jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Luis Casillas‐Trujillo | Abhijith S. Parackal | Rickard Armiento | Björn Alling
Source: arXiv (Cornell University) (preprint, 2024-06-25)
Field: Materials Science — Materials Chemistry

Evaluating and improving the predictive accuracy of mixing enthalpies and volumes in disordered alloys from universal pre-trained machine learning potentials The advent of machine learning in materials science opens the way for exciting and ambitious simulations of large systems and long time scales with the accuracy of ab-initio calculations. Recently, several pre-trained universal machine learned interatomic potentials (UPMLIPs) have been published, i.e., potentials distributed with a single set of weights trained to target systems across a very wide range of chemistries and atomic arrangements. These potentials raise the hope of reducing the computational cost and methodological complexity of performing simulations compared to models that require for-purpose training. However, the application of these models needs critical evaluation to assess their usability across material types and properties. In this work, we investigate the application of the following UPMLIPs: MACE, CHGNET, and M3GNET to the context of alloy theory. We calculate the mixing enthalpies and volumes of 21 binary alloy systems and compare the results with DFT calculations to assess the performance of these potentials over different properties and types of materials. We find that small relative energies necessary to correctly predict mixing energies are generally not reproduced by these methods with sufficient accuracy to describe correct mixing behaviors. However, the performance can be significantly improved by supplementing the training data with relevant training data. The potentials can also be used to partially accelerate these calculations by replacing the ab-initio structural relaxation step. Mixing (physics) ; Materials science ; Machine learning ; Thermodynamics ; Artificial intelligence ; Computer science ; Physics ; Quantum mechanics Machine Learning in Materials Science
Authors: John C. Steuben | A. B. Geltmacher | Sabrina N Rodriguez | Andrew J. Birnbaum | Benjamin Graber | Anna K. Rawlings | Athanasios Iliopoulos | John G. Michopoulos
Source: Journal of Computing and Information Science in Engineering (article, 2024-03-01)
Field: Materials Science — Materials Chemistry

Machine-Learning Metacomputing for Materials Science Data Abstract Materials science requires the collection and analysis of great quantities of data. These data almost invariably require various post-acquisition computation to remove noise, classify observations, fit parametric models, or perform other operations. Recently developed machine-learning (ML) algorithms have demonstrated great capability for performing many of these operations, and often produce higher quality output than traditional methods. However, it has been widely observed that such algorithms often suffer from issues such as limited generalizability and the tendency to “over fit” to the input data. In order to address such issues, this work introduces a metacomputing framework capable of systematically selecting, tuning, and training the best available machine-learning model in order to process an input dataset. In addition, a unique “cross-training” methodology is used to incorporate underlying physics or multiphysics relationships into the structure of the resultant ML model. This metacomputing approach is demonstrated on four example problems: repairing “gaps” in a multiphysics dataset, improving the output of electron back-scatter detection crystallographic measurements, removing spurious artifacts from X-ray microtomography data, and identifying material constitutive relationships from tensile test data. The performance of the metacomputing framework on these disparate problems is discussed, as are future plans for further deploying metacomputing technologies in the context of materials science and mechanical engineering. Metacomputing ; Computer science ; Engineering drawing ; Human–computer interaction ; Computer graphics (images) ; Engineering ; Artificial intelligence Machine Learning in Materials Science ; Machine Learning and Algorithms ; Scientific Computing and Data Management
Authors: Zhen Fan | Shuai Lian | Guangyong Jin | Chao Xin | Ye Li | Boshi Yuan
Source: CrystEngComm (article, 2024-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Predictive nonlinear optical crystal formation energy regression model based on convolutional neural networks A convolutional neural network (CNN) model has been constructed to predict the formation energy of nonlinear optical crystals solely based on their chemical formulas. Convolutional neural network ; Nonlinear system ; Energy (signal processing) ; Nonlinear regression ; Artificial neural network ; Crystal (programming language) ; Regression ; Biological system ; Computer science ; Nonlinear optical ; Materials science ; Regression analysis ; Artificial intelligence ; Pattern recognition (psychology) ; Machine learning ; Mathematics ; Physics ; Statistics ; Biology Crystal Structures and Properties ; X-ray Diffraction in Crystallography ; Nonlinear Optical Materials Research
Authors: Tomohiro Fukushima | Masaki Itatani | Kei Murakoshi
Source: ECS Meeting Abstracts (article, 2024-11-22)
Field: Chemistry — Electrochemistry

(Invited) Evaluation of Oxygen Evolution Reaction Electrodes through Machine-Learning Analysis and in-Situ Electrochemical Spectroscopy To enhance the catalysis of the oxygen evolution reaction (OER) at water electrolysis electrodes, it is imperative to eliminate the energy-consuming processes involved in multiple electron and proton transfer reactions. We propose employing a combination of machine-learning analysis of electrochemical activity and in-situ electrochemical Raman observation for the OER electrodes. The OER behavior can be categorized based on the contributions of Ni-OH and Ni-OOH, which serve as key OER intermediates. This information is utilized to elucidate the OER behavior through energetic and intermediate analyses. The presented study provides the direction for designing active OER catalysis in the future. In situ ; Electrochemistry ; Electrode ; Oxygen evolution ; Spectroscopy ; Materials science ; Environmental science ; Analytical Chemistry (journal) ; Chemical engineering ; Computer science ; Chemistry ; Engineering ; Environmental chemistry ; Physics ; Physical chemistry Electrochemical Analysis and Applications ; Fuel Cells and Related Materials
Authors: Takeru Miyagawa | Namita Krishnan | Manuel Grumet | Christian Reverón Baecker | Waldemar Kaiser | David A. Egger
Source: arXiv (Cornell University) (preprint, 2024-01-20)
Field: Materials Science — Materials Chemistry

Accurate Description of Ion Migration in Solid-State Ion Conductors from Machine-Learning Molecular Dynamics Solid-state ion conductors (SSICs) have emerged as a promising material class for electrochemical storage devices and novel compounds of this kind are continuously being discovered. High-throughout approaches that enable a rapid screening among the plethora of candidate SSIC compounds have been essential in this quest. While first-principles methods are routinely exploited in this context to provide atomic-level details on ion migration mechanisms, dynamic calculations of this type are computationally expensive and limit us in the time- and length-scales accessible during the simulations. Here, we explore the potential of recently developed machine-learning force fields for predicting different ion migration mechanisms in SSICs. Specifically, we systematically investigate three classes of SSICs that all exhibit complex ion dynamics including vibrational anharmonicities: AgI, a strongly disordered Ag$^+$ conductor; Na$_3$SbS$_4$, a Na$^+$ vacancy conductor; and Li$_{10}$GeP$_2$S$_{12}$, which features concerted Li$^+$ migration. Through systematic comparison with \textit{ab initio} molecular dynamics data, we demonstrate that machine-learning molecular dynamics provides very accurate predictions of the structural and vibrational properties including the complex anharmonic dynamics in these SSICs. The \textit{ab initio} accuracy of machine-learning molecular dynamics simulations at relatively low computational cost open a promising path toward the rapid design of novel SSICs. Molecular dynamics ; Context (archaeology) ; Ion ; Ab initio ; Anharmonicity ; Conductor ; Chemical physics ; Computer science ; Statistical physics ; Physics ; Materials science ; Nanotechnology ; Chemistry ; Computational chemistry ; Quantum mechanics Machine Learning in Materials Science ; Advanced Battery Materials and Technologies
Authors: David Montes de Oca Zapiain | Aditya Venkatraman | Mark Wilson
Source: Research Square (preprint, 2024-09-13)
Field: Materials Science — Materials Chemistry

Accelerating Charge Estimation in Molecular Dynamics Simulations Using Physics-Informed Neural Networks: Corrosion Applications Molecular dynamics ; Partial charge ; Statistical physics ; Computer science ; Context (archaeology) ; Charge density ; Electronegativity ; Charge (physics) ; Chemical physics ; Materials science ; Physics ; Chemistry ; Computational chemistry ; Quantum mechanics Machine Learning in Materials Science ; Corrosion Behavior and Inhibition ; Fuel Cells and Related Materials
Authors: Pavlo Golub | Chao Yang | Vojtěch Vlček | Libor Veis
Source: arXiv (Cornell University) (preprint, 2024-12-10)
Field: Chemistry — Spectroscopy

Quantum Chemical Density Matrix Renormalization Group Method Boosted by Machine Learning Accurate electronic structure calculations are essential in modern materials science, but strongly correlated systems pose a significant challenge due to their computational cost. Traditional methods, such as complete active space self-consistent field (CASSCF), scale exponentially with system size, while alternative methods like the density matrix renormalization group (DMRG) scale more favorably, yet remain limited for large systems. In this work, we demonstrate how a simple machine learning model can enhance quantum chemical DMRG calculations, improving their accuracy to chemical precision, even for systems that would otherwise require considerably higher computational resources. The systems under study are polycyclic aromatic hydrocarbons, which are typical candidates for DMRG calculations and are highly relevant for advanced technological applications. Density matrix renormalization group ; Quantum chemical ; Renormalization group ; Matrix (chemical analysis) ; Group (periodic table) ; Quantum ; Physics ; Mathematics ; Quantum mechanics ; Chemistry ; Chromatography ; Molecule Molecular spectroscopy and chirality ; Machine Learning in Materials Science ; Complex Network Analysis Techniques
Authors: Yongliang Ou | Yuji Ikeda | Lena Scholz | Sergiy V. Divinski | Felix Fritzen | Blazej Grabowski
Source: arXiv (Cornell University) (preprint, 2024-07-04)
Field: Materials Science — Materials Chemistry

Atomistic modeling of bulk and grain boundary diffusion in solid electrolyte Li$_6$PS$_5$Cl using machine-learning interatomic potentials Li$_6$PS$_5$Cl is a promising candidate for the solid electrolyte in all-solid-state Li-ion batteries. In applications, this material is in a polycrystalline state with grain boundaries (GBs) that can affect ionic conductivity. While atomistic modeling provides valuable information on the impact of GBs on Li diffusion, such studies face either high computational cost (\textit{ab initio} methods) or accuracy limitations (classical potentials) as challenges. Here, we develop a quality-level-based active learning scheme for efficient and systematic development of \textit{ab initio}-based machine-learning interatomic potentials, specifically moment tensor potentials (MTPs), for large-scale, long-time, and high-accuracy simulations of complex atomic structures and diffusion mechanisms as encountered in solid electrolytes. Based on this scheme, we obtain MTPs for Li$_6$PS$_5$Cl and investigate two tilt GBs, $\Sigma3(1\bar{1}2)[110]$, $\Sigma3(\bar{1}11)[110]$, and one twist GB, $\Sigma5(001)[001]$. All three GBs exhibit low formation energies of less than \SI{20}{meV/\angstrom\textsuperscript{2}}, indicating their high stability in polycrystalline Li$_6$PS$_5$Cl. Using the MTPs, diffusion coefficients of the anion-ordered and anion-disordered bulk, as well as the three GBs, are obtained from molecular dynamics simulations of atomistic models. At \SI{300}{\kelvin}, the GB diffusion coefficients fall between the ones of the anion-ordered bulk structure (\SI{0.012e-7}{cm^2/s}, corresponding ionic conductivity about \SI{0.2}{mS/cm}) and the anion-disordered bulk structure (\SI{50}{\percent} Cl/S-anion disorder; \SI{2.203e-7}{cm^2/s}, about \SI{29.8}{mS/cm}) of Li$_6$PS$_5$Cl. Experimental data fall between the Arrhenius-extrapolated diffusion coefficients of the investigated atomic structures. Grain boundary ; Electrolyte ; Diffusion ; Materials science ; Grain boundary diffusion coefficient ; Chemical physics ; Boundary (topology) ; Interatomic potential ; Thermodynamics ; Physical chemistry ; Molecular dynamics ; Chemistry ; Computational chemistry ; Composite material ; Physics ; Microstructure ; Mathematics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Advanced Battery Materials and Technologies
Authors: Qinxi Fan | Yewei Ding | Yutong Song | Haomin Wu | Yuanhui Ji
Source: Scientia Sinica Chimica (article, 2024-09-19)
Field: Materials Science — Materials Chemistry

Model study and machine learning prediction of phase behavior of drug/polymer complex systems under different relative humidity <p indent="0mm">To rationally design, prepare, and store pharmacological formulations, it is crucial to understand the phase behavior of complicated drug/polymer complexes under varying relative humidity conditions. By creating thermodynamic models of vapor-liquid and liquid-solid multiphase equilibria and Gordon-Taylor equation systems, the thermodynamic phase behavior and glass transition temperature of drug/polymer at different relative humidity (RH = 0%, 60%, and 75%) were studied in this paper. Combining five distinct machine learning algorithms with the perturbed-chain statistical associating fluid theory model parameters and molecular structure descriptors creates a novel approach for predicting the phase diagram of drug/polymer complex systems under different RH. The results demonstrate that the random forest algorithm has the highest prediction accuracy among machine learning techniques combined with thermodynamic model parameters and molecular structure descriptors. The determination coefficients <italic>R</italic><sup>2</sup> of the test set results of drug solubility in polymer and glass transition temperature curves at different RH were 0.970 and 0.992, respectively. The study further shows that the machine learning algorithm coupled mechanism model parameters and molecular structure descriptors can achieve accurate prediction of drug/polymer phase diagrams under different RH, which is expected to provide effective guidance for rational design of drug preparations, preparation and storage conditions screening. Computer science Machine Learning in Materials Science ; Hydrogels: synthesis, properties, applications ; Block Copolymer Self-Assembly
Authors: Zhao Hao-feng | Jun Xia | Ru Wang | Yiheng He
Source: Journal of Physics Conference Series (article, 2024-02-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Application of Neural Network Prediction in the Optimization of Forming Process of Magnetic Ceramic/Rare Earth Alloy Composite Magnets Abstract In order to ensure that magnet materials have higher mechanical properties and magnetic properties at the same time, magnetic ceramics and rare earth alloys are combined to form composite magnets by bonding and pressing. The nonlinear relationship from input (pressing pressure and holding time) to output (coercivity, density and compressive strength) was established by neural network platform, and the optimization of pressing process was studied. The results show that the density, compressive strength and coercivity of the material increase with increasing pressure. When the pressing pressure exceeds a certain value, the density and compressive strength of the material increase slowly, but the coercivity of the material will decrease. The reason for the decrease of coercivity is related to the crushing of the fast quenched rare earth alloy powders in the form of scales due to excessive pressure and long holding time. The experimental results show that the optimum process parameters with high compressive strength and high coercivity can be found by the prediction of neural network. Rare earth ; Ceramic ; Magnet ; Alloy ; Artificial neural network ; Composite number ; Materials science ; Process (computing) ; Mechanical engineering ; Composite material ; Process engineering ; Metallurgy ; Computer science ; Engineering ; Artificial intelligence Magnetic Properties of Alloys ; Magnetic Properties and Applications ; Electric Power Systems and Control
Authors: Yankai Dai | Zhiwei Zhang | Dong Wang | Tianliang Li | Yuze Ren | Jingqi Chen | Lingyan Feng
Source: Advanced Materials (article, 2024-01-01)
Field: Materials Science — Biomaterials

Machine‐Learning‐Driven G‐Quartet‐Based Circularly Polarized Luminescence Materials (Adv. Mater. 4/2024) Machine Learning In article number 2310455, Lingyan Feng and co-workers pioneer the application of machine learning (ML) models to guide the synthesis of circularly polarized luminescence (CPL) gels characterized by a high dissymmetry factor (glum) and diverse chiral regulation methods. The relationship between synthesis parameters and the glum value is clarified using ML models, successfully forecasting and producing G-quartet-based CPL materials with a maximum glum value of 0.15. Materials science ; Luminescence ; Optoelectronics ; Nanotechnology ; Optics ; Physics Supramolecular Self-Assembly in Materials ; Polydiacetylene-based materials and applications ; Synthesis and Properties of Aromatic Compounds
Authors: Zhiyuan Chen | Daoyuan Yang | Xianghui Li | Jinfeng Li | Huiyu Yuan | Junyan Cui
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning-Assisted High-Entropy Optimization of Mechanical Properties in Spinel Refractory Materials Spinel ; Refractory (planetary science) ; Materials science ; High entropy alloys ; Entropy (arrow of time) ; Computer science ; Artificial intelligence ; Metallurgy ; Thermodynamics ; Physics ; Microstructure Machine Learning in Materials Science ; Advanced X-ray and CT Imaging ; Iron and Steelmaking Processes
Authors: Xiang‐Long Peng | Mozhdeh Fathidoost | Binbin Lin | Yangyiwei Yang | Bai‐Xiang Xu
Source: arXiv (Cornell University) (preprint, 2024-05-28)
Field: Materials Science — Materials Chemistry

What can machine learning help with microstructure-informed materials modeling and design? Machine learning techniques have been widely employed as effective tools in addressing various engineering challenges in recent years, particularly for the challenging task of microstructure-informed materials modeling. This work provides a comprehensive review of the current machine learning-assisted and data-driven advancements in this field, including microstructure characterization and reconstruction, multiscale simulation, correlations among process, microstructure, and properties, as well as microstructure optimization and inverse design. It outlines the achievements of existing research through best practices and suggests potential avenues for future investigations. Moreover, it prepares the readers with educative instructions of basic knowledge and an overview on machine learning, microstructure descriptors and machine learning-assisted material modeling, lowering the interdisciplinary hurdles. It should help to stimulate and attract more research attention to the rapidly growing field of machine learning-based modeling and design of microstructured materials. Microstructure ; Computer science ; Artificial intelligence ; Materials science ; Metallurgy Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Microstructure and mechanical properties
Authors: Daisuke Kuroshima | Michael Kilgour | Mark E. Tuckerman | Jutta Rogal
Source: arXiv (Cornell University) (preprint, 2024-03-29)
Field: Chemistry — Physical and Theoretical Chemistry

Machine learning classification of local environments in molecular crystals Identifying local structural motifs and packing patterns of molecular solids is a challenging task for both simulation and experiment. We demonstrate two novel approaches to characterize local environments in different polymorphs of molecular crystals using learning models that employ either flexibly learned or handcrafted molecular representations. In the first case, we follow our earlier work on graph learning in molecular crystals, deploying an atomistic graph convolutional network, combined with molecule-wise aggregation, to enable per-molecule environmental classification. For the second model, we develop a new set of descriptors based on symmetry functions combined with a point-vector representation of the molecules, encoding information about the positions as well as relative orientations of the molecule. We demonstrate very high classification accuracy for both approaches on urea and nicotinamide crystal polymorphs, and practical applications to the analysis of dynamical trajectory data for nanocrystals and solid-solid interfaces. Both architectures are applicable to a wide range of molecules and diverse topologies, providing an essential step in the exploration of complex condensed matter phenomena. Computer science ; Artificial intelligence Various Chemistry Research Topics
Authors: Stefaan S. P. Hessmann | Kristof T. Schütt | Niklas W. A. Gebauer | Michael Gastegger | Tamio Oguchi | Tomoki Yamashita
Source: arXiv (Cornell University) (preprint, 2024-08-07)
Field: Materials Science — Materials Chemistry

Accelerating crystal structure search through active learning with neural networks for rapid relaxations Global optimization of crystal compositions is a significant yet computationally intensive method to identify stable structures within chemical space. The specific physical properties linked to a three-dimensional atomic arrangement make this an essential task in the development of new materials. We present a method that efficiently uses active learning of neural network force fields for structure relaxation, minimizing the required number of steps in the process. This is achieved by neural network force fields equipped with uncertainty estimation, which iteratively guide a pool of randomly generated candidates towards their respective local minima. Using this approach, we are able to effectively identify the most promising candidates for further evaluation using density functional theory (DFT). Our method not only reliably reduces computational costs by up to two orders of magnitude across the benchmark systems Si16 , Na8Cl8 , Ga8As8 and Al4O6 , but also excels in finding the most stable minimum for the unseen, more complex systems Si46 and Al16O24 . Moreover, we demonstrate at the example of Si16 that our method can find multiple relevant local minima while only adding minor computational effort. Artificial neural network ; Computer science ; Active learning (machine learning) ; Artificial intelligence ; Machine learning X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Crystallization and Solubility Studies