jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hyun Seung Kim | Jonggeol Na | Won Bo Lee
Source: arXiv (Cornell University) (preprint, 2021-02-27)
Field: Materials Science — Materials Chemistry

Generative Chemical Transformer: Neural Machine Learning of Molecular Geometric Structures from Chemical Language via Attention Discovering new materials better suited to specific purposes is an important issue in improving the quality of human life. Here, a neural network that creates molecules that meet some desired conditions based on a deep understanding of chemical language is proposed (Generative Chemical Transformer, GCT). The attention mechanism in GCT allows a deeper understanding of molecular structures beyond the limitations of chemical language itself which cause semantic discontinuity by paying attention to characters sparsely. It is investigated that the significance of language models for inverse molecular design problems by quantitatively evaluating the quality of the generated molecules. GCT generates highly realistic chemical strings that satisfy both chemical and linguistic grammar rules. Molecules parsed from generated strings simultaneously satisfy the multiple target properties and vary for a single condition set. These advances will contribute to improving the quality of human life by accelerating the process of desired material discovery. Computer science ; Transformer ; Generative grammar ; Parsing ; Artificial intelligence ; Artificial neural network ; Natural language processing ; Process (computing) ; Chemical process ; Chemistry ; Engineering ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Trevor David Rhone | Robert A. Hoyt | C. R. O'Connor | M. M. Montemore | C.Siva Kumar | C. M. Friend | Efthimios Kaxiras
Source: Bulletin of the American Physical Society (article, 2021-03-15)
Field: Materials Science — Materials Chemistry

Predicting outcomes of catalytic reactions using machine learning Predicting the outcome of a chemical reaction using efficient computational models can be used to develop high-throughput screening techniques. This can significantly reduce the number of experiments needed to be performed in a huge search space, which saves time, effort and expense. Recently, machine learning methods have been bolstering conventional structure-activity relationships used to advance understanding of chemical reactions. We have developed a model to predict the products of catalytic reactions on the surface of oxygen-covered and bare gold using machine learning. Using experimental data, we developed a machine learning model that maps reactants to products, using a chemical space representation. This involves predicting a chemical space value for the products, and then matching this value to a molecular structure chosen from a database. The database was developed by applying a set of possible reaction outcomes using known reaction mechanisms. Our machine learning approach complements chemical intuition in predicting the outcome of several types of chemical reactions. In some cases, machine learning makes correct predictions where chemical intuition fails. We achieve up to 93% prediction accuracy for a small data set of less than two hundred reactions. Chemical space ; Intuition ; Machine learning ; Computer science ; Artificial intelligence ; Chemical reaction ; Outcome (game theory) ; Chemistry ; Mathematics ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Text Analysis Techniques
Authors: Kenneth Atz | Clemens Isert | Markus N. A. Böcker | José Jiménez-Luna | Gisbert Schneider
Source:  (preprint, 2021-09-03)
Field: Materials Science — Materials Chemistry

Open-source Δ-quantum machine learning for medicinal chemistry Certain molecular design tasks benefit from fast and accurate calculations of quantum-mechanical (QM) properties. However, the computational cost of QM methods applied to drug-like compounds currently makes large-scale applications of quantum chemistry challenging. In order to mitigate this problem, we developed DelFTa, an open-source toolbox for predicting small-molecule electronic properties at the density functional (DFT) level of theory, using the Δ-machine learning principle. DelFTa employs state-of-the-art E(3)-equivariant graph neural networks that were trained on the QMugs dataset of QM properties. It provides access to a wide array of quantum observables by predicting approximations to ωB97X-D/def2-SVP values from a GFN2-xTB semiempirical baseline. Δ-learning with DelFTa was shown to outperform direct DFT learning for most of the considered QM endpoints. The software is provided as open-source code with fully-documented command-line and Python APIs. Computer science ; Density functional theory ; Toolbox ; Quantum ; Python (programming language) ; Open source ; Source code ; Quantum chemistry ; Theoretical computer science ; Software ; Observable ; Computational science ; Artificial intelligence ; Machine learning ; Computational chemistry ; Chemistry ; Molecule ; Quantum mechanics ; Programming language ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: JCS Kadupitiya | Nasim Anousheh | Vikram Jadhao
Source: arXiv (Cornell University) (preprint, 2021-10-27)
Field: Materials Science — Materials Chemistry

Designing Machine Learning Surrogates using Outputs of Molecular\n Dynamics Simulations as Soft Labels Molecular dynamics simulations are powerful tools to extract the microscopic\nmechanisms characterizing the properties of soft materials. We recently\nintroduced machine learning surrogates for molecular dynamics simulations of\nsoft materials and demonstrated that artificial neural network based regression\nmodels can successfully predict the relationships between the input material\nattributes and the simulation outputs. Here, we show that statistical\nuncertainties associated with the outputs of molecular dynamics simulations can\nbe utilized to train artificial neural networks and design machine learning\nsurrogates with higher accuracy and generalizability. We design soft labels for\nthe simulation outputs by incorporating the uncertainties in the estimated\naverage output quantities, and introduce a modified loss function that\nleverages these soft labels during training to significantly reduce the\nsurrogate prediction error for input systems in the unseen test data. The\napproach is illustrated with the design of a surrogate for molecular dynamics\nsimulations of confined electrolytes to predict the complex relationship\nbetween the input electrolyte attributes and the output ionic structure. The\nsurrogate predictions for the ionic density profiles show excellent agreement\nwith the ground truth results produced using molecular dynamics simulations.\nThe high accuracy and small inference times associated with the surrogate\npredictions provide quick access to quantities derived using the number density\nprofiles and facilitate rapid sensitivity analysis.\n Computer science ; Molecular dynamics ; Generalizability theory ; Artificial neural network ; Artificial intelligence ; Inference ; Machine learning ; Surrogate model ; Biological system ; Mathematics ; Chemistry Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Electrostatics and Colloid Interactions
Authors: Filip Ekström | Rickard Armiento | Fredrik Lindsten
Source: arXiv (Cornell University) (preprint, 2021-07-01)
Field: Materials Science — Materials Chemistry

Graph-based machine learning beyond stable materials and relaxed crystal structures There has been a recent surge of interest in using machine learning to approximate density functional theory (DFT) in materials science. However, many of the most performant models are evaluated on large databases of computed properties of, primarily, materials with precise atomic coordinates available, and which have been experimentally synthesized, i.e., which are thermodynamically stable or metastable. These aspects provide challenges when applying such models on theoretical candidate materials, for example for materials discovery, where the coordinates are not known. To extend the scope of this methodology, we investigate the performance of the Crystal Graph Convolutional Neural Network (CGCNN) on a data set of theoretical structures in three related ternary phase diagrams (Ti,Zr,Hf)-Zn-N, which thus include many highly unstable structures. We then investigate the impact on the performance of using atomic positions that are only partially relaxed into local energy minima. We also explore options for improving the performance in these scenarios by transfer learning, either from models trained on a large database of mostly stable systems, or a different but related phase diagram. Models pre-trained on stable materials do not significantly improve performance, but models trained on similar data transfer very well. We demonstrate how our findings can be utilized to generate phase diagrams with a major reduction in computational effort. Maxima and minima ; Computer science ; Phase diagram ; Metastability ; Convolutional neural network ; Graph ; Algorithm ; Theoretical computer science ; Ternary operation ; Machine learning ; Density functional theory ; Artificial intelligence ; Phase (matter) ; Mathematics ; Computational chemistry ; Physics ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Yuanbin Liu | Xin Liu | Bing Cao
Source: Research Square (preprint, 2021-11-12)
Field: Materials Science — Materials Chemistry

Multiscale Graph Attention Neural Networks for Mapping Materials and Molecules beyond Short-Range Interatomic Correlations Range (aeronautics) ; Artificial neural network ; Statistical physics ; Graph ; Computer science ; Theoretical computer science ; Physics ; Artificial intelligence ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Ruimin Ma | Hanfeng Zhang | Jiaxin Xu | Yasuhiko Hayashi | Ryo Yoshida | Junichiro Shiomi | Tengfei Luo
Source: arXiv (Cornell University) (preprint, 2021-09-07)
Field: Materials Science — Materials Chemistry

Machine Learning-Assisted Exploration of Thermally Conductive Polymers Based on High-Throughput Molecular Dynamics Simulations Finding amorphous polymers with higher thermal conductivity is important, as they are ubiquitous in heat transfer applications. With recent progress in material informatics, machine learning approaches have been increasingly adopted for finding or designing materials with desired properties. However, relatively limited effort has been put into finding thermally conductive polymers using machine learning, mainly due to the lack of polymer thermal conductivity databases with reasonable data volume. In this work, we combine high-throughput molecular dynamics (MD) simulations and machine learning to explore polymers with relatively high thermal conductivity (&gt; 0.300 W/m-K). We first randomly select 365 polymers from the existing PolyInfo database and calculate their thermal conductivity using MD simulations. The data are then employed to train a machine learning regression model to quantify the structure-thermal conductivity relation, which is further leveraged to screen polymer candidates in the PolyInfo database with thermal conductivity &gt; 0.300 W/m-K. 133 polymers with MD-calculated thermal conductivity above this threshold are eventually identified. Polymers with a wide range of thermal conductivity values are selected for re-calculation under different simulation conditions, and those polymers found with thermal conductivity above 0.300 W/m-K are mostly calculated to maintain values above this threshold despite fluctuation in the exact values. A classification model is also constructed, and similar results were obtained compared to the regression model in predicting polymers with thermal conductivity above or below 0.300 W/m-K. The strategy and results from this work may contribute to automating the design of polymers with high thermal conductivity. Thermal conductivity ; Polymer ; Materials science ; Work (physics) ; Thermal conduction ; Conductivity ; Molecular dynamics ; Amorphous solid ; Machine learning ; Computer science ; Thermodynamics ; Composite material ; Physics ; Chemistry ; Organic chemistry ; Computational chemistry ; Physical chemistry Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Thermal properties of materials
Authors: 吉伸 鎌倉 | Yoshinobu Kamakura
Source: Institutional Repositories DataBase (IRDB) (article, 2021-01-01)
Field: Chemistry — Inorganic Chemistry

Machine-Learning-Assisted Selective Synthesis of a Semiconductive Silver Thiolate Coordination Polymer with Segregated Paths for Holes and Electrons Machine-Learning-Assisted Selective Synthesis of a Semiconductive Silver Thiolate Coordination Polymer with Segregated Paths for Holes and Electrons, Machine-Learning-Assisted Selective Synthesis of a Semiconductive Silver Thiolate Coordination Polymer with Segregated Paths for Holes and Electrons Coordination polymer ; Polymer ; Electron ; Metal ; Transition metal Metal-Organic Frameworks: Synthesis and Applications ; Polyoxometalates: Synthesis and Applications ; Perovskite Materials and Applications
Authors: Hiromasa Kaneko
Source: MEMBRANE (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Research on Design of Molecules, Materials, and Processes Using Machine Learning In researching, developing, and manufacturing highly functional materials such as membrane materials, it becomes common to utilize chemical data and chemical engineering data for machine learning to improve the efficiency of molecular design, material design, process design, and process control. It is important to construct mathematical model y ＝f (x) with high predictive ability between explanatory variables x and objective variables y, and then, y values can be predicted from x values using the constructed model, and x values can be designed to meet target y values. In this article, as examples of research in chemoinformatics, materials informatics, and process informatics, the estimation of prediction errors in new samples, modeling of metal–organic frameworks with machine learning, adaptive design of experiments with direct inverse analysis for designs of molecules, materials, and processes, and prediction of future transmembrane pressure in a drinking water treatment process are introduced. Cheminformatics ; Materials informatics ; Process (computing) ; Computer science ; Process design ; Construct (python library) ; Inverse ; Machine learning ; Informatics ; Engineering design process ; Artificial intelligence ; Data mining ; Process engineering ; Engineering ; Chemistry ; Mathematics ; Mechanical engineering ; Health informatics ; Engineering informatics ; Computational chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Sam Lemonick﻿
Source: C&EN Global Enterprise (article, 2021-02-08)
Field: Materials Science — Materials Chemistry

Machine learning maps reaction space A machine-learning technique first developed for understanding language can accurately classify reactions according to type ( Nat. Mach. Intell. 2021, DOI: 10.1038/s42256-020-00284-w ). The model also tagged reactions with computer-readable codes that allow chemists to search for similar reactions. Transformers are a type of machine-learning algorithm useful for interpreting sequences of information. They’re widely used in translation software and voice assistants like Amazon’s Alexa, but chemists recently have shown their utility in chemistry (see page 19) . Philippe Schwaller of IBM Research–Zurich and the University of Bern and colleagues show the transformer approach can classify reactions by type, identifying broad categories like carbon-carbon bond formation or deprotection and finer-scale groups like chloro or bromo Suzuki coupling . When categorizing reactions, the machine-learning model matched the classifications assigned by software that used human-coded rules 98% of the time. The researchers tested their model on data sets containing tens of thousands of Space (punctuation) ; Computer science ; Artificial intelligence ; Operating system Machine Learning in Materials Science
Authors: Eva Michelle Allen | Linda Y. Lim | Albert Liu | Michael F. Toney | Jordi Cabana | Johanna Nelson Weker
Source: ECS Meeting Abstracts (article, 2021-05-30)
Field: Materials Science — Surfaces, Coatings and Films

Deep Learning Segmentation of Operando X-Ray Microtomography of 18650 Lithium-Ion Batteries for Electric Vehicles The US Advanced Battery Consortium goals for 2023 call for low cost, fast charge, electric vehicle (EV) batteries with a 15-minute charge time at 80% pack capacity. [1] This milestone would have a huge impact on the universal market adoption of EVs. Unfortunately, fast charging at rates above 1C, defined as the current at which it takes one hour for a battery to charge/discharge to full capacity, imposes extensive deleterious effects on current lithium-ion battery (LiB) chemistries. High charging rates aggressively accelerate degradation mechanisms owed to structural damage due to both increased cycling temperature and inhomogeneous Li ion mass transport properties. The induced electrode damage can cause void formation between the active material and conductive-binder matrix and delamination from the current collecting foil. The now electronically isolated active material results in large costs on the capacity retention of the battery. In this study, we report measurements via operando X-ray microtomography of cylindrical cells used in EVs under simulated fast charge and drive cycles. Three batteries were measured during cycling after antecedent fast charging cycles, the 3rd, 5th, and 81st - 82nd cycles were analyzed to track morphological damage at different battery life points. To track the spatial and morphological degradation of both the anode and cathode structures, we employed a deep learning segmentation method using the U-Net convolutional neural network (CNN). Using a Euclidean Distance Mapping method, void formation is tracked spatially in 3-dimensions within the electrode coating. Insight into how fast charging induces structural damage will better inform research into fast-charge protocols and new battery chemistries for electrolytes, electrolyte additives, and novel electrode architectures. [1] Liu, Y.; Zhu, Y.; Cui, Y. Challenges and Opportunities towards Fast-Charging Battery Materials. Nat. Energy 2019 , 4 (7), 540–550. https://doi.org/10.1038/s41560-019-0405-3. Anode ; Materials science ; Battery (electricity) ; Cathode ; Electrode ; Lithium-ion battery ; Void (composites) ; Battery pack ; Computer science ; Composite material ; Nuclear engineering ; Electrical engineering ; Chemistry ; Engineering ; Physics Electron and X-Ray Spectroscopy Techniques ; Advanced Battery Technologies Research ; Advanced Neural Network Applications
Authors: Alisha Sharma | Ryan F. Johnson | Adam Moses | David A. Kessler
Source: AIAA Scitech 2021 Forum (article, 2021-01-04)
Field: Materials Science — Materials Chemistry

Toward Compact, Numerically Stable Machine Learning Functions for Chemical Kinetics View Video Presentation: https://doi.org/10.2514/6.2021-1785.vid In this study, we systematically evaluate several different machine learning approaches for suitability in reacting flow simulations. Chemical kinetics are a singificant bottleneck in combustion simulations: solving the chemistry source term can take 80–90% of the total compute time for the simulation, which makes many fuels and scenarios computationally prohibitive, especially at-scale. Machine learning techniques offer an interesting alternative to traditional chemistry surrogate models due to their potential for accurate, detailed, and cheap predictions. Unfortunately, it is very challenging to develop stable, accurate models that are able to represent the full parameter range. Previous work shows the potential of machine learning techniques—from tree-based regression to deep neural networks—but many of these networks are too unstable over time or represent too narrow of a parameter range for practical use. In this study, we model a hydrogen mechnism using a number of different machine learning architectures, training techniques, and physics constraints, developing a set of best-practices for practical ML-CFD integration. Bottleneck ; Computer science ; Machine learning ; Artificial neural network ; Range (aeronautics) ; Artificial intelligence ; Set (abstract data type) ; Combustion ; Chemistry ; Engineering Machine Learning in Materials Science
Authors: И. А. Рубцов | K.A. Karpov | Artem Mitrofanov
Source:  (article, 2021-10-27)
Field: Materials Science — Materials Chemistry

PREDICTION OF THE BAND GAP OF CRYSTAL MATERIALS USING GRAPH NEURAL NETWORK In this paper, graph convolutional neural network is modeled to predict band gap from the crystal structure using the experimental base. Computer science ; Artificial neural network ; Crystal (programming language) ; Graph ; Artificial intelligence ; Materials science ; Theoretical computer science ; Programming language Machine Learning in Materials Science ; Material Selection and Properties ; Composite Material Mechanics
Authors: Changhae Andrew Kim | Nathan D. Ricke | Troy Van Voorhis
Source:  (preprint, 2021-08-05)
Field: Materials Science — Materials Chemistry

Machine Learning Dynamic Correlation in Chemical Kinetics The kinetics of surface reactions are often described using a lattice model. Since it is expensive to propagate the configuration probabilities of the entire lattice, it is practical to consider the occupation probabilities of a typical site or a cluster of sites instead. This amounts to a moment closure approximation of the chemical master equation (CME). Unfortunately, simple closures, such as the mean-field (MF) and the pair approximation (PA), exhibit weaknesses in systems with significant long-range correlation. In this paper, we show that machine learning (ML) can be used to construct accurate moment closures in chemical kinetics, using the lattice Lotka-Volterra model (LLVM) as a model system. We trained feed-forward neural networks (FFNNs) to estimate the three-site occupation probabilities, using kinetic Monte Carlo (KMC) results at select values of rate constants and initial conditions. Given the same level of input as PA, the machine learning moment closure (MLMC) gave drastic improvements in the simulated dynamics and descriptions of the dynamical regimes throughout the parameter space. In this way, MLMC is a promising tool to interpolate KMC simulations or construct pre-trained closures that would enable researchers to extract useful insight at a fraction of the computational cost. Moment closure ; Lattice (music) ; Monte Carlo method ; Statistical physics ; Moment (physics) ; Closure (psychology) ; Computer science ; Artificial neural network ; Machine learning ; Mathematics ; Algorithm ; Physics ; Thermodynamics ; Classical mechanics ; Statistics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion