The design space of E (3)-equivariant atom-centred interatomic potentials
Molecular dynamics simulation is an important tool in computational materials science and
chemistry, and in the past decade it has been revolutionized by machine learning. This rapid …
chemistry, and in the past decade it has been revolutionized by machine learning. This rapid …
Cartesian atomic cluster expansion for machine learning interatomic potentials
B Cheng - npj Computational Materials, 2024 - nature.com
Abstract Machine learning interatomic potentials are revolutionizing large-scale, accurate
atomistic modeling in material science and chemistry. Many potentials use atomic cluster …
atomistic modeling in material science and chemistry. Many potentials use atomic cluster …
Introduction to machine learning potentials for atomistic simulations
Abstract Machine learning potentials have revolutionised the field of atomistic simulations in
recent years and are becoming a mainstay in the toolbox of computational scientists. This …
recent years and are becoming a mainstay in the toolbox of computational scientists. This …
[HTML][HTML] A theoretical case study of the generalization of machine-learned potentials
Abstract Machine-learned interatomic potentials (MLIPs) are typically trained on datasets
that encompass a restricted subset of possible input structures, which presents a potential …
that encompass a restricted subset of possible input structures, which presents a potential …
Overcoming the chemical complexity bottleneck in on-the-fly machine learned molecular dynamics simulations
LR Timmerman, S Kumar… - Journal of Chemical …, 2024 - ACS Publications
We develop a framework for on-the-fly machine learned force field molecular dynamics
simulations based on the multipole featurization scheme that overcomes the bottleneck with …
simulations based on the multipole featurization scheme that overcomes the bottleneck with …
Guest editorial: Special Topic on software for atomistic machine learning
Welcome to the Journal of Chemical Physics' Special Topic on Software for Atomistic
Machine Learning. For some years now, search engines have been dominating our online …
Machine Learning. For some years now, search engines have been dominating our online …
Prediction rigidities for data-driven chemistry
The widespread application of machine learning (ML) to the chemical sciences is making it
very important to understand how the ML models learn to correlate chemical structures with …
very important to understand how the ML models learn to correlate chemical structures with …
17O NMR Spectroscopy Reveals CO2 Speciation and Dynamics in Hydroxide‐Based Carbon Capture Materials
BJ Rhodes, LL Schaaf, ME Zick, SM Pugh… - …, 2024 - Wiley Online Library
Carbon dioxide capture technologies are set to play a vital role in mitigating the current
climate crisis. Solid‐state 17O NMR spectroscopy can provide key mechanistic insights that …
climate crisis. Solid‐state 17O NMR spectroscopy can provide key mechanistic insights that …
Graph Atomic Cluster Expansion for Semilocal Interactions beyond Equivariant Message Passing
The atomic cluster expansion provides local, complete basis functions that enable efficient
parametrization of many-atom interactions. We extend the atomic cluster expansion to …
parametrization of many-atom interactions. We extend the atomic cluster expansion to …
Vibrational and thermal properties of amorphous alumina from first principles
Amorphous alumina is employed ubiquitously as a high-dielectric-constant material in
electronics, and its thermal-transport properties are of key relevance for heat management in …
electronics, and its thermal-transport properties are of key relevance for heat management in …