A large-scale on-the-fly machine learning molecular dynamics simulation to explore lithium metal battery interfaces

Abstract

The global rapid transition towards sustainable energy systems has heightened the demand for high-performance lithium metal batteries (LMBs), where understanding interfacial phenomena is paramount. In this contribution, we present an on-the-fly machine learning molecular dynamics (OTF-MLMD) approach to probe the complex side reactions at lithium metal anode–electrolyte interfaces with exceptional accuracy and computational efficiency. The machine learning force field (MLFF) was firstly validated in a bulk-phase system comprising twenty 1,2-dimethoxyethane (DME) molecules, demonstrating energy fluctuations and structural parameters in close agreement with ab initio molecular dynamics (AIMD) benchmarks. Subsequent simulations of lithium–DME and lithium–electrolyte interfaces revealed minimal discrepancies in energy, bond lengths, and net charge variations (notably in FSI⁻ species), underscoring the method’s DFT-level precision of the approach. A further small-scale interfacial model enabled on-the-fly training over a mere of 340 fs, which was then successfully transferred to a large-scale simulation encompassing nearly 300,000 atoms, representing the largest interfacial model in LMB research up to date. The hierarchical validation strategy not only establishes the robustness of the MLFF in capturing both interfacial and bulk-phase chemistry but also paves the way for statistically meaningful simulations of battery interfaces. The fruitful findings highlight the transformative potential of OTF-MLMD in bridging the gap between atomistic accuracy and macroscopic modeling, affording a universal approach to understand interfacial reactions in LMBs.