A 1.62-trillion-atom molecular dynamics simulation achieves ab initio accuracy with 100x speedup over prior machine learning force fields and 86.9% weak scaling to 45,000 GPGPUs.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
KA-CRNN learns continuous SOC-dependent kinetic parameters for cathode-electrolyte decomposition directly from DSC data, reproducing heat-release features across all SOCs for NCA, NM, and NMA cathodes.
citing papers explorer
-
Trillion-atom molecular dynamics simulations with ab initio accuracy
A 1.62-trillion-atom molecular dynamics simulation achieves ab initio accuracy with 100x speedup over prior machine learning force fields and 86.9% weak scaling to 45,000 GPGPUs.
-
Learning continuous state of charge dependent thermal decomposition kinetics for Li-ion cathodes using Kolmogorov-Arnold Chemical Reaction Neural Networks (KA-CRNNs)
KA-CRNN learns continuous SOC-dependent kinetic parameters for cathode-electrolyte decomposition directly from DSC data, reproducing heat-release features across all SOCs for NCA, NM, and NMA cathodes.