jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Janine George | Guido Petretto | Aakash Ashok Naik | Marco Esters | Adam Jackson | Ryky Nelson | Richard Dronskowski | Gian‐Marco Rignanese | Geoffroy Hautier
Source: ChemRxiv (preprint, 2022-03-21)
Field: Materials Science — Materials Chemistry

Automated Bonding Analysis with Crystal Orbital Hamilton Populations Understanding crystalline structures based on their chemical bonding is growing in importance. In this context, chemical bonding can be studied with the Crystal Orbital Hamilton Population (COHP), allowing to quantify interatomic bond strength. Here we present a new set of tools to automate the calculation of COHP and analyze the results. We use the program packages VASP and LOBSTER and the Python packages atomate and pymatgen. The analysis produced by our tools includes plots, a textual description, and key data in machine-readable format. To illustrate those capabilities, we have selected simple test compounds (NaCl, GaN), the oxynitrides BaTaO2N, CaTaO2N, and SrTaO2N, and the thermoelectric material Yb14Mn1Sb11. We show correlations between bond strengths and stabilities in the oxynitrides, as well as the influence of the Mn-Sb bonds on the magnetism in Yb14Mn1Sb11. Our contribution enables high-throughput bonding analysis and will facilitate the use of bonding information for machine learning studies. Chemical bond ; Python (programming language) ; Population ; Context (archaeology) ; Materials science ; Magnetism ; Computer science ; Nanotechnology ; Chemistry ; Physics ; Condensed matter physics Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Rare-earth and actinide compounds
Authors: Caroline Desgranges | Jérôme Delhommelle
Source: Entropy (article, 2022-01-07)
Field: Materials Science — Materials Chemistry

Machine-Learned Free Energy Surfaces for Capillary Condensation and Evaporation in Mesopores Using molecular simulations, we study the processes of capillary condensation and capillary evaporation in model mesopores. To determine the phase transition pathway, as well as the corresponding free energy profile, we carry out enhanced sampling molecular simulations using entropy as a reaction coordinate to map the onset of order during the condensation process and of disorder during the evaporation process. The structural analysis shows the role played by intermediate states, characterized by the onset of capillary liquid bridges and bubbles. We also analyze the dependence of the free energy barrier on the pore width. Furthermore, we propose a method to build a machine learning model for the prediction of the free energy surfaces underlying capillary phase transition processes in mesopores. Capillary condensation ; Capillary action ; Evaporation ; Condensation ; Materials science ; Mesoporous material ; Phase transition ; Thermodynamics ; Process (computing) ; Chemical physics ; Chemistry ; Adsorption ; Computer science ; Composite material ; Physics ; Physical chemistry ; Organic chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Phase Equilibria and Thermodynamics
Authors: Kihoon Bang | Doosun Hong | Youngtae Park | Dong-Hun Kim | Sang Soo Han | Hyuck Mo Lee
Source: Research Square (preprint, 2022-10-11)
Field: Materials Science — Materials Chemistry

Machine Learning-Enabled Exploration of the Electrochemical Stability of Real-Scale Metallic Nanoparticles Scale (ratio) ; Nanoparticle ; Electrochemistry ; Materials science ; Stability (learning theory) ; Nanotechnology ; Metal ; Artificial intelligence ; Computer science ; Chemical engineering ; Metallurgy ; Machine learning ; Engineering ; Chemistry ; Physics ; Electrode Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Electrochemical Analysis and Applications
Authors: Ruizhi Zhang | Sohan Seth | James Cumby
Source: ChemRxiv (preprint, 2022-04-21)
Field: Materials Science — Materials Chemistry

Grouped representation of interatomic distances as a similarity measure for crystal structures Determining how similar two materials are in terms of both atomic composition and crystallographic structure remains a challenge, the solution of which would enable generalised machine learning using crystal structure data. We demonstrate a new method of describing crystal structures based on interatomic distances, termed the Grouped Representation of Interatomic Distances (GRID). This fast to compute descriptor can equally be applied to crystalline or disordered materials, and encodes additional information beyond pairwise distances, such as coordination environments. Combined with earth mover’s distance as a measure of similarity, we show that GRID is able to quantitatively compare materials involving both short- and long-range structural variation. Using this new material descriptor, we show that it can accurately predict bulk moduli using a simple nearest-neighbour model, and that the resulting similarity shows good generalisability across multiple materials properties. Similarity (geometry) ; Representation (politics) ; Pairwise comparison ; Measure (data warehouse) ; Crystal structure ; Interatomic potential ; Crystal (programming language) ; Computer science ; Statistical physics ; Materials science ; Crystallography ; Physics ; Artificial intelligence ; Data mining ; Chemistry ; Molecular dynamics ; Computational chemistry Machine Learning in Materials Science ; Geochemistry and Geologic Mapping ; History and advancements in chemistry
Authors: Faizan Ejaz | Leslie K Hwang | Jangyup Son | Jinsang Kim | Dong Su Lee | Beomjin Kwon
Source: Research Square (preprint, 2022-06-07)
Field: Materials Science — Materials Chemistry

Convolutional Neural Networks for Approximating Electrical and Thermal Conductivities of Cu-CNT Composites Abstract This article explores the deep learning approach towards approximating the effective electrical and thermal conductivities of copper (Cu)-carbon nanotube (CNT) composites with CNTs aligned to the field direction. Convolutional neural networks (CNN) are trained to map the two-dimensional images of stochastic Cu-CNT networks to corresponding conductivities. The CNN model learns to estimate the Cu-CNT composite conductivities for various CNT volume fractions, interfacial electrical resistances, R c = 20 Ω − 20 kΩ, and interfacial thermal resistances, R ” t,c = 10 − 10 m 2 K/W − 10 − 7 m 2 K/W. For training the CNNs, we optimize the hyperparameters such as learning rate, minibatch size, and hidden layer neurons. Without iteratively solving the physical governing equations, the trained CNN model approximates the electrical and thermal conductivities within a second with an accuracy greater than 98%, which may take longer than 100 minutes for a convectional numerical simulation. This work demonstrates the potential of the deep learning surrogate model for the complex transport processes in composite materials. Hyperparameter ; Carbon nanotube ; Convolutional neural network ; Composite number ; Thermal ; Materials science ; Thermal conductivity ; Copper ; Composite material ; Electrical resistivity and conductivity ; Deep learning ; Field (mathematics) ; Computer science ; Artificial intelligence ; Mathematics ; Physics ; Thermodynamics ; Metallurgy Thermal properties of materials ; Composite Material Mechanics ; Dielectric materials and actuators
Authors: Bojian Fan | Xuemei Liu | Shengping Si | Shuang Liu | Ruyue Xie | Jinxu Liu
Source: Journal of Physics Conference Series (article, 2022-10-01)
Field: Materials Science — Materials Chemistry

Study on the key features of dynamic compressive fracture strain of Ti-Zr-Nb solid solution alloys through random forest regressor Abstract In some specific application fields, dynamic fracture strain regarding as evaluating dynamic properties of Ti-Zr-Nb solid solution alloy have attracted extensive attention. However, the main influence factors of the dynamic strain of alloys were unclear. For the purpose of regulating Ti-Zr-Nb alloys’ dynamic plasticity and clarify main influence factors of the dynamic plasticity of the materials, powder metallurgy, dynamic properties test combined with machine learning were performed. 56 Ti-Zr-Nb alloys were prepared through powder metallurgy and their dynamic compressive fracture strain was tested. Furthermore, optimization of machine learning model and selection of key features for the prediction of dynamic compressive fracture strain were carried out. The prediction accuracy of optimized model was more than 80%, and three key features that significantly influence the dynamic fracture strain were selected and ordered as: VEC&gt;λ&gt;ΔG. Materials science ; Plasticity ; Strain (injury) ; Fracture (geology) ; Powder metallurgy ; Alloy ; Compressive strength ; Metallurgy ; Composite material ; Microstructure Titanium Alloys Microstructure and Properties ; Orthopaedic implants and arthroplasty ; Advanced materials and composites
Authors: H Suhendar | Eri Widianto
Source: Journal of Physics Conference Series (article, 2022-11-01)
Field: Materials Science — Materials Chemistry

Genetics Algorithm Feature Selection for Improving Aqueous Solubility Prediction Aqueous solubility is an important property for conducting chemical reactions of the compound. In this research, we develop several machine learning models for predicting the aqueous solubility reaction of molecules. The open public dataset, AqSolDB, was used for model development which contains 9982 data on molecule solubility. Several machine learning regression models were trained on the dataset and their performance was evaluated using mean absolute error. In this research, we use machine learning model-based tree for model development. The result showed that the best model for solubility prediction is using Categoric Boosting Regressor achieving 0.854 mean absolute error. The importance of feature that affected solubility can also be calculated from the calculation. It is shown that variable MolLogP strongly correlated with solubility reaction. To further improve our model, we selected several features using a genetics algorithm and trained selected feature using several machine learning-based tree models. It showed that the lowest mean absolute error obtained from Categoric Boosting Regressor model achieving 0.771 which provides an improvement with previous calculation without feature selection. Solubility ; Feature selection ; Boosting (machine learning) ; Gradient boosting ; Machine learning ; Artificial intelligence ; Computer science ; Algorithm ; Decision tree ; Random forest ; Chemistry ; Organic chemistry Crystallization and Solubility Studies ; Machine Learning in Materials Science ; Analytical Chemistry and Chromatography
Authors: Darren C. Pagan | Calvin R. Pash | Austin R. Benson | Matthew Kasemer
Source: arXiv (Cornell University) (preprint, 2022-05-12)
Field: Materials Science — Materials Chemistry

Graph Neural Network Modeling of Grain-scale Anisotropic Elastic Behavior using Simulated and Measured Microscale Data Here we assess the applicability of graph neural networks (GNNs) for predicting the grain-scale elastic response of polycrystalline metallic alloys. Using GNN surrogate models, grain-averaged stresses during uniaxial elastic tension in Low Solvus High Refractory (LSHR) Ni Superalloy and Ti 7wt%Al (Ti-7Al), as example face centered cubic and hexagonal closed packed alloys, are predicted. A transfer learning approach is taken in which GNN surrogate models are trained using crystal elasticity finite element method (CEFEM) simulations and then the trained surrogate models are used to predict the mechanical response of microstructures measured using high-energy X-ray diffraction microscopy (HEDM). The performance of using various microstructural and micromechanical descriptors for input nodal features to the GNNs is explored through comparisons to traditional mean-field theory predictions, reserved full-field CEFEM data, and measured far-field HEDM data. The effects of elastic anisotropy on GNN model performance and outlooks for extension of the framework are discussed. Materials science ; Anisotropy ; Microscale chemistry ; Microstructure ; Crystallite ; Solvus ; Artificial neural network ; Superalloy ; Computer science ; Artificial intelligence ; Metallurgy ; Mathematics ; Physics ; Optics Machine Learning in Materials Science ; Microstructure and mechanical properties ; Microstructure and Mechanical Properties of Steels
Authors: Guojing Cong | Anshul Gupta | Rodrigo Neumann Barros Ferreira | Maíra Gatti de Bayser | M. Steiner | Breanndán Ó Conchúir
Source: arXiv (Cornell University) (preprint, 2022-08-22)
Field: Chemistry — Inorganic Chemistry

Prediction of $\textrm{CO}_2$ Adsorption in Nano-Pores with Graph Neural Networks We investigate the graph-based convolutional neural network approach for predicting and ranking gas adsorption properties of crystalline Metal-Organic Framework (MOF) adsorbents for application in post-combustion capture of $\textrm{CO}_2$. Our model is based solely on standard structural input files containing atomistic descriptions of the adsorbent material candidates. We construct novel methodological extensions to match the prediction accuracy of classical machine learning models that were built with hundreds of features at much higher computational cost. Our approach can be more broadly applied to optimize gas capture processes at industrial scale. Adsorption ; Ranking (information retrieval) ; Artificial neural network ; Convolutional neural network ; Graph ; Computer science ; Combustion ; Construct (python library) ; Artificial intelligence ; Materials science ; Theoretical computer science ; Chemistry ; Physical chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Carbon Dioxide Capture Technologies
Authors: Yang Zhong | Hongyu Yu | Xin-Gao Gong | Hongjun Xiang
Source: arXiv (Cornell University) (preprint, 2022-01-15)
Field: Materials Science — Materials Chemistry

Edge-based Tensor prediction via graph neural networks Message-passing neural networks (MPNN) have shown extremely high efficiency and accuracy in predicting the physical properties of molecules and crystals, and are expected to become the next-generation material simulation tool after the density functional theory (DFT). However, there is currently a lack of a general MPNN framework for directly predicting the tensor properties of the crystals. In this work, a general framework for the prediction of tensor properties was proposed: the tensor property of a crystal can be decomposed into the average of the tensor contributions of all the atoms in the crystal, and the tensor contribution of each atom can be expanded as the sum of the tensor projections in the directions of the edges connecting the atoms. On this basis, the edge-based expansions of force vectors, Born effective charges (BECs), dielectric (DL) and piezoelectric (PZ) tensors were proposed. These expansions are rotationally equivariant, while the coefficients in these tensor expansions are rotationally invariant scalars which are similar to physical quantities such as formation energy and band gap. The advantage of this tensor prediction framework is that it does not require the network itself to be equivariant. Therefore, in this work, we directly designed the edge-based tensor prediction graph neural network (ETGNN) model on the basis of the invariant graph neural network to predict tensors. The validity and high precision of this tensor prediction framework were shown by the tests of ETGNN on the extended systems, random perturbed structures and JARVIS-DFT datasets. This tensor prediction framework is general for nearly all the GNNs and can achieve higher accuracy with more advanced GNNs in the future. Tensor (intrinsic definition) ; Artificial neural network ; Invariant (physics) ; Mathematics ; Topology (electrical circuits) ; Computer science ; Artificial intelligence ; Pure mathematics ; Combinatorics ; Mathematical physics Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Advanced NMR Techniques and Applications
Authors: A. I. Nikiforov | I. V. Babchuk | V. A. Vorobkalo | E. A. Chesnokov | D. L. Chistov
Source: Petroleum Chemistry (article, 2022-03-01)
Field: Materials Science — Materials Chemistry

Application of Machine Learning Methods to Predicting the Degree of Crystallinity of MFI Type Zeolites Predicting the degree of crystallinity of zeolites from the initial synthesis parameters is an extremely difficult-to-solve problem. One of the ways to find the corresponding relationships is processing of data on the zeolite synthesis by machine learning algorithms. In this study, we analyzed 650 research papers and created a database including the parameters of the synthesis of MFI type zeolites and data on the degree of crystallinity of the material obtained. Finding relationships between the initial synthesis parameters and degree of crystallinity of the zeolite formed is a regression problem. In this study, it was solved by three machine learning algorithms: decision tree, random forest, and gradient boosting. To enhance the algorithm operation accuracy, we added to the initial dataset polynomial features of degrees 2–5. The gradient boosting algorithm based on data with third-degree polynomial features showed the highest accuracy in combination with the database processing rate. The mean absolute error (MAE) of the values given by the model relative to the real degrees of crystallinity was 10.3%. Crystallinity ; Degree (music) ; Zeolite ; Gradient boosting ; Decision tree ; Boosting (machine learning) ; Algorithm ; Polynomial ; Computer science ; Machine learning ; Artificial intelligence ; Materials science ; Mathematics ; Random forest ; Composite material ; Chemistry ; Catalysis ; Physics ; Mathematical analysis ; Organic chemistry Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis ; Advanced Data Processing Techniques
Authors: HyeonSeok Shin | Young-Min Park | Kyunggeun Ahn | Sungsoo Kim
Source: Analytical Chemistry (article, 2022-05-24)
Field: Chemistry — Spectroscopy

Accurate Prediction of y Ions in Beam-Type Collision-Induced Dissociation Using Deep Learning Peptide fragmentation spectra contain critical information for the identification of peptides by mass spectrometry. In this study, we developed an algorithm that more accurately predicts the high-intensity peaks among the peptide spectra. The training data are composed of 180,833 peptides from the National Institute of Standards and Technology and Proteomics Identification database, which were fragmented by either quadrupole time-of-flight or triple-quadrupole collision-induced dissociation methods. Exploratory analysis of the peptide fragmentation pattern was focused on the highest intensity peaks that showed proline, peptide length, and a sliding window of four amino acid combination that can be exploited as key features. The amino acid sequence of each peptide and each of the key features were allocated to different layers of the model, where recurrent neural network, convolutional neural network, and fully connected neural network were used. The trained model, PrAI-frag, accurately predicts the fragmentation spectra compared to previous machine learning-based prediction algorithms. The model excels at high-intensity peak prediction, which is advantageous to selective/multiple reaction monitoring application. PrAI-frag is provided via a Web server which can be used for peptides of length 6-15. Fragmentation (computing) ; Chemistry ; Peptide ; Mass spectrometry ; Artificial neural network ; Convolutional neural network ; Tandem mass spectrometry ; Dissociation (chemistry) ; Artificial intelligence ; Ion ; Pattern recognition (psychology) ; Amino acid ; Biological system ; Computer science ; Chromatography ; Physical chemistry ; Biochemistry Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Analytical Chemistry and Chromatography
Authors: Julia Westermayr | Shayantan Chaudhuri | Andreas Jeindl | Oliver T. Hofmann | Reinhard J. Maurer
Source: arXiv (Cornell University) (preprint, 2022-02-25)
Field: Materials Science — Materials Chemistry

Long-range dispersion-inclusive machine learning potentials for structure search and optimization of hybrid organic-inorganic interfaces The computational prediction of the structure and stability of hybrid organic-inorganic interfaces provides important insights into the measurable properties of electronic thin film devices, coatings, and catalyst surfaces and plays an important role in their rational design. However, the rich diversity of molecular configurations and the important role of long-range interactions in such systems make it difficult to use machine learning (ML) potentials to facilitate structure exploration that otherwise require computationally expensive electronic structure calculations. We present an ML approach that enables fast, yet accurate, structure optimizations by combining two different types of deep neural networks trained on high-level electronic structure data. The first model is a short-ranged interatomic ML potential trained on local energies and forces, while the second is an ML model of effective atomic volumes derived from atoms-in-molecules partitioning. The latter can be used to connect short-range potentials to well-established density-dependent long-range dispersion correction methods. For two systems, specifically gold nanoclusters on diamond (110) surfaces and organic $π$-conjugated molecules on silver (111) surfaces, we train models on sparse structure relaxation data from density functional theory and show the ability of the models to deliver highly efficient structure optimizations and semi-quantitative energy predictions of adsorption structures. Nanoclusters ; Electronic structure ; Range (aeronautics) ; Density functional theory ; Relaxation (psychology) ; Materials science ; Stability (learning theory) ; Computer science ; Dispersion (optics) ; Chemical physics ; Nanotechnology ; Computational chemistry ; Chemistry ; Physics ; Machine learning ; Quantum mechanics Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Electronic and Structural Properties of Oxides
Authors: Jing Wu | E Zhou | An Huang | Hongbin Zhang | Ming Hu | Guangzhao Qin
Source: arXiv (Cornell University) (preprint, 2022-01-03)
Field: Materials Science — Materials Chemistry

Deep-potential enabled multiscale simulation of gallium nitride devices on boron arsenide cooling substrates High-efficient heat dissipation plays critical role for high-power-density electronics. Experimental synthesis of ultrahigh thermal conductivity boron arsenide (BAs, 1300 W m-1K-1) cooling substrates into the wide-bandgap semiconductor of gallium nitride (GaN) devices has been realized. However, the lack of systematic analysis on the heat transfer across the BAs-GaN interface hampers the practical applications. In this study, by constructing the accurate and high-efficient machine learning interatomic potentials, we performed multiscale simulations of the BAs-GaN heterostructures. Ultrahigh interfacial thermal conductance (ITC) of 265 MW m-2K-1 is achieved, which lies in the well-matched lattice vibrations of BAs and GaN. Moreover, the competition between grain size and boundary resistance was revealed with size increasing from 1 nm to 100 μm. Such deep-potential equipped multiscale simulations not only promote the practical applications of BAs cooling substrates in electronics, but also offer new approach for designing advanced thermal management systems. Materials science ; Gallium arsenide ; Thermal conductivity ; Boron nitride ; Gallium nitride ; Wide-bandgap semiconductor ; Optoelectronics ; Heat sink ; Heterojunction ; Semiconductor ; Electronics ; Phonon ; Interfacial thermal resistance ; Band gap ; Thermal resistance ; Engineering physics ; Heat transfer ; Nanotechnology ; Condensed matter physics ; Mechanical engineering ; Electrical engineering ; Composite material ; Physics ; Thermodynamics Thermal properties of materials ; GaN-based semiconductor devices and materials ; Heat Transfer and Optimization