(Invited) Electrochemical Interfaces in Energy Storage: Theory Meets Experiment

Abstract

Understanding the dynamic processes at solid-liquid interfaces in electrochemical devices like batteries is key to developing more efficient and durable technologies for the green transition. Fundamental and performance-limiting interfacial processes like the formation of the Solid-Electrolyte Interphase (SEI) [1,2] and dendritic growth [3] span numerous time- and length scales. Despite decades of research, the fundamental understanding of structure-property relations remains elusive. Ab initio molecular dynamics (AIMD) generally provides sufficient accuracy to describe chemical reactions and the making and breaking of chemical bonds at these interfaces [4]. Still, the cost is prohibitively high to reach sufficiently long time- and length scales to ensure proper statistical sampling [5]. Machine learning (ML) potentials offer a potential solution to this challenge. Still, training ML-based potentials capable of handling activated processes in organic or aqueous electrolytes remains a fundamental challenge since the potential must capture both intra- and intermolecular interactions in the electrolyte and during chemical reactions at the interface [5]. Here, we present new approaches using phase field models [3], graph neural networks [6] and new transition state training sets [7] for chemical reaction networks, and machine/deep learning models to predict the spatio-temporal evolution of electrochemical interphases [2]. We also discuss the development of methods like symbolic regression to learn the laws of electrolyte transport [8], uncertainty quantification training and evaluating neural network ensemble models [9] using models trained on multi-sourced and multi-fidelity data from multiscale computer simulations, operando characterization, high-throughput synthesis, and testing, to provide, e.g., uncertainty-aware and explainable ML for early prediction of battery degradation trajectories (Figure 1) [10]. Figure 1