Recent advances and applications of deep learning methods in materials science
Deep learning (DL) is one of the fastest-growing topics in materials data science, with
rapidly emerging applications spanning atomistic, image-based, spectral, and textual data …
rapidly emerging applications spanning atomistic, image-based, spectral, and textual data …
Atomic scale design of MXenes and their parent materials─ from theoretical and experimental perspectives
More than a decade after the discovery of MXene, there has been a remarkable increase in
research on synthesis, characterization, and applications of this growing family of two …
research on synthesis, characterization, and applications of this growing family of two …
CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling
Large-scale simulations with complex electron interactions remain one of the greatest
challenges for atomistic modelling. Although classical force fields often fail to describe the …
challenges for atomistic modelling. Although classical force fields often fail to describe the …
Review of computational approaches to predict the thermodynamic stability of inorganic solids
CJ Bartel - Journal of Materials Science, 2022 - Springer
Improvements in the efficiency and availability of quantum chemistry codes, supercomputing
centers, and open materials databases have transformed the accessibility of computational …
centers, and open materials databases have transformed the accessibility of computational …
Accelerating the prediction of stable materials with machine learning
Despite the rise in computing power, the large space of possible combinations of elements
and crystal structure types makes large-scale high-throughput surveys of stable materials …
and crystal structure types makes large-scale high-throughput surveys of stable materials …
Probing out-of-distribution generalization in machine learning for materials
Scientific machine learning (ML) aims to develop generalizable models, yet assessments of
generalizability often rely on heuristics. Here, we demonstrate in the materials science …
generalizability often rely on heuristics. Here, we demonstrate in the materials science …
WyCryst: Wyckoff inorganic crystal generator framework
Recent advancements in property-directed generative design of inorganic materials account
for periodicity and global Euclidian symmetry through translations, rotations, and reflections; …
for periodicity and global Euclidian symmetry through translations, rotations, and reflections; …
Multi-scale computational study of high-temperature corrosion and the design of corrosion-resistant alloys
Corrosion is a serious problem, which reduces the efficiency and lifespan of various
technologies, such as thermal power plants, aviation, nuclear reactors, etc. It starts from the …
technologies, such as thermal power plants, aviation, nuclear reactors, etc. It starts from the …
[PDF][PDF] Open materials 2024 (omat24) inorganic materials dataset and models
Date: October 18, 2024 Correspondence: L. Barroso-Luque (lbluque@ meta. com), CL
Zitnick (zitnick@ meta. com), Z. Ulissi (zulissi@ meta. com) Code: https://github. com/FAIR …
Zitnick (zitnick@ meta. com), Z. Ulissi (zulissi@ meta. com) Code: https://github. com/FAIR …
Accelerating the Search for New Solid Electrolytes: Exploring Vast Chemical Space with Machine Learning-Enabled Computational Calculations
Discovering new solid electrolytes (SEs) is essential to achieving higher safety and better
energy density for all-solid-state lithium batteries. In this work, we report machine learning …
energy density for all-solid-state lithium batteries. In this work, we report machine learning …