Risk-Controlled DFT Selection for Machine-Learned Interatomic Potentials
arXiv:2610.19630v1 Announce Type: new Abstract: Automated materials discovery requires evaluating large pools of candidate configurations, with density functional theory (DFT) as a major throughput bottleneck. Pretrained machine-learned interatomic potentials (MLIPs) approximate DFT cheaply, but th…
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- 2026-10-15 04:00 · arXiv stat.ML
Risk-Controlled DFT Selection for Machine-Learned Interatomic Potentials