ByteFF-Pol

Differentiable hybrid force fields support scalable autonomous electrolyte discovery

arXiv:2604.07979v2 Announce Type: replace-cross Abstract: Autonomous electrolyte discovery demands a computational engine that satisfies a critical trilemma: it must be fast enough for high-throughput screening, accurate enough for quantitative property prediction, and calibratable enough for online refinement. Classical empirical force fields (FFs) are fast but rely on error cancellation, while standard machine learning interatomic potentials (MLIPs) are computationally expensive. In this...