; Physics ; Combinatorics ; Artificial intelligence ; Pure mathematics ; Optics Machine Learning in Materials Science