Applying Classical, Ab Initio, and Machine-Learning Molecular Dynamics Simulations to the Liquid Electrolyte for Rechargeable Batteries
Rechargeable batteries have become indispensable implements in our daily life and are
considered a promising technology to construct sustainable energy systems in the future …
considered a promising technology to construct sustainable energy systems in the future …
Small data machine learning in materials science
P Xu, X Ji, M Li, W Lu - npj Computational Materials, 2023 - nature.com
This review discussed the dilemma of small data faced by materials machine learning. First,
we analyzed the limitations brought by small data. Then, the workflow of materials machine …
we analyzed the limitations brought by small data. Then, the workflow of materials machine …
MACE: Higher order equivariant message passing neural networks for fast and accurate force fields
Creating fast and accurate force fields is a long-standing challenge in computational
chemistry and materials science. Recently, Equivariant Message Passing Neural Networks …
chemistry and materials science. Recently, Equivariant Message Passing Neural Networks …
Physics-inspired structural representations for molecules and materials
The first step in the construction of a regression model or a data-driven analysis, aiming to
predict or elucidate the relationship between the atomic-scale structure of matter and its …
predict or elucidate the relationship between the atomic-scale structure of matter and its …
Nested sampling for physical scientists
G Ashton, N Bernstein, J Buchner, X Chen… - Nature Reviews …, 2022 - nature.com
Abstract This Primer examines Skilling's nested sampling algorithm for Bayesian inference
and, more broadly, multidimensional integration. The principles of nested sampling are …
and, more broadly, multidimensional integration. The principles of nested sampling are …
Device-scale atomistic modelling of phase-change memory materials
Computer simulations can play a central role in the understanding of phase-change
materials and the development of advanced memory technologies. However, direct quantum …
materials and the development of advanced memory technologies. However, direct quantum …
Machine learning interatomic potentials and long-range physics
DM Anstine, O Isayev - The Journal of Physical Chemistry A, 2023 - ACS Publications
Advances in machine learned interatomic potentials (MLIPs), such as those using neural
networks, have resulted in short-range models that can infer interaction energies with near …
networks, have resulted in short-range models that can infer interaction energies with near …
SELFIES and the future of molecular string representations
Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad
applications to challenging tasks in chemistry and materials science. Examples include the …
applications to challenging tasks in chemistry and materials science. Examples include the …
Extending machine learning beyond interatomic potentials for predicting molecular properties
Abstract Machine learning (ML) is becoming a method of choice for modelling complex
chemical processes and materials. ML provides a surrogate model trained on a reference …
chemical processes and materials. ML provides a surrogate model trained on a reference …
Implicit solvation methods for catalysis at electrified interfaces
Implicit solvation is an effective, highly coarse-grained approach in atomic-scale simulations
to account for a surrounding liquid electrolyte on the level of a continuous polarizable …
to account for a surrounding liquid electrolyte on the level of a continuous polarizable …