jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Arti Kashyap
Source: Applied Physics Letters (article, 2024-11-25)
Field: Materials Science — Materials Chemistry

Density functional theory and material databases in the era of machine learning This perspective article presents the density functional theory and traces its evolution. With the advancement in density functional theory-based computations and the efforts to collate the data generated through density functional theory, the field now has a good repository/database of materials and their properties. This repository, though not as substantial as generally used for machine learning, has nonetheless made it possible to combine density functional theory and machine learning. This article highlights current research challenges and presents an optimistic outlook for the future of “Density Functional Theory with Machine Learning” by discussing some specific examples. Density functional theory ; Database ; Computer science ; Artificial intelligence ; Information retrieval ; Materials science ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Daisuke Kuroshima | Michael Kilgour | Mark E. Tuckerman | Jutta Rogal
Source: Journal of Chemical Theory and Computation (article, 2024-07-03)
Field: Materials Science — Materials 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 and 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 ; Data science ; Machine learning ; Human–computer interaction Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Tingting Liang | Yuan Yuan | Tao Chen | Jun Wang | Ligang Zhang | Liang Wu | Yangyang Wang | Aitao Tang | Xianhua Chen | Fusheng Pan
Source: Materials Today Communications (article, 2024-08-01)
Field: Materials Science — Biomaterials

Electrical conductivity of binary magnesium alloys: Integrating first-principles and machine learning studies Electronegativity ; Materials science ; Electrical resistivity and conductivity ; Valence (chemistry) ; Binary number ; Valence electron ; Atomic radius ; Thermodynamics ; Solubility ; Conductivity ; Metallurgy ; Electron ; Physical chemistry ; Quantum mechanics ; Physics Magnesium Alloys: Properties and Applications ; Hydrogen Storage and Materials ; MXene and MAX Phase Materials
Authors: Maria Bånkestad | Kevin M. Dorst | Göran Widmalm | Jerk Rönnols
Source: RSC Advances (article, 2024-01-01)
Field: Chemistry — Spectroscopy

Carbohydrate NMR chemical shift prediction by GeqShift employing E(3) equivariant graph neural networks Carbohydrates, vital components of biological systems, are well-known for their structural diversity. Nuclear Magnetic Resonance (NMR) spectroscopy plays a crucial role in understanding their intricate molecular arrangements and is essential in assessing and verifying the molecular structure of organic molecules. An important part of this process is to predict the NMR chemical shift from the molecular structure. This work introduces a novel approach that leverages E(3) equivariant graph neural networks to predict carbohydrate NMR spectral data. Notably, our model achieves a substantial reduction in mean absolute error, up to threefold, compared to traditional models that rely solely on two-dimensional molecular structure. Even with limited data, the model excels, highlighting its robustness and generalization capabilities. The model is dubbed <i>GeqShift</i> (geometric equivariant shift) and uses equivariant graph self-attention layers to learn about NMR chemical shifts, in particular since stereochemical arrangements in carbohydrate molecules are characteristics of their structures. Equivariant map ; Graph ; Artificial neural network ; Artificial intelligence ; Chemistry ; Computational chemistry ; Computer science ; Biological system ; Mathematics ; Theoretical computer science ; Pure mathematics ; Biology Molecular spectroscopy and chirality ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Haibo Xue | Guanjian Cheng | Wan‐Jian Yin
Source: Wiley Interdisciplinary Reviews Computational Molecular Science (article, 2024-09-01)
Field: Materials Science — Materials Chemistry

Computational design of energy‐related materials: From first‐principles calculations to machine learning Abstract Energy‐related materials are crucial for advancing energy technologies, improving efficiency, reducing environmental impacts, and supporting sustainable development. Designing and discovering these materials through computational techniques necessitates a comprehensive understanding of the material space, which is defined by the constituent atoms, composition, and structure. Depending on the search space involved in the investigation, the computational materials design can be categorized into four primary approaches: atomic substitution in fixed prototype structures, crystal structure prediction (CSP), variable‐composition CSP, and inverse design across the entire materials space. This review provides an overview of these paradigms, detailing the concepts, strategies, and applications pertinent to energy‐related materials. The progression from first‐principles calculations to machine learning techniques is emphasized, with the aim of enhancing understanding and elucidating new advancements in computationally design of energy‐related materials. This article is categorized under: Structure and Mechanism &gt; Computational Materials Science Data Science &gt; Artificial Intelligence/Machine Learning Electronic Structure Theory &gt; Density Functional Theory Computer science ; Energy (signal processing) ; Computational science ; Physics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography
Authors: K. J. Schmidt | Aristana Scourtas | Logan Ward | Steven R. Wangen | Marcus Schwarting | Isaac Darling | Ethan Truelove | Aadit Ambadkar | R. Bose | Zoa Katok | Jingrui Wei | Xiangguo Li | Ryan Jacobs | Lane E. Schultz | Doyeon Kim | Michael C. Ferris | Paul M. Voyles | Dane Morgan | Ian Foster | Ben Blaiszik
Source: The Journal of Open Source Software (article, 2024-01-23)
Field: Materials Science — Materials Chemistry

Foundry-ML - Software and Services to Simplify Accessto Machine Learning Datasets in Materials Science Schmidt et al., (2024). Foundry-ML - Software and Services to Simplify Access to Machine Learning Datasets in Materials Science. Journal of Open Source Software, 9(93), 5467, https://doi.org/10.21105/joss.05467 Foundry ; Computer science ; Software ; Software engineering ; Engineering ; Operating system ; Mechanical engineering Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Electron and X-Ray Spectroscopy Techniques
Authors: Congli Mei | Yingchao Xue | Qihan Li | Hui Jiang
Source: Infrared Physics & Technology (article, 2024-06-14)
Field: Chemistry — Analytical Chemistry

Deep learning model based on molecular spectra to determine chlorpyrifos residues in corn oil Chlorpyrifos ; Corn oil ; Spectral line ; Environmental science ; Materials science ; Computer science ; Agronomy ; Pesticide ; Chemistry ; Biology ; Food science ; Physics Spectroscopy and Chemometric Analyses ; Pesticide Residue Analysis and Safety ; Water Quality Monitoring and Analysis
Authors: Hamdi Chaouk | Emil Obeid | Jalal Halwani | Jack Arayro | Rabih Mezher | Omar Mouhtady | Eddie Gazo Hanna | Semaan Amine | Khaled Younes
Source: Gels (article, 2024-08-27)
Field: Chemistry — Spectroscopy

Machine Learning Techniques to Analyze the Influence of Silica on the Physico-Chemical Properties of Aerogels This study explores the application of machine learning techniques, specifically principal component analysis (PCA), to analyze the influence of silica content on the physical and chemical properties of aerogels. Silica aerogels are renowned for their exceptional properties, including high porosity, large surface area, and low thermal conductivity, but their mechanical brittleness poses significant challenges. The study initially utilized cross-correlation analysis to examine the relationships between key properties such as the Brunauer-Emmett-Teller (BET) surface area, pore volume, density, and thermal conductivity. However, weak correlations prompted the application of PCA to uncover deeper insights into the data. The PCA results demonstrated that silica content has a significant impact on aerogel properties, with the first principal component (PC1) showing a strong positive correlation (R<sup>2</sup> = 94%) with silica content. This suggests that higher silica levels correspond to lower thermal conductivity, porosity, and BET surface area, while increasing the density and elastic modulus. Additionally, the analysis identified the critical role of thermal conductivity in the second principal component (PC2), particularly in samples with moderate to high silica content. Overall, this study highlights the effectiveness of machine learning techniques like PCA in optimizing and understanding the complex inter-relationships among the physico-chemical properties of silica aerogels. Materials science ; Aerogel ; Nanotechnology ; Computer science ; Chemical engineering ; Engineering Aerogels and thermal insulation
Authors: Mashqoor Alam | Samina Husain
Source: Applied Physics A (article, 2024-12-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Hyperparameter tuned machine learning predictions of specific capacitance of conducting polymers and their composites for high performance advanced supercapacitors Supercapacitor ; Support vector machine ; Random forest ; Capacitance ; Mean squared error ; Materials science ; Conductive polymer ; Multilayer perceptron ; Perceptron ; Polypyrrole ; Computer science ; Polymer ; Artificial neural network ; Electrode ; Artificial intelligence ; Machine learning ; Composite material ; Mathematics ; Statistics ; Chemistry Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials
Authors: Maximilian P Niroomand | Luke Dicks | Edward O. Pyzer‐Knapp | David J. Wales
Source: Digital Discovery (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Insights into machine learning models from chemical physics: an energy landscapes approach (EL for ML) In this work, we outline how methods from the energy landscapes field of theoretical chemistry can be applied to study machine learning models. Various applications are found, ranging from interpretability to improved model performance. Interpretability ; Field (mathematics) ; Energy (signal processing) ; Computer science ; Machine learning ; Artificial intelligence ; Data science ; Physics ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Yuexin Wang | Min‐Hui Li | Ran Cao | Ming Lei | Zhi‐Jun Sui | Xinggui Zhou | De Chen | Yi‐An Zhu
Source: Chem Catalysis (article, 2024-01-12)
Field: Materials Science — Materials Chemistry

A machine-learning-assisted study of propylene adsorption behaviors on transition metals and alloys: Beyond the Dewar-Chatt-Duncanson model Adsorption ; Transition metal ; Materials science ; Chemistry ; Physical chemistry ; Catalysis ; Organic chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: David Dell’Angelo | Yann Foucaud | Júlio De Mesquita | Juliette Lainé | Henrique Dias Gatti Turrer | Michaël Badawi
Source: Minerals Engineering (article, 2024-10-10)
Field: Materials Science — Materials Chemistry

Enhanced Machine Learning Molecular Simulations for optimization of flotation selectivity: A perspective paper The recovery of valuable minerals in froth flotation industry relies on finding inexpensive and environmentally friendly reagents that selectively adsorb upon surfaces and interfaces. Computer simulations, especially when extended, provide access to detailed mechanistic information on solvent configurations and may ascertain crucial dynamical events over the adsorption process. Further, in silico throughput screening can prevent both the high cost of experiments and the related risks to the environment. Yet, a better compromise between accuracy and computational cost must be met. Machine learning (ML) simulations may ease the latter and suggest solidophilic reagents able to improve the flotation efficiency, shedding new light on discerning descriptors able to accurately capture the nature of the molecule-surface interaction. In this work, our recent advancements in modeling of new accurate mineral-water interfaces based on active learning of ab initio molecular dynamics trajectories have been introduced. The case of some habitual oxides and minerals liberated in mining industry will be taken as examples. • How the mineral slab may influence the layering of surfactant water molecules. • Water kinetics at the hematite, ferrosilite and magnetite interfaces. • Competing adsorption phenomena between oleic acid and amylopectin on calcite surface. • In silico screening of flotation reagents employing machine learning methodologies. Perspective (graphical) ; Selectivity ; Process engineering ; Computer science ; Biochemical engineering ; Nanotechnology ; Chemistry ; Machine learning ; Materials science ; Engineering ; Artificial intelligence ; Organic chemistry Machine Learning in Materials Science ; Minerals Flotation and Separation Techniques ; Mineral Processing and Grinding
Authors: Nikzad Motamedi | Hazem Wannous | Vincent Magnier
Source: International Journal of Mechanical Sciences (article, 2024-09-04)
Field: Materials Science — Materials Chemistry

Deep learning identifies transversely isotropic material properties using kinematics fields Transverse isotropy ; Kinematics ; Isotropy ; Materials science ; Geometry ; Engineering ; Classical mechanics ; Mechanical engineering ; Physics ; Mathematics ; Optics Machine Learning in Materials Science ; Mineral Processing and Grinding ; Rock Mechanics and Modeling
Authors: Raúl A. Márquez | Erin Elizabeth Oefelein | Thuy Vy Le | Kenta Kawashima | Lettie A. Smith | C. Buddie Mullins
Source: ACS Materials Letters (article, 2024-06-10)
Field: Materials Science — Materials Chemistry

Redefining the Stability of Water Oxidation Electrocatalysts: Insights from Materials Databases and Machine Learning Research on electrochemical water splitting has experienced significant growth in interest in transition metal borides, carbides, pnictides, and chalcogenides, owing to their notable catalytic performance. These materials, collectively called X-ides, are often considered promising electrocatalysts for the oxygen evolution reaction (OER). However, under the strongly oxidizing conditions of the OER, transition metal X-ides often act as precatalysts, undergoing in situ reconstruction to a different, catalytically active phase. Discrepancies exist in the literature, with some studies claiming the absence of such transformations. Building upon previous efforts to elucidate catalytic performance trends in the community, this Perspective discusses a more nuanced approach to X-ide research, emphasizing the need to reassess our understanding of their chemical stability and the significance of the in situ reconstruction process. By discussing the role of experimental and computational databases, we present strategies for predicting X-ide stability and stress the importance of thorough experimental validation. Moreover, we highlight the use of machine learning to extract meaningful insights from these data and urge the community to adopt a standardized, systematic reporting of X-ide performance. Finally, we provide strategic guidelines and directions to advance transition metal X-ide research, ultimately enhancing their future application for a sustainable hydrogen economy. Stability (learning theory) ; Computer science ; Database ; Materials science ; Nanotechnology ; Machine learning Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: Yunzhen Du | Yuan Yao | Kunling Peng | Jizheng Duan | Changwei Hao | Yuan Tian | Wen‐Shan Duan | Lei Yang | Ping Lin | Sheng Zhang
Source: Physical Chemistry Chemical Physics (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Impact of vacancy defects on the thermal conductivity of BaAgBi: a comprehensive study using molecular dynamics simulations with neural network potentials The presence of vacancy defects significantly impacts thermal properties of materials. In this research, we delve into the effects of vacancy defects on the thermal conductivity of ternary alloy BaAgBi, employing molecular dynamics simulations coupled with a deep neural network potential (NNP). Initially, we validate the precision of our NNP by comparing their predictions for energy, atomic forces, phonon dispersion curves, phonon density of states, and vacancy formation energy with density functional theory calculations, ensuring a high degree of accuracy. Our findings reveal that the reduction in thermal conductivity due to vacancies aligns with the Debye-Callaway model, with variations depending on the type of vacancy. Specifically, Ba vacancies result in the most notable decrement in thermal conductivity, attributable to their low phonon participation ratio and high lattice distortion, both factors that enhance phonon scattering. Besides, we find that the high energy barrier (∼1.66 eV) indicates that Ba vacancies hardly migrate at 300 K. This study helps us understand how vacancies affect thermal conductivity in BaAgBi and how different vacancy types affect it. Vacancy defect ; Molecular dynamics ; Thermal conductivity ; Statistical physics ; Thermal ; Chemical physics ; Artificial neural network ; Dynamics (music) ; Materials science ; Physics ; Computational chemistry ; Chemistry ; Condensed matter physics ; Thermodynamics ; Computer science ; Artificial intelligence Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Superconductivity in MgB2 and Alloys
Authors: Chingtham Henthoibi Devi | S.A. Sahaaya Arul Mary | N. Karthikeyan | S Varalakshmi | Vamsidhar Talasila | Gangu Rama Naidu
Source: Thermal Science and Engineering Progress (article, 2024-10-01)
Field: Materials Science — Materials Chemistry

Graph neural network-based multiscale thermal modeling for heterogeneous materials with complex structures Computer science ; Artificial neural network ; Graph ; Artificial intelligence ; Theoretical computer science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Iron and Steelmaking Processes
Authors: Ke Yuan | Nikhil Rampal | Sai Adapa | Barbara R. Evans | Jacquelyn N. Bracco | Matthew G. Boebinger | Andrew G. Stack | Juliane Weber
Source: ACS Applied Materials & Interfaces (article, 2024-11-11)
Field: Materials Science — Materials Chemistry

Iron Impurity Impairs the CO<sub>2</sub> Capture Performance of MgO: Insights from Microscopy and Machine Learning Molecular Dynamics Magnesium oxide (MgO) is a promising sorbent for direct air capture (DAC) of carbon dioxide. Iron (Fe) is a common impurity in naturally occurring MgO and minerals used to produce MgO, yet a molecular-scale understanding of Fe-doping effects on carbonation is lacking. Here, we observed reduced carbonation performance in Fe-doped MgO experimentally. The energetics of adsorbing a (bi)carbonate ion on pristine and Fe-doped MgO(001) surfaces were further investigated using <i>ab initio</i> and machine learning potential molecular dynamics coupled with metadynamics simulations. Both pristine and Fe-doped surfaces exhibited a basic (OH<sup>-</sup>) hydration layer, where the (bi)carbonate ion adsorption is thermodynamically favorable. However, the dissolution of surface Fe had smaller energy barriers and was more favorable than Mg. Leached Fe likely neutralized the near-surface basicity, yielding reduced reactivity on Fe-doped MgO. Our observations offer critical insights for material selection and emphasize the importance of evaluating the geologic origin of earth materials used for DAC. Materials science ; Carbonation ; Metadynamics ; Doping ; Impurity ; Molecular dynamics ; Magnesium ; Dissolution ; Adsorption ; Chemical engineering ; Oxide ; Sorbent ; Inorganic chemistry ; Chemical physics ; Carbonate ; Physical chemistry ; Metallurgy ; Computational chemistry ; Chemistry ; Organic chemistry Magnesium Oxide Properties and Applications ; High-pressure geophysics and materials ; CO2 Sequestration and Geologic Interactions
Authors: Zhenhao Sun | Nanyan Hu | Yicheng Ye | Dunxi Chen | Liyuan Gui | Rongbin Tang
Source: Ceramics International (article, 2024-06-25)
Field: Materials Science — Materials Chemistry

A novel deep learning strategy to optimize Al2O3–SiO2 porous ceramics with phosphate tailings as raw material Tailings ; Materials science ; Raw material ; Ceramic ; Porosity ; Phosphate ; Metallurgy ; Mineralogy ; Chemical engineering ; Composite material ; Geology ; Engineering Magnesium Oxide Properties and Applications ; Geophysical Methods and Applications ; Ferroelectric and Piezoelectric Materials