jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: S. Mathew | Aben Regi Abraham | Sandhya Chintalapati | Soumya Sarkar | Boby Joseph | T. Venkatesan
Source: Condensed Matter (article, 2020-11-19)
Field: Materials Science — Materials Chemistry

Temperature Dependent Structural Evolution of WSe2: A Synchrotron X-ray Diffraction Study A thorough investigation of the structural parameters of micromechanically exfoliated multilayer WSe2 flakes was undertaken between 400 K to 110 K. Crystal structure of WSe2 remains in the trigonal prismatic structure in this temperature range, however, with a clear difference in the temperature dependence of the in-plane a, and the out-of-plane c, lattice parameters. The linear coefficients of thermal expansion of a and c are 5.132 × 10−6/K and 8.105 × 10−6/K, respectively. The temperature dependence of the unit-cell volume is analyzed using zero-pressure equation-of-state which yielded the Debye temperature of the WSe2 to be 160 K. Following the temperature dependence of the W-Se and W-W bond distances, a nonlinear behavior is observed in the former in contrast to a rather regular behavior of the later. This significant difference in the temperature dependence of the a and c lattice parameters can have consequences in the macroscopic physical properties of the system. A good correlation between the temperature dependence of the W-Se bond distance and Raman E2g1 mode has been observed. Thermal expansion ; Atmospheric temperature range ; Raman spectroscopy ; Materials science ; Diffraction ; Synchrotron ; Lattice (music) ; Condensed matter physics ; Bond length ; Debye model ; Synchrotron radiation ; Crystal structure ; Negative thermal expansion ; Crystallography ; Thermodynamics ; Chemistry ; Physics ; Optics 2D Materials and Applications ; MXene and MAX Phase Materials ; Perovskite Materials and Applications
Authors: Gyoung S. Na | Hyunju Chang | Hyun Woo Kim
Source: Physical Chemistry Chemical Physics (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Machine-guided representation for accurate graph-based molecular machine learning In chemistry-related fields, graph-based machine learning has received significant attention as atoms and their chemical bonds in a molecule can be represented as a mathematical graph. However, many molecular properties are sensitive to changes in the molecular structure. For this reason, molecules have a mixed distribution for their molecular properties in molecular space, and it consequently makes molecular machine learning difficult. However, this problem has not been investigated in either chemistry or computer science. To tackle this problem, we propose a robust and machine-guided molecular representation based on deep metric learning (DML), which automatically generates an optimal representation for a given dataset. To this end, we first adopt DML for molecular machine learning by integrating it with graph neural networks (GNNs) and devising a new objective function for representation learning. In experimental evaluations, machine learning algorithms with the proposed method achieved better prediction accuracy than state-of-the-art GNNs. Furthermore, the proposed method was also effective on extremely small datasets, and this result is impressive because many real world applications suffer from a lack of training data. Molecular graph ; Computer science ; Artificial intelligence ; Machine learning ; Representation (politics) ; Graph ; Molecular machine ; Metric (unit) ; Chemical space ; Theoretical computer science ; Chemistry ; Drug discovery ; Nanotechnology ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Chang‐Ho Hong | Jeong Min Choi | Wonseok Jeong | Sungwoo Kang | Suyeon Ju | Kyeongpung Lee | Jisu Jung | Yong Youn | Seungwu Han
Source: Physical review. B./Physical review. B (article, 2020-12-18)
Field: Materials Science — Materials Chemistry

Training machine-learning potentials for crystal structure prediction using disordered structures Prediction of the stable crystal structure for multinary (ternary or higher) compounds with unexplored compositions demands fast and accurate evaluation of free energies in exploring the vast configurational space. The machine-learning potential such as the neural network potential (NNP) is poised to meet this requirement but a dearth of information on the crystal structure poses a challenge in choosing training sets. Herein we propose constructing the training set from density functional theory (DFT)--based dynamical trajectories of liquid and quenched amorphous phases, which does not require any preceding information on material structures except for the chemical composition. To demonstrate suitability of the trained NNP in the crystal structure prediction, we compare NNP and DFT energies for ${\mathrm{Ba}}_{2}{\mathrm{AgSi}}_{3}$, ${\mathrm{Mg}}_{2}{\mathrm{SiO}}_{4}$, ${\mathrm{LiAlCl}}_{4}$, and ${\mathrm{InTe}}_{2}{\mathrm{O}}_{5}\mathrm{F}$ over experimental phases as well as low-energy crystal structures that are generated theoretically. For every material, we find strong correlations between DFT and NNP energies, ensuring that the NNPs can properly rank energies among low-energy crystalline structures. We also find that the evolutionary search using the NNPs can identify low-energy metastable phases more efficiently than the DFT-based approach. By proposing a way to developing reliable machine-learning potentials for the crystal structure prediction, this work paves the way to identifying unexplored multinary phases efficiently. Training (meteorology) ; Artificial intelligence ; Machine learning ; Crystal structure prediction ; Computer science ; Crystal structure ; Pattern recognition (psychology) ; Physics ; Crystallography ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Xinpeng Zhao | Haiyou Huang | Cheng Wen | Yanjing Su | Ping Qian
Source: Computational Materials Science (article, 2020-01-14)
Field: Materials Science — Materials Chemistry

Accelerating the development of multi-component Cu-Al-based shape memory alloys with high elastocaloric property by machine learning Ternary operation ; Shape-memory alloy ; Refrigeration ; Thermodynamics ; Component (thermodynamics) ; Materials science ; Principal component analysis ; Alloy ; Development (topology) ; Entropy (arrow of time) ; Metallurgy ; Biological system ; Computer science ; Mathematics ; Artificial intelligence ; Physics ; Mathematical analysis Shape Memory Alloy Transformations ; High Entropy Alloys Studies ; Phase-change materials and chalcogenides
Authors: Takeshi Aoyagi
Source: Computational Materials Science (article, 2020-12-10)
Field: Materials Science — Materials Chemistry

Deep learning model for predicting phase diagrams of block copolymers Block copolymers show various microphase-separated structures depending on their chain architecture and the interaction parameters between the different chemical structures of monomeric unit χ. Self-consistent field theory is a powerful tool to predict such phase-separated structures, and phase diagrams of block copolymers have been reported using self-consistent field theory. However, obtaining stable morphology of each polymer structure and the χ parameter requires intensive computational study. We applied deep learning to predict phase diagrams from metastable structures obtained by crude, cost-effective self-consistent field calculation. We used a 3D convolutional neural network for classification of the metastable structures, and a limited number of sets of block copolymer structures and χ parameters with stable phase labels were used for training. After the model was trained using the training set, it successfully assigned the metastable structures of a wide variety of diblock and triblock copolymers to the correct stable phases. This approach is capable of predicting the phase diagrams of block copolymers effectively without intensive self-consistent field calculation. Copolymer ; Phase diagram ; Metastability ; Phase (matter) ; Block (permutation group theory) ; Materials science ; Field (mathematics) ; Monomer ; Computer science ; Deep learning ; Polymer ; Chemical physics ; Statistical physics ; Artificial intelligence ; Chemistry ; Mathematics ; Physics ; Geometry ; Organic chemistry Block Copolymer Self-Assembly ; Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization
Authors: Yihang Li | Bin Xiao | Yuchao Tang | Fu Liu | Xiaomeng Wang | Feinan Yan | Yi Liu
Source: The Journal of Physical Chemistry C (article, 2020-12-17)
Field: Materials Science — Materials Chemistry

Center-Environment Feature Model for Machine Learning Study of Spinel Oxides Based on First-Principles Computations Spinel oxides have attracted extensive attention due to their unique physical, chemical, optical, and electronic properties with their applications in lithium batteries, photocatalysts, and ferroelectricity. Owing to a large number of possible cation substitutions, many novel potential oxides are yet to be discovered with interesting properties. Inspired by the data-driven materials design approach, in this work, we developed machine learning (ML) models based on the first-principles computational data to investigate the energy and structure properties of normal cubic spinel oxides. The density functional theory (DFT) calculations were first carried out for 5329 spinel oxides with cubic AB2O4 structures where A and B sites were substituted with 73 elements, respectively. We predicted 451 new spinel oxides more stable than all the studied known experimental structures worth for further experimental investigation. We found the “good” A/B elements stabilizing spinel oxides include II–IV group elements and rare-earth elements in the periodic table. Furthermore, we proposed a new "Center-Environment" (CE) model to construct features containing both the composition and structure information as inputs to machine learning algorithms. Based on the DFT data, we developed ML models using a support vector regression algorithm to predict accurately and efficiently the formation energies, lattice parameters, and band gaps of spinel oxides. The composition design principles proposed in this work prompt the experimental discovery of new spinel oxides and the CE feature model can be generally applied in the data-driven materials design by ML methods. Spinel ; Feature (linguistics) ; Computation ; Center (category theory) ; Computer science ; Artificial intelligence ; Materials science ; Metallurgy ; Chemistry ; Algorithm ; Philosophy Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions
Authors: Amir Kordijazi | Hathibelagal M. Roshan | Arushi Dhingra | Marco Povolo | Pradeep K. Rohatgi | Michael Nosonovsky
Source: Surface Innovations (article, 2020-06-22)
Field: Materials Science — Surfaces, Coatings and Films

Machine-learning methods to predict the wetting properties of iron-based composites The authors used three different methods of statistical data analysis to establish correlations between the water contact angle (CA) on ductile iron and composition, roughness (grit size), elapsed time between sample preparation and CA measurement and droplet size. The three methods are linear regression analysis (LRA), artificial neural network (ANN) model and multivariate polynomial regression analysis. It was established that the size of the water droplet is statistically insignificant, while correlations with the other three parameters were found. Surface roughness is the most important predictor of CA. A low coefficient of determination of the linear regression indicates that the correlation is non-linear. The ANN model showed much stronger predictive potential than LRA. The authors discuss the correlation with the experimental values of the CA and the physical mechanisms behind the observed trends. It is particularly promising that the ANN can be trained to predict the wetting characteristics. The application of machine-learning methods to synthesize new materials and coatings with desired surface properties, such as self-cleaning, is a technology that may become part of the emergent ‘triboinformatics’ field, related to the application of machine-learning methods to surface science and engineering. Wetting ; Surface roughness ; Linear regression ; Materials science ; Artificial neural network ; Multivariate statistics ; Regression analysis ; Surface finish ; Machine learning ; Contact angle ; Correlation coefficient ; Polynomial ; Polynomial regression ; Response surface methodology ; Support vector machine ; Bayesian multivariate linear regression ; Artificial intelligence ; Composite material ; Computer science ; Mathematics Surface Modification and Superhydrophobicity ; Electrohydrodynamics and Fluid Dynamics ; Surface Roughness and Optical Measurements
Authors: Xiang Gao | Farhad Ramezanghorbani | Olexandr Isayev | Justin S. Smith | Adrián E. Roitberg
Source: ChemRxiv (preprint, 2020-05-04)
Field: Materials Science — Materials Chemistry

TorchANI: A Free and Open Source PyTorch Based Deep Learning Implementation of the ANI Neural Network Potentials &lt;div&gt;This paper presents TorchANI, a PyTorch based software for training/inference&lt;/div&gt;&lt;div&gt;of ANI (ANAKIN-ME) deep learning models to obtain potential energy surfaces and&lt;/div&gt;&lt;div&gt;other physical properties of molecular systems. ANI is an accurate neural network&lt;/div&gt;&lt;div&gt;potential originally implemented using C++/CUDA in a program called NeuroChem.&lt;/div&gt;&lt;div&gt;Compared with NeuroChem, TorchANI has a design emphasis on being light weight,&lt;/div&gt;&lt;div&gt;user friendly, cross platform, and easy to read and modify for fast prototyping, while&lt;/div&gt;&lt;div&gt;allowing acceptable sacrifice on running performance. Because the computation of&lt;/div&gt;&lt;div&gt;atomic environmental vectors (AEVs) and atomic neural networks are all implemented&lt;/div&gt;&lt;div&gt;using PyTorch operators, TorchANI is able to use PyTorch’s autograd engine to automatically compute analytical forces and Hessian matrices, as well as do force training&lt;/div&gt;&lt;div&gt;without additional codes required.&lt;/div&gt; Hessian matrix ; Computer science ; Artificial neural network ; Deep learning ; Artificial intelligence ; Computational science ; Mathematics ; Applied mathematics Machine Learning in Materials Science ; Fuel Cells and Related Materials
Authors: Nick Gerrits | Jan Geweke | Egidius W. F. Smeets | Johannes Voss | Alec M. Wodtke | Geert–Jan Kroes
Source: The Journal of Physical Chemistry C (article, 2020-06-24)
Field: Materials Science — Materials Chemistry

Closing the Gap Between Experiment and Theory: Reactive Scattering of HCl from Au(111) Accurate simulation of molecules reacting on metal surfaces, which can help in improving heterogeneous catalysts, remains out of reach for several reactions. For example, a large disagreement between theory and experiment for HCl reacting on Au(111) still remains, despite many efforts. In this work, the dissociative chemisorption of HCl on Au(111) is investigated with a recently developed MGGA density functional (MS-RPBEl) and a high-dimensional neural network potential. Additionally, previous experimental sticking probabilities are re-examined. A considerably improved agreement between experiment and theory is obtained, although theory still overestimates experimental sticking probabilities by a factor of 2–7 at the highest incidence energy. Computed and measured vibrational transition probabilities are also in improved agreement. Several dynamical effects such as angular steering and energy transfer from the molecule to the surface are found to play an important role. Sticking probability ; Chemisorption ; Density functional theory ; Molecule ; Work (physics) ; Scattering ; Atomic physics ; Closing (real estate) ; Vibrational energy ; Transition metal ; Chemistry ; Materials science ; Chemical physics ; Physics ; Computational chemistry ; Physical chemistry ; Thermodynamics ; Catalysis ; Quantum mechanics ; Adsorption ; Desorption Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Surface Chemistry and Catalysis
Authors: Tian Wang | Mingqi Shao | Rong Guo | Fei Tao | Gang Zhang | Hichem Snoussi | Xingling Tang
Source: Advanced Functional Materials (article, 2020-12-01)
Field: Materials Science — Materials Chemistry

Surrogate Model via Artificial Intelligence Method for Accelerating Screening Materials and Performance Prediction Abstract Predicting the performance of mechanical properties is an important and current issue in the field of engineering and materials science, but traditional experiments and modeling calculations often consume large amounts of time and resources. Therefore, it is imperative to use appropriate methods to accelerate the process of material selection and design. The artificial intelligence method, particularly deep learning models, has been verified as an effective and efficient method for handling computer vision and neural language problems. In this paper, a deep learning surrogate model (DLS) is proposed for predicting the mechanical performance of materials, that is, the maximum stress value under complex working conditions. The DLS can reproduce the finite element analysis model results with 98.79% accuracy. The results show that deep learning has great potential. This research also provides a new approach for material screening in practical engineering. Artificial intelligence ; Surrogate model ; Artificial neural network ; Field (mathematics) ; Computer science ; Deep learning ; Machine learning ; Process (computing) ; Selection (genetic algorithm) ; Materials science Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Advanced machining processes and optimization
Authors: Kaiwen Wang | Yinan Wang | Xiaowei Yue | Wenjun Cai
Source: Corrosion Science (article, 2020-11-02)
Field: Materials Science — Materials Chemistry

Multiphysics modeling and uncertainty quantification of tribocorrosion in aluminum alloys Tribocorrosion ; Multiphysics ; Materials science ; Corrosion ; Tafel equation ; Pitting corrosion ; Stress (linguistics) ; Metallurgy ; Anode ; Finite element method ; Composite material ; Structural engineering ; Electrochemistry ; Engineering Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Orthopaedic implants and arthroplasty
Authors: Zheng Cheng | Dongbo Zhao | Jing Ma | Wei Li | Shuhua Li
Source: The Journal of Physical Chemistry A (article, 2020-05-27)
Field: Materials Science — Materials Chemistry

An On-the-Fly Approach to Construct Generalized Energy-Based Fragmentation Machine Learning Force Fields of Complex Systems An on-the-fly fragment-based machine learning (ML) approach was developed to construct machine learning force fields for large complex systems. In this approach, the energy, forces, and molecular properties of the target system are obtained by combining machine learning force fields of various subsystems with the generalized energy-based fragmentation (GEBF) approach. Using a nonparametric Gaussian process (GP) model, all the force fields of subsystems are automatically generated online without data selection and parameter optimization. With the GEBF-ML force field constructed for a normal alkane, C60H122, long-time molecular dynamics (MD) simulations are performed on different sizes of alkanes, and the predicted energy, forces, and molecular properties (dipole moment) are favorably comparable with full quantum mechanics (QM) calculations. The predicted IR spectra also show excellent agreement with the direct ab initio MD results. Our results demonstrate that the GEBF-ML method provides an automatic and efficient way to build force fields for a broad range of complex systems such as biomolecules and supramolecular systems. Force field (fiction) ; Dipole ; Computer science ; Ab initio ; Statistical physics ; Artificial intelligence ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Mass Spectrometry Techniques and Applications
Authors: Fu‐Zhi Dai | Bo Wen | Huimin Xiang | Yanchun Zhou
Source: Journal of the European Ceramic Society (article, 2020-06-07)
Field: Materials Science — Materials Chemistry

Grain boundary strengthening in ZrB2 by segregation of W: Atomistic simulations with deep learning potential Grain boundary ; Materials science ; Grain boundary strengthening ; Molecular dynamics ; Chemical physics ; Metallurgy ; Microstructure ; Computational chemistry ; Physics Nuclear Materials and Properties ; Boron and Carbon Nanomaterials Research ; Advanced ceramic materials synthesis
Authors: Anna Costine | Paige Delsa | Tianxi Li | Petra Reinke | Prasanna V. Balachandran
Source: Journal of Applied Physics (article, 2020-12-17)
Field: Materials Science — Materials Chemistry

Data-driven assessment of chemical vapor deposition grown MoS2 monolayer thin films Growth of high quality two-dimensional transition metal dichalcogenide monolayers with the desired microstructure and morphology is critical for enabling key technological solutions. This is a non-trivial problem because the processing space is vast and lack of a priori guidelines impedes rapid progress. A machine learning approach is discussed that leverages the data present in published growth experiments to predict growth performance in regions of unexplored parameter space. Starting from the literature data on MoS2 thin films grown using chemical vapor deposition (CVD), a database is manually constructed. Unsupervised and supervised machine learning methods are used to learn from the compiled data by extracting trends that underlie the formation of MoS2 monolayers. Design rules are uncovered that establish the phase boundaries classifying monolayers from other possible outcomes, which offers future guidance of CVD experiments. Monolayer ; Chemical vapor deposition ; Thin film ; Computer science ; Materials science ; A priori and a posteriori ; Key (lock) ; Deposition (geology) ; Nanotechnology ; Space (punctuation) ; Unsupervised learning ; Chemical space ; Artificial intelligence ; Chemistry ; Geology 2D Materials and Applications ; MXene and MAX Phase Materials ; Ferroelectric and Negative Capacitance Devices