[HTML][HTML] A review of the recent progress in battery informatics

C Ling - npj Computational Materials, 2022 - nature.com
Batteries are of paramount importance for the energy storage, consumption, and
transportation in the current and future society. Recently machine learning (ML) has …

Machine learning for battery research

Z Wei, Q He, Y Zhao - Journal of Power Sources, 2022 - Elsevier
Batteries are vital energy storage carriers in industry and in our daily life. There is continued
interest in the developments of batteries with excellent service performance and safety …

[HTML][HTML] Learning local equivariant representations for large-scale atomistic dynamics

A Musaelian, S Batzner, A Johansson, L Sun… - Nature …, 2023 - nature.com
A simultaneously accurate and computationally efficient parametrization of the potential
energy surface of molecules and materials is a long-standing goal in the natural sciences …

[HTML][HTML] E (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials

S Batzner, A Musaelian, L Sun, M Geiger… - Nature …, 2022 - nature.com
Abstract This work presents Neural Equivariant Interatomic Potentials (NequIP), an E (3)-
equivariant neural network approach for learning interatomic potentials from ab-initio …

Beyond local solvation structure: nanometric aggregates in battery electrolytes and their effect on electrolyte properties

Z Yu, NP Balsara, O Borodin, AA Gewirth… - ACS Energy …, 2021 - ACS Publications
Electrolytes are an essential component of all electrochemical storage and conversion
devices, such as batteries. In the history of battery development, the complex nature of …

[HTML][HTML] On-the-fly active learning of interpretable Bayesian force fields for atomistic rare events

J Vandermause, SB Torrisi, S Batzner, Y Xie… - npj Computational …, 2020 - nature.com
Abstract Machine learned force fields typically require manual construction of training sets
consisting of thousands of first principles calculations, which can result in low training …

[HTML][HTML] Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture

CW Park, M Kornbluth, J Vandermause… - npj Computational …, 2021 - nature.com
Recently, machine learning (ML) has been used to address the computational cost that has
been limiting ab initio molecular dynamics (AIMD). Here, we present GNNFF, a graph neural …

[HTML][HTML] Active learning of reactive Bayesian force fields applied to heterogeneous catalysis dynamics of H/Pt

J Vandermause, Y Xie, JS Lim, CJ Owen… - Nature …, 2022 - nature.com
Atomistic modeling of chemically reactive systems has so far relied on either expensive ab
initio methods or bond-order force fields requiring arduous parametrization. Here, we …

Graph neural networks accelerated molecular dynamics

Z Li, K Meidani, P Yadav… - The Journal of Chemical …, 2022 - pubs.aip.org
Molecular Dynamics (MD) simulation is a powerful tool for understanding the dynamics and
structure of matter. Since the resolution of MD is atomic-scale, achieving long timescale …

[HTML][HTML] Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning

MF Langer, A Goeßmann, M Rupp - npj Computational Materials, 2022 - nature.com
Computational study of molecules and materials from first principles is a cornerstone of
physics, chemistry, and materials science, but limited by the cost of accurate and precise …