jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lin Chen | Qing Shen | Jungang Lou
Source: Research Square (preprint, 2022-05-20)
Field: Materials Science — Materials Chemistry

Magicmol - Fast and variable synthetic accessibility molecule generative model with reinforcement learning Abstract The flourishment of machine learning and deep learning methods have boosted the development of cheminformatics, especially when it comes to the application of drug discovery and new materials exploration. Lower time and space expenses make it possible for scientists to search the enormous chemical space for novel molecules. During this process, the generative model plays a significant role in quickly generating novel molecules, including predefined properties. Recently, some work combines reinforcement learning strategies with a generative model to optimize the property of generated drug-like molecules, which notably improved a batch of critical factors when it comes to drug discovery. However, a common problem of these RNN (recurrent neural network) based methods is that the novelty and validity of generated molecules depend on the “teacher-forcing strategy.” which is learned from an extensive database. Thus, there is inevitable to meet this situation that several generated molecules have difficulty in synthesizing even if owning higher desired properties such as binding score. In this paper, we proposed a new pipeline called Magicmol to directly change the synthetic accessibility of RNN-based generative models using reinforcement learning without introducing extra parameters. With proper strategies, the model can generate either hard-to-synthesis or easy-to-synthesis molecules with high validity. And our result shows a significant shift in the distribution of molecules with different synthetic accessibility when compared with the original model ,and we concluded some possible applications of Magicmol. Reinforcement learning ; Chemical space ; Computer science ; Artificial intelligence ; Generative model ; Generative grammar ; Cheminformatics ; Machine learning ; Recurrent neural network ; Novelty ; Drug discovery ; Process (computing) ; Artificial neural network ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Genetics, Bioinformatics, and Biomedical Research
Authors: Brenden G. Pelkie | Stéphanie Valleau
Source: Digital Discovery (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Machine learning the quantum flux–flux correlation function for catalytic surface reactions Gaussian process regressors were successfuly trained to predict fully quantum flux–flux correlation functions and the corresponding reaction rate constants. Flux (metallurgy) ; Function (biology) ; Quantum ; Surface (topology) ; Correlation ; Catalysis ; Physics ; Materials science ; Quantum mechanics ; Chemistry ; Mathematics ; Geometry ; Biology Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Machine Learning and ELM
Authors: 亮 高橋 | Akira Takahashi | 悠 熊谷 | Yu Kumagai | 宏賢 青木 | Hirotaka Aoki | 亮 田村 | Ryo Tamura | 史康 大場 | Fumiyasu Oba
Source: Institutional Repositories DataBase (IRDB) (article, 2022-04-01)
Field: Materials Science — Materials Chemistry

Adaptive sampling methods via machine learning for materials screening Sampling (signal processing) ; Noise (video) ; Support vector machine ; Feature (linguistics) ; Pattern recognition (psychology) ; Training set Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Bayesian Methods and Mixture Models
Authors: Xiang Li | Zhe Li | Ji Chen
Source: arXiv (Cornell University) (preprint, 2022-03-29)
Field: Materials Science — Materials Chemistry

Ab initio calculation of real solids via neural network ansatz Neural networks have been applied to tackle many-body electron correlations for small molecules and physical models in recent years. Here we propose a new architecture that extends molecular neural networks with the inclusion of periodic boundary conditions to enable ab initio calculation of real solids. The accuracy of our approach is demonstrated in four different types of systems, namely the one-dimensional periodic hydrogen chain, the two-dimensional graphene, the three-dimensional lithium hydride crystal, and the homogeneous electron gas, where the obtained results, e.g. total energies, dissociation curves, and cohesive energies, outperform many traditional ab initio methods and reach the level of the most accurate approaches. Moreover, electron densities of typical systems are also calculated to provide physical intuition of various solids. Our method of extending a molecular neural network to periodic systems can be easily integrated into other neural network structures, highlighting a promising future of ab initio solution of more complex solid systems using neural network ansatz, and more generally endorsing the application of machine learning in materials simulation and condensed matter physics. Ab initio ; Artificial neural network ; Ansatz ; Ab initio quantum chemistry methods ; Periodic boundary conditions ; Statistical physics ; Physics ; Computer science ; Materials science ; Molecule ; Quantum mechanics ; Boundary value problem ; Artificial intelligence Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced Physical and Chemical Molecular Interactions
Authors: Irina V. Sterkhova | Л. В. Камаева | V. I. Lad’yanov | N. M. Chtchelkatchev
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Structure and Solidification of the (Fe0.75b0.15si0.1)100-Xtax (X=0-2) Melts: Experiment and Machine Learning Materials science Magnetic Properties and Applications ; Microstructure and Mechanical Properties of Steels ; Machine Learning in Materials Science
Authors: Kunal Ghosh
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2022-05-13)
Field: Materials Science — Materials Chemistry

Assessing the potential of active machine learning for curating molecular datasets Active learning (machine learning) ; Support vector machine ; Feature (linguistics) ; Task (project management) ; Key (lock) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Cell Image Analysis Techniques
Authors: Irina V. Sterkhova | Л. В. Камаева | V. I. Lad’yanov | N. M. Chtchelkatchev
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Structure and Solidification of the (Fe &lt;sub&gt;0.75&lt;/sub&gt;B &lt;sub&gt;0.15&lt;/sub&gt;Si &lt;sub&gt;0.1&lt;/sub&gt;) &lt;sub&gt;100-x&lt;/sub&gt;Ta &lt;sub&gt;x&lt;/sub&gt; (X=0–2) Melts Humanities ; Art Magnetic Properties of Alloys ; Metallic Glasses and Amorphous Alloys ; Rare-earth and actinide compounds
Authors: Andrei A. Tereshchenko | D. M. Pashkov | Alexander A. Guda | С. А. Гуда | Yury V. Rusalev | А. В. Солдатов
Source: Journal of Surface Investigation X-ray Synchrotron and Neutron Techniques (article, 2022-10-01)
Field: Materials Science — Materials Chemistry

Application of Machine Learning Methods to Approximate the Binding Energy of CO Molecules on the Surface of Pd Nanoparticles This manuscript presents a study of the applicability of machine learning methods for the problem of binding energy approximation of carbon monoxide adsorbates on the surface of a palladium nanoparticle. Machine learning algorithms were trained using a set of structures that represent models of CO interaction with different parts of the Pd55 nanocluster with a variable distance from the molecule to the surface, for which the energy was calculated using the density functional theory methods. For structures that make up a training set, the radial distribution functions were calculated. Using these functions and their segments as descriptors, the effectiveness of various machine learning algorithms, such as “gradient boosting,” “ridge regression,” “extra trees,” and “support vector machines” for calculating the binding energy, was tested. Based on three different metrics, it was found that the error in determining the binding energy was the smallest when using the “support vector machines”: the mean absolute error was 0.093 eV. The efficiency of various individual sections of the distribution function used as descriptors was compared. It was found that it is crucial to take into account the part of the radial distribution function from 1.5 to 2.5 Å for a correct approximation of the energy. Support vector machine ; Energy (signal processing) ; Artificial intelligence ; Ridge ; Surface (topology) ; Computer science ; Artificial neural network ; Machine learning ; Algorithm ; Mathematics ; Geometry ; Statistics Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: 真一 大川原 | Shinichi Ookawara | 知輝 保田 | Tomoki Yasuda | 陽介 松田 | Yosuke Matsuda | 史郎 吉川 | Shiro Yoshikawa | 秀行 松本 | Hideyuki Matsumoto
Source: Institutional Repositories DataBase (IRDB) (article, 2022-06-01)
Field: Materials Science — Materials Chemistry

Machine Learning in Porous Materials: SVM-Based Characterization and CGAN-Driven Materials Discovery and Design Characterization (materials science) ; Porosity ; Active learning (machine learning) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Nanoporous metals and alloys
Authors: Saba Kharabadze | A. Thorn | Ekaterina A. Koulakova | Aleksey N. Kolmogorov
Source: npj Computational Materials (article, 2022-06-28)
Field: Materials Science — Materials Chemistry

Prediction of stable Li-Sn compounds: boosting ab initio searches with neural network potentials Abstract The Li-Sn binary system has been the focus of extensive research because it features Li-rich alloys with potential applications as battery anodes. Our present re-examination of the binary system with a combination of machine learning and ab initio methods has allowed us to screen a vast configuration space and uncover a number of overlooked thermodynamically stable alloys. At ambient pressure, our evolutionary searches identified an additional stable Li 3 Sn phase with a large BCC-based hR48 structure and a possible high- T LiSn 4 ground state. By building a simple model for the observed and predicted Li-Sn BCC alloys we constructed an even larger viable hR75 structure at an exotic 19:6 stoichiometry. At 20 GPa, low-symmetry 11:2, 5:1, and 9:2 phases found with our global searches destabilize previously proposed phases with high Li content. The findings showcase the appreciable promise machine-learning interatomic potentials hold for accelerating ab initio prediction of complex materials. Ab initio ; Binary number ; Ab initio quantum chemistry methods ; Stoichiometry ; Artificial neural network ; Ground state ; Materials science ; Crystal structure prediction ; Computer science ; Thermodynamics ; Machine learning ; Crystal structure ; Physics ; Chemistry ; Crystallography ; Atomic physics ; Physical chemistry ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Thermal Expansion and Ionic Conductivity ; Inorganic Chemistry and Materials
Authors: 啓介 井手 | Keisuke Ide | 貴義 片瀬 | Takayoshi Katase | 秀雄 細野 | HIDEO HOSONO | 利夫 神谷 | TOSHIO KAMIYA
Source: Institutional Repositories DataBase (IRDB) (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Local bonding structures in amorphous oxide semiconductors studied by DFT and machine-learning potential Amorphous semiconductors ; Work (physics) ; Oxide ; Semiconductor ; Amorphous solid Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Chemical and Physical Properties of Materials
Authors: 俊太 千頭 | Shunta Chikami | 智広 林 | Tomohiro Hayashi
Source: Institutional Repositories DataBase (IRDB) (article, 2022-12-01)
Field: Materials Science — Surfaces, Coatings and Films

Prediction of serum adsorption onto polymer brush films by machine learning Adsorption ; Polymer ; Brush ; Polymer brush Polymer Surface Interaction Studies ; Nanocomposite Films for Food Packaging ; Hydrogels: synthesis, properties, applications
Authors: Jun Ni | Xinming Wang | Nguyen-Dung Tran | Shuming Zeng | Cong Hou | Ying Chen
Source: Research Square (preprint, 2022-06-27)
Field: Materials Science — Materials Chemistry

Element-wise representations with ECNet for material property prediction and applications in high-entropy alloys Abstract To develop machine learning models for accurate property prediction, the current graph networks are designed to give a sufficient representations of materials. However, the relationships between the atomic and structural inputs and many target properties are very complex, and even insensitive to the local environment. Here, we propose the elemental convolution (EC) operation to obtain a more general and global element-wise representations, and develop EC graph neural networks (ECNet) to accurately model material properties. We demonstrate that the ECNet models show better prediction in properties like band gaps, refractive index, and elastic moduli of crystals. The element-wise feature vectors are able to capture information of elemental types and crystal structures, and show a special characteristics related to its target properties. To explore its application on high entropy alloys, we focus on the properties modelling on CrFeNiCoMn/Pd high entropy alloys. Multi-task learning of ECNet models show its advantage in accuracy and convenience for multiple property predictions. Furthermore, we demonstrate that the transfer learning (TL) from the less-principal element alloys can enhance model performance in multiple-principal element alloys. Generally, the TL element-wise feature vectors extracted from parent model can be used as universal descriptors and retain good accuracy at small data limits when combined with simple multi-layer perceptrons. Finally, the TL framework is used to do interpolations in Fe-Co-Mn system to understand the underlying physics including the stability and magnetic moment.PACS numbers: 74.25.-q,71.15.-m,89.20.Ff Perceptron ; Convolutional neural network ; Computer science ; Entropy (arrow of time) ; Artificial neural network ; Graph ; Element (criminal law) ; Algorithm ; Moduli ; Artificial intelligence ; Theoretical computer science ; Physics ; Thermodynamics Machine Learning in Materials Science ; High Entropy Alloys Studies ; Chalcogenide Semiconductor Thin Films
Authors: Khachik Sargsyan | Logan Williams | Katherine Johnston | Habib N. Najm
Source:  (article, 2022-04-01)
Field: Materials Science — Materials Chemistry

Quantification and Propagation of Uncertainties in Machine Learning Interatomic Potentials for Molecular Dynamics. Abstract not provided. Molecular dynamics ; Computer science ; Dynamics (music) ; Interatomic potential ; Statistical physics ; Artificial intelligence ; Physics ; Computational chemistry ; Chemistry Machine Learning in Materials Science
Authors: Subramanian K. R. S. Sankaranarayanan | Bilvin Varughese | Sukriti Manna | Troy D. Loeffler | Rohit Batra | Mathew J. Cherukara
Source: Research Square (preprint, 2022-07-12)
Field: Materials Science — Materials Chemistry

Transfer and Active Learning of High Dimensional Neural Network Potentials for Transition Metal Clusters and Bulk Abstract Classical molecular dynamics (MD) simulations represent a very popular and powerful tool for materials modeling and design. The predictive power of MD hinges on the ability of the interatomic potential to capture the underlying physics and chemistry. There have been decades of seminal work on developing interatomic potentials albeit with a focus predominantly on capturing the properties of bulk materials. Such physics-based models, while extensively deployed for predicting dynamics and properties of nanoscale systems over the past two decades, tend to perform poorly in predicting nanoscale potential energy surface when compared to high-fidelity first-principles calculations. These limitations stem from the lack of flexibility in such models, which rely on a pre-defined functional form. Machine learning models and approaches have emerged as a viable alternative to capture the diverse size-dependent cluster geometries, nanoscale dynamics and the complex nanoscale potential energy surfaces (PES), without sacrificing the bulk properties. Here, we introduce an ML workflow that combines transfer and active learning strategies to develop high-dimensional neural networks (NN) for capturing cluster and bulk properties for several different transition metals with applications in catalysis, microelectronics, and energy storage to name a few. Our NN first learns the bulk PES from the high-quality physics-based models in literature and subsequently augments this learning via retraining with a higher fidelity first-principles training dataset to concurrently capture both the nanoscale and bulk PES. Our workflow departs from status-quo in its ability to learn from a sparsely sampled dataset that nonetheless covers a diverse range of cluster configurations from near-equilibrium to highly non-equilibrium as well as learning strategies that iteratively improves the fingerprinting depending on model fidelity. All the developed models are rigorously tested against an extensive first-principles dataset of energies and forces of cluster configurations as well as several properties of bulk configurations for 10 different transition metals. Our approach is material agnostic and provides a methodology to transfer and build upon the learnings from decades of seminal work in molecular simulations on to a new generation of ML trained potentials to accelerate materials discovery and design. Transition metal ; Artificial neural network ; Transition (genetics) ; Chemical physics ; Materials science ; Nanotechnology ; Chemistry ; Computer science ; Artificial intelligence ; Catalysis Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Bilvin Varughese | Sukriti Manna | Troy D. Loeffler | Rohit Batra | Mathew J. Cherukara | Subramanian K.R.S. Sankaranarayanan
Source: ChemRxiv (preprint, 2022-06-13)
Field: Materials Science — Materials Chemistry

Transfer and Active Learning of High Dimensional Neural NetworkPotentials for Transition Metal Clusters and Bulk Classical molecular dynamics (MD) simulations represent a very popular and powerful tool for materials modeling and design. The predictive power of MD hinges on the ability of the interatomic potential to capture the underlying physics and chemistry. There have been decades of seminal work on developing interatomic potentials albeit with a focus predominantly on capturing the properties of bulk materials. Such physics-based models, while extensively deployed for predicting dynamics and properties of nanoscale systems over the past two decades, tend to perform poorly in predicting nanoscale potential energy surface when compared to high-fidelity first-principles calculations. These limitations stem from the lack of flexibility in such models, which rely on a pre-defined functional form. Machine learning models and approaches have emerged as a viable alternative to capture the diverse size-dependent cluster geometries, nanoscale dynamics and the complex nanoscale potential energy surfaces (PES), without sacrificing the bulk properties. Here, we introduce an ML workflow that combines transfer and active learning strategies to develop high-dimensional neural networks (NN) for capturing cluster and bulk properties for several different transition metals with applications in catalysis, microelectronics, and energy storage to name a few. Our NN first learns the bulk PES from the high-quality physics-based models in literature and subsequently augments this learning via retraining with a higher fidelity first-principles training dataset to concurrently capture both the nanoscale and bulk PES. Our workflow departs from status-quo in its ability to learn from a sparsely sampled dataset that nonetheless covers a diverse range of cluster configurations from near-equilibrium to highly non-equilibrium as well as learning strategies that iteratively improves the fingerprinting depending on model fidelity. All the developed models are rigorously tested against an extensive first-principles dataset of energies and forces of cluster configurations as well as several properties of bulk configurations for 10 different transition metals. Our approach is material agnostic and provides a methodology to transfer and build upon the learnings from decades of seminal work in molecular simulations on to a new generation of ML trained potentials to accelerate materials discovery and design. Workflow ; Flexibility (engineering) ; Nanotechnology ; Computer science ; Molecular dynamics ; Transfer of learning ; Deep learning ; Materials science ; Artificial intelligence ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Electron and X-Ray Spectroscopy Techniques
Authors: Б. А. Панченко | A. E. Mayer
Source: Physical Mesomechanics of Materials. Physical Principles of Multi-Layer Structure Forming and Mechanisms of Non-Linear Behavior: Conference Proceedings (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

DEVELOPMENT OF POTENTIALS FOR INTERATOMIC INTERACTION BASED ON NEURAL NETWORKS FOR Al-Cu ALLOYS 1 Interatomic potential ; Artificial neural network ; Materials science ; Computer science ; Chemical physics ; Artificial intelligence ; Chemistry ; Molecular dynamics ; Computational chemistry Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Welding Techniques and Residual Stresses
Authors: 凌太 中澤 | Ryota Nakazawa | 勇気 源 | Yuki Minamoto | 中順 井上 | Nakamasa Inoue | 護 店橋 | MAMORU TANAHASHI
Source: Institutional Repositories DataBase (IRDB) (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Species reaction rate modelling based on physics-guided machine learning Feature (linguistics) ; Support vector machine ; Training set ; Feature selection Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Machine Learning in Bioinformatics
Authors: Jacob Startt | Mitchell Wood | Megan McCarthy | Andrew Kustas | Sean Donegan | Rémi Dingreville
Source:  (article, 2022-02-01)
Field: Materials Science — Materials Chemistry

Expediting the materials discovery process of MPEAs through atomistic modeling and machine learning techniques. Abstract not provided. Expediting ; Process (computing) ; Computer science ; Process modeling ; Work in process ; Systems engineering ; Engineering ; Operating system Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; X-ray Diffraction in Crystallography
Authors: Wei Wang | Wenkai Fu | Steven R. Spurgeon | Chongmin Wang | Yuyan Shao | Amra Peles
Source: Research Square (preprint, 2022-06-03)
Field: Materials Science — Materials Chemistry

Deep-Learning-Based Prediction of Nanoparticle Phase Transitions During In Situ Transmission Electron Microscopy Transmission electron microscopy ; In situ ; Nanoparticle ; Materials science ; Phase (matter) ; Electron microscope ; Microscopy ; Transmission (telecommunications) ; Phase transition ; Nanotechnology ; Artificial intelligence ; Optics ; Computer science ; Physics ; Condensed matter physics ; Telecommunications ; Quantum mechanics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications