[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 …
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 …
interest in the developments of batteries with excellent service performance and safety …
[HTML][HTML] Learning local equivariant representations for large-scale atomistic dynamics
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 …
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
Abstract This work presents Neural Equivariant Interatomic Potentials (NequIP), an E (3)-
equivariant neural network approach for learning interatomic potentials from ab-initio …
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
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 …
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
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 …
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
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 …
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
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 …
initio methods or bond-order force fields requiring arduous parametrization. Here, we …
Graph neural networks accelerated molecular dynamics
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 …
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
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 …
physics, chemistry, and materials science, but limited by the cost of accurate and precise …