active learning 1atomic environment extraction 1density functional theory 1machine learning interatomic potentials 1molecular dynamics 1
From the 1 of 2 linked papers with an AI index.
2 papers
cond-mat.mtrl-sci2026
Extracting Atomic Environments for Machine Learning Interatomic Potentials
Jared C. Stimac, Fei Zhou, Kyle Bushick +4
The paper benchmarks methods for extracting small atomic environments from large-scale simulations to enable DFT calculations for training machine‑learning interatomic potentials,…
cond-mat.mtrl-sci2026
A probabilistic framework for crystal structure denoising, phase classification, and order parameters
Hyuna Kwon, Babak Sadigh, Sebastien Hamel +3
Atomistic simulations generate large volumes of noisy structural data, yet extracting phase labels and continuous order parameters (OPs) in a robust and general manner remains chal…