Reproduce “Scalable long-range machine learning interatomic potentials with electrical response for atomistic material modeling” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Yicheng Chen, Xiaoyun Yu, Hyejeong Song, Xintian Wang, Zihui Li, Zixi Xia, Junmin Chen, Peichen Zhong
DPA4C-LR extends local atomistic models to long-range electrostatics and electrical response while retaining distributed local derivatives. It predicts short-range energies and latent atomic charges, then computes global electrostatics and propagates charge gradients locally. It scales to 5.2 million atoms on four GPUs. In PMN, simulated cells show slimmer, nearly closed polarization-field loops without showing cell size is the only cause.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.