Physics and Uncertainty-Aware Ensemble-Based ML-Models for Interatomic Potentials and Prediction of Reaction Networks and Cell-Level Performance

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] and dendritic growth [2] 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 [3]. Still, the cost is prohibitively high to reach sufficiently long time- and length scales to ensure proper statistical sampling [4]. 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 [4]. Here, we present new approaches using phase field models [2], graph neural networks [5] and new transition state training sets [6] for chemical reaction networks, and machine/deep learning models to predict the spatio-temporal evolution of electrochemical interphases [3]. We also discuss the development of methods like symbolic regression to learn the laws of electrolyte transport [8], the predictions of energy and forces with calibrated uncertainty quantification for interatomic potentials using neural network ensemble models [8,9]. Finally, we discuss how such models trained on multi-sourced and multi-fidelity data from multiscale computer simulations, operando characterization, high-throughput synthesis, and testing, to provide uncertainty-aware and explainable ML for early prediction of, e.g., of degradation trajectories for battery cells [10].<br/>1. Diddens, Bhowmik, et al., <i>Adv. </i><i>Mater. Inter.</i>, <b>2022</b>, 2101734<br/>2. Jeon, Ho, Vegge, Chang, <i>ACS Appl. </i><i>Mater. Inter</i>., <b>2022</b>, 14, 15275<br/>3. Bhowmik, Castelli, Garcia-Lastra, Jørgensen, Winther, Vegge, <i>Energy Storage Mater</i>., <b>2019</b>, 21, 446<br/>4. Bhowmik, Vegge, et al., <i>Adv. </i><i>Energy Mater</i>, <b>2021</b>, 2102698<br/>5. Schreiner, Bhowmik, Vegge, Winther, <i>Mach. Learn. Sci. Tech</i>., <b>2022</b>, 3, 045022<br/>6. Schreiner, Bhowmik, Vegge, Winther, <i>Sci. Data</i>, <b>2022</b>, 9, 779<br/>7. Flores, Wölke, Yan, Winter, Vegge, Cekic-Laskovic, Bhowmik, <i>Digital Discovery</i>,<b> 2022</b>, 1, 440<br/>8. Busk, Jørgensen, Bhowmik, Schmidt, Winther, Vegge, <i>Mach. </i><i>Learn.: Sci. Technol</i>, <b>2022</b>, 3, 015012<br/>9. Busk, Schmidt, Winther, Vegge, Jørgensen, <i>Phys. </i><i>Chem. Chem. Phys.</i>, <b>2023</b>, 5, 25828<br/>10. Rieger, Flores, Nielsen, Winther, Ayerbe, Vegge, Bhowmik, <i>Digital Discovery</i>,<b> 2023</b>, 2, 112