BAMBOO: a predictive and transferable machine learning force field framework for liquid electrolyte development

arXiv:2404.07181v1 Announce Type: cross
Abstract: Despite the widespread applications of machine learning force field (MLFF) on solids and small molecules, there is a notable gap in applying MLFF to complex liquid electrolytes. In this work, we introduce BAMBOO (ByteDance AI Molecular Simulation Booster), a novel framework for molecular dynamics (MD) simulations, with a demonstration of its capabilities in the context of liquid electrolytes for lithium batteries. We design a physics-inspired graph equivariant transformer architecture as the backbone of BAMBOO to …

abstract applications arxiv bytedance cond-mat.mtrl-sci cs.lg development electrolytes framework gap machine machine learning molecules novel physics.comp-ph predictive simulation small type work