jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jong Youl Choi | Pei Zhang | Kshitij Mehta | Andrew E. Blanchard | Massimiliano Lupo Pasini
Source: Research Square (preprint, 2022-08-01)
Field: Materials Science — Materials Chemistry

Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules Abstract Graph Convolutional Neural Network (GCNN) is a popular class of deep learning (DL) models in material science to predict material properties from the graph representation of molecular structures. Training an accurate and comprehensive GCNN surrogate for molecular design requires large-scale graph datasets and is usually a time-consuming process. Recent advances in GPUs and distributed computing open a path to reduce the computational cost for GCNN training effectively. However, efficient utilization of high performance computing (HPC) resources for training requires simultaneously optimizing large-scale data management and scalable stochastic batched optimization techniques. In this work, we focus on building GCNN models on HPC systems to predict material properties of millions of molecules. We use HydraGNN, our in-house library for large-scale GCNN training, leveraging distributed data parallelism in PyTorch. We use ADIOS, a high-performance data management framework for efficient storage and reading of large molecular graph data. We perform parallel training on two open-source large-scale graph datasets to build a GCNN predictor for an important quantum property known as the HOMO-LUMO gap. We measure the scalability, accuracy, and convergence of our approach on two DOE supercomputers: the Summit supercomputer at the Oak Ridge Leadership Computing Facility (OLCF) and the Perlmutter system at the National Energy Research Scientific Computing Center (NERSC). We present our experimental results with HydraGNN showing i) reduction of data loading time up to 4.2 times compared with a conventional method and ii) linear scaling performance for training up to 1,024 GPUs on both Summit and Perlmutter. Computer science ; Scalability ; Supercomputer ; Graph ; Convolutional neural network ; Semantic similarity ; Parallel computing ; Big data ; Benchmark (surveying) ; Deep learning ; Summit ; Theoretical computer science ; Artificial intelligence ; Database ; Data mining Machine Learning in Materials Science ; Advanced Graph Neural Networks ; CO2 Reduction Techniques and Catalysts
Authors: Junxian Chen | Jianhai Zhang
Source: Journal of Physics Conference Series (article, 2022-12-01)
Field: Materials Science — Materials Chemistry

Predicting Misalignments of the Material Test System through a Thick-flat Specimen by Machine Learning Abstract Material properties are often determined by conducting uniaxial tests on a variety of materials. The precision of the material testing system is critical to the reliability of the mechanical properties of the tested materials. Among the significant factors affecting the performance of the material testing system is its loading coaxiality. Since predicting misalignments involves high border nonlinearity, it is challenging to quantify misalignments. To assess the alignment deviations of a tensile test system., on a thick-flat specimen, a feed-forward neural network was employed for the first time to develop a mapped relation involving misalignments and strain distribution. Additionally, we utilized the genetic algorithm to optimize the essential hyperparameters (learning rate, batch size, and the nodes in the hidden layer) of the created deep feed-forward network. Simulation results indicated that the optimized neural network could reach an accuracy of 99.89% in the prediction of misalignments. Predicted misalignments can measure the test errors associated with the material testing system to avoid incorrect conclusions from being drawn because of systematic errors. The proposed method could coordinate the location of the uniaxial test equipment during manufacturing and assembly. Hyperparameter ; Artificial neural network ; Computer science ; Reliability (semiconductor) ; Nonlinear system ; Structural engineering ; Artificial intelligence ; Engineering Machine Learning in Materials Science ; Advanced machining processes and optimization ; Manufacturing Process and Optimization
Authors: K. Ghorbani | P. Mirchi | S. Arabha | Ali Rajabpour | Sebastian Volz
Source: arXiv (Cornell University) (preprint, 2022-12-01)
Field: Materials Science — Materials Chemistry

Lattice thermal conductivity and elastic modulus of XN4 (X=Be, Mg and Pt) 2D materials using machine learning interatomic potentials The newly synthesized BeN4 monolayer has introduced a novel group of 2D materials called nitrogen-rich 2D materials. In the present study, the anisotropic mechanical and thermal properties of three members of this group, BeN4, MgN4, and PtN4, are investigated. To this end, a machine learning-based interatomic potential (MLIP) is developed on the basis of the moment tensor potential (MTP) method and utilized in classical molecular dynamics (MD) simulation. Mechanical properties are calculated by extracting the stress-strain curve and thermal properties by non-equilibrium molecular dynamics (NEMD) method. Acquired results show the anisotropic elastic modulus and lattice thermal conductivity of these materials. Generally, elastic modulus and thermal conductivity in the armchair direction are higher than in the zigzag direction. Also, the elastic anisotropy is almost constant at every temperature for BeN4 and MgN4, while for PtN4, this parameter is decreased by increasing the temperature. The findings of this research are not only evidence of the application of machine learning in MD simulations, but also provide information on the basic anisotropic mechanical and thermal properties of these newly discovered 2D nanomaterials. Anisotropy ; Materials science ; Thermal conductivity ; Interatomic potential ; Zigzag ; Elastic modulus ; Molecular dynamics ; Thermal expansion ; Condensed matter physics ; Lattice (music) ; Thermodynamics ; Composite material ; Chemistry ; Computational chemistry ; Physics ; Optics ; Geometry ; Mathematics MXene and MAX Phase Materials ; Machine Learning in Materials Science ; Thermal properties of materials
Authors: iping suhariadi | Jaka Fajar Fatriansyah | Haya Ayu Fauziyyah | Ibnu Rais Syukran | Fernanda Hartoyo | Donanta Dhaneswara | Zainovia Lockman | Andrian Fauzi | Muhammad Syaikh Rohman
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Prediction and Optimization of Mechanical Properties of Ni-Base and Fe-Ni-Base Super Alloys Via Neural Network Approach with Alloying Composition Parameter Base (topology) ; Materials science ; Artificial neural network ; Composition (language) ; Metallurgy ; Computer science ; Artificial intelligence ; Mathematics ; Mathematical analysis Metal Alloys Wear and Properties ; Powder Metallurgy Techniques and Materials ; Metal and Thin Film Mechanics
Authors: Jeong Min Kim | Taejun Ha | Joonho Lee | Young‐Su Lee | Jae-Hyeok Shim
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Prediction of Pressure-Composition-Temperature Curves of AB &lt;sub&gt;2&lt;/sub&gt;-Type Hydrogen Storage Alloys by Machine Learning Artificial neural network ; Hydrogen storage ; Desorption ; Type (biology) ; Thermodynamics ; Hydrogen ; Materials science ; Algorithm ; Artificial intelligence ; Analytical Chemistry (journal) ; Alloy ; Mathematics ; Chemistry ; Computer science ; Metallurgy ; Physics ; Physical chemistry Nuclear Materials and Properties ; Material Properties and Failure Mechanisms ; Thermodynamic and Structural Properties of Metals and Alloys
Authors: Zhisen Meng
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2022-09-01)
Field: Chemistry — Spectroscopy

Machine-learning identified molecular fragments of polycyclic aromatic hydrocarbons responsible for interstellar infrared emission bands This is the supporting information for the article "Machine-learning identified molecular fragments of polycyclic aromatic hydrocarbons responsible for interstellar infrared emission bands". It contains: In Supporting_Information_For_Molecular_Fragment_Table.pdf: Table: Molecular fragments responsible for infrared emission features of polycyclic aromatic hydrocarbons. In Supporting_Information_For_Example_Code.pdf: An explanation of the file example_code In example_code: An example code of machine learning model and corresponding input files. Infrared ; Polycyclic aromatic hydrocarbon ; Photochemistry ; Interstellar medium ; Chemistry ; Astrophysics ; Physics ; Environmental chemistry ; Astronomy ; Galaxy Molecular Spectroscopy and Structure ; Chemical Thermodynamics and Molecular Structure ; Computational Drug Discovery Methods
Authors: Eduardo A. Barros de Moraes | Marta D’Elia | Mohsen Zayernouri
Source: arXiv (Cornell University) (preprint, 2022-05-11)
Field: Materials Science — Materials Chemistry

Nonlocal Machine Learning of Micro-Structural Defect Evolutions in Crystalline Materials The presence and evolution of defects that appear in the manufacturing process play a vital role in the failure mechanisms of engineering materials. In particular, the collective behavior of dislocation dynamics at the mesoscale leads to avalanche, strain bursts, intermittent energy spikes, and nonlocal interactions producing anomalous features across different time- and length-scales, directly affecting plasticity, void and crack nucleation. Discrete Dislocation Dynamics (DDD) simulations are often used at the meso-level, but the cost and complexity increase dramatically with simulation time. To further understand how the anomalous features propagate to the continuum, we develop a probabilistic model for dislocation motion constructed from the position statistics obtained from DDD simulations. We obtain the continuous limit of discrete dislocation dynamics through a Probability Density Function for the dislocation motion, and propose a nonlocal transport model for the PDF. We develop a machine-learning framework to learn the parameters of the nonlocal operator with a power-law kernel, connecting the anomalous nature of DDD to the origin of its corresponding nonlocal operator at the continuum, facilitating the integration of dislocation dynamics into multi-scale, long-time material failure simulations. Statistical physics ; Dislocation ; Probabilistic logic ; Mesoscale meteorology ; Nucleation ; Plasticity ; Mechanics ; Classical mechanics ; Computer science ; Physics ; Artificial intelligence ; Condensed matter physics Machine Learning in Materials Science ; Microstructure and mechanical properties ; Force Microscopy Techniques and Applications
Authors: Xiaowei Li | Jinhui Wang | Jiaxuan Ma | Ting Yang | Shuai Yuan | Xiaoyu Liu | Yunduo Feng | Peipeng Jin
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Biomaterials

Thermal Deformation Behavior of Mg–3sn–1mn Mg Alloy Based on Constitutive Relation Model and Artificial Neural Network Materials science ; Constitutive equation ; Deformation (meteorology) ; Alloy ; Artificial neural network ; Metallurgy ; Thermodynamics ; Composite material ; Artificial intelligence ; Computer science ; Physics ; Finite element method Magnesium Alloys: Properties and Applications ; Metal and Thin Film Mechanics ; Metallurgy and Material Forming
Authors: R. Tranås | E.D. Sødahl | J.M. Carrete | G. Madsen | K. Berland
Source: Acta Crystallographica Section A Foundations and Advances (article, 2022-08-23)
Field: Materials Science — Materials Chemistry

Predictive modelling of order–disorder phase transitions in hybrid organic materials with machine learning force fields The caloric and ferroic properties of hybrid-organic materials make them attractive environmental-friendly materials for a range of technological applications, including actuators, sensors, energy harvesting, and solid-state refrigeration.However, progress in the development of such materials is hampered by the huge computational costs of ab-initio molecular dynamics modelling at finite temperatures which is required for predictive modelling of dynamical properties including phase transition temperatures.However, recent advances in machine-learning force fields (ML-FF) hold a promise of providing first-principles accuracy of dynamical properties at a fraction of the computational costs while retaining high accuracy.In this case study, we use the recently developed NeuralIL [1] ML-FF software to provide microscopic insight into the barocaloric (CH3)2NH2Mg(HCOO)3 system [2].The simulations reveal the important role of methyl side group rotations in the vicinity of the phase transitions.The predictive phase transition temperature is also in close agreement with the experimental predictions, highlighting the potential of the method. Materials science ; Order (exchange) ; Phase transition ; Phase (matter) ; Computer science ; Condensed matter physics ; Physics ; Business ; Quantum mechanics Machine Learning in Materials Science
Authors: Xiaowei Li | Jinhui Wang | Jiaxuan Ma | Ting Yang | Shuai Yuan | Xiaoyu Liu | Yunduo Feng | Peipeng Jin
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Biomaterials

Thermal Deformation Behavior of Mg–3sn–1mn Mg Alloy Based on Constitutive Relation Model and Artificial Neural Network Constitutive equation ; Materials science ; Artificial neural network ; Alloy ; Deformation (meteorology) ; Metallurgy ; Composite material ; Structural engineering ; Finite element method ; Computer science ; Artificial intelligence ; Engineering Magnesium Alloys: Properties and Applications ; Metal and Thin Film Mechanics ; Metallurgy and Material Forming
Authors: Magnus Röding | Piotr Tomaszewski | Shun Yu | Markus Borg | Jerk Rönnols
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2022-09-14)
Field: Materials Science — Materials Chemistry

Machine learning-accelerated small-angle X-ray scattering analysis of disordered two- and three-phase materials Dataset and code used in M Röding, et al, "Machine learning-accelerated small-angle X-ray scattering analysis of disordered two- and three-phase materials", published in Frontiers in Materials. In this work, we develop a machine learning-based framework for prediction of material parameters from small-angle X-ray scattering (SAXS) data. The method is trained using data from a Gaussian random field-based model for the electron density of the material and a very fast Fourier transform-based numerical method for simulating realistic SAXS measurements. The prediction is performed using regression with XGBoost. Herein, the codes in Matlab and Python/XGBoost necessary to investigate the prediction models and reproduce the results of the paper are supplied. Also, the dataset and the trained XGBoost models are supplied. Scattering ; Small-angle X-ray scattering ; Phase (matter) ; Materials science ; X-ray ; Optics ; Physics ; Quantum mechanics X-ray Diffraction in Crystallography ; Machine Learning in Materials Science
Authors: Mito, Masaki | Mokutani, Narimichi | Tsuji, Hiroki | Tang, Yongpeng | Matsumoto, Kaname | Murayama, Mitsuhiro | Horita, Zenji
Source: Institutional Repositories DataBase (IRDB) (article, 2022-03-11)
Field: Materials Science — Materials Chemistry

Achieving Superconductivity with Higher Tc in Lightweight Al-Ti-Mg Alloys: Prediction using Machine Learning and Synthesis via High-Pressure Torsion Process Aluminum (Al) and titanium (Ti) are superconducting materials but their superconducting transition temperatures (Tc) are quite low as 1.20 and 0.39 K, respectively, while magnesium (Mg) never exhibits superconductivity. In this study, we explored new superconductors with higher Tc in the Al–Mg–Ti ternary system, along with the prediction using machine learning. High-pressure torsion (HPT) is utilized to produce the superconducting states. While performing AC magnetization measurements, we found, for the first time, superconducting states with Tc=4.0 and 7.3 K for a composition of Al:Ti = 1:2. The magnetic anomalies appeared more sharply when the sample was processed by HPT at 573 K than at room temperature, and the anomalies exhibited DC magnetic field dependence characteristic of superconductivity. Magnetic anomalies also appeared at ∼55 and ∼93 K, being supported by the prediction using the machine learning for the Al–Ti–O system, and this suggests that Al–Ti oxides play an important role in the advent of such anomalies but that the addition of Mg could be less effective. Superconductivity ; Magnetization ; Ternary operation ; Torsion (gastropod) ; Magnetic field ; Titanium alloy ; Transition temperature Machine Learning in Materials Science ; Superconductivity in MgB2 and Alloys ; Metallurgy and Material Forming
Authors: Thomas Linker | Shogo Fukushima | Rajiv K. Kalia | Aravind Krishnamoorthy | Aiichiro Nakano | Ken‐ichi Nomura | Kohei Shimamura | Fuyuki Shimojo | Priya Vashishta
Source: Frontiers in Nanotechnology (article, 2022-08-30)
Field: Materials Science — Materials Chemistry

Towards computational polar-topotronics: Multiscale neural-network quantum molecular dynamics simulations of polar vortex states in SrTiO3/PbTiO3 nanowires Recent discoveries of polar topological structures ( e.g ., skyrmions and merons) in ferroelectric/paraelectric heterostructures have opened a new field of polar topotronics. However, how complex interplay of photoexcitation, electric field and mechanical strain controls these topological structures remains elusive. To address this challenge, we have developed a computational approach at the nexus of machine learning and first-principles simulations. Our multiscale neural-network quantum molecular dynamics molecular mechanics approach achieves orders-of-magnitude faster computation, while maintaining quantum-mechanical accuracy for atoms within the region of interest. This approach has enabled us to investigate the dynamics of vortex states formed in PbTiO 3 nanowires embedded in SrTiO 3 . We find topological switching of these vortex states to topologically trivial, uniformly polarized states using electric field and trivial domain-wall states using shear strain. These results, along with our earlier results on optical control of polar topology, suggest an exciting new avenue toward opto-electro-mechanical control of ultrafast, ultralow-power polar topotronic devices. Nanowire ; Electric field ; Topology (electrical circuits) ; Topological defect ; Quantum ; Polar ; Vortex ; Physics ; Condensed matter physics ; Materials science ; Quantum mechanics ; Mathematics Electronic and Structural Properties of Oxides ; Multiferroics and related materials ; Ferroelectric and Piezoelectric Materials
Authors: Samantha Durdy | Michael W. Gaultois | Vladimir V. Gusev | Danushka Bollegala | Matthew J. Rosseinsky
Source: arXiv (Cornell University) (preprint, 2022-06-17)
Field: Materials Science — Materials Chemistry

Random projections and Kernelised Leave One Cluster Out Cross-Validation: Universal baselines and evaluation tools for supervised machine learning for materials properties With machine learning being a popular topic in current computational materials science literature, creating representations for compounds has become common place. These representations are rarely compared, as evaluating their performance - and the performance of the algorithms that they are used with - is non-trivial. With many materials datasets containing bias and skew caused by the research process, leave one cluster out cross validation (LOCO-CV) has been introduced as a way of measuring the performance of an algorithm in predicting previously unseen groups of materials. This raises the question of the impact, and control, of the range of cluster sizes on the LOCO-CV measurement outcomes. We present a thorough comparison between composition-based representations, and investigate how kernel approximation functions can be used to better separate data to enhance LOCO-CV applications. We find that domain knowledge does not improve machine learning performance in most tasks tested, with band gap prediction being the notable exception. We also find that the radial basis function improves the linear separability of chemical datasets in all 10 datasets tested and provide a framework for the application of this function in the LOCO-CV process to improve the outcome of LOCO-CV measurements regardless of machine learning algorithm, choice of metric, and choice of compound representation. We recommend kernelised LOCO-CV as a training paradigm for those looking to measure the extrapolatory power of an algorithm on materials data. Computer science ; Machine learning ; Artificial intelligence ; Metric (unit) ; Kernel (algebra) ; Process (computing) ; Range (aeronautics) ; Predictive power ; Skew ; Function (biology) ; Cluster (spacecraft) ; Representation (politics) ; Data mining ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Rémi Pétuya | Abhishek Punase | Emanuele Bosoni | Antonio Pedro de Oliveira Filho | Juan Sarria | Nirumpam Purkayastha | Jonathan Wylde | Stephan Mohr
Source: ChemRxiv (preprint, 2022-11-02)
Field: Chemistry — Analytical Chemistry

Molecular dynamics simulations of asphaltene aggregation: machine learning identification of representative molecules, polydispersity and inhibitor performance Molecular Dynamics simulations have been employed to investigate the effect of polydispersity on the aggregation of asphaltene. To make the large combinatorial space of possible asphaltene blends accessible to a systematic study via simulation, an upfront unsupervised machine learning approach (clustering) was employed to identify a reduced set of model molecules representative of the diversity of asphaltene. For these molecules, monodisperse asphaltene simulations have shown a broad range of aggregation behavior, driven by their structural features: size of the aromatic core, length of the aliphatic chains and presence of heteroatoms. Then, the combination of these model molecules in a series of polydisperse mixtures have highlighted the complex and diverse effects of polydispersity on the aggregation process of asphaltene, which yielded both antagonistic, synergistic and seed effects. These findings illustrate the necessity of accounting for polydispersity when studying the asphaltene aggregation process and have permitted to establish a robust protocol for the in-silico evaluation of the performance of asphaltene inhibitors, as illustrated for the case of a nonylphenol resin. Asphaltene ; Dispersity ; Molecular dynamics ; Molecule ; Chemistry ; Chemical physics ; Materials science ; Computational chemistry ; Organic chemistry Petroleum Processing and Analysis ; Analytical Chemistry and Chromatography ; Surfactants and Colloidal Systems
Authors: Shampa Bhattacharyya | Gourisankar Roymahapatra
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2022-08-31)
Field: Chemistry — Physical and Theoretical Chemistry

Application of Machine Learning (ML); a tool for teaching and future research in undergraduate chemistry laboratory experiment 'Extent of miscibility of the Phenol-Water system' Department of Chemistry, Hansraj College, University of Delhi, Delhi-110 007, India Department of Applied Sciences, Haldia Institute of Technology, Haldia-721 657, India E-mail: shampa@hrc.du.ac.in This paper demonstrates how machine learning methods can replace conventional approaches to fitting data measured in chemistry experiments, especially those which are conducted at the undergraduate level. As a case study, raw data collected from a Phenol-water experiment is used to outline the methodology needed to arrive at the best fit curve. Code snippets are provided and explained in detail and metrics from different models are tabulated and compared. The model with the best performing metrics is then chosen as the representative fit. Miscibility ; Phenol ; Engineering ; Chemistry ; Mathematics education ; Organic chemistry ; Mathematics ; Polymer Various Chemistry Research Topics ; Advanced Data Processing Techniques ; Advanced Chemical Sensor Technologies