(Invited) BIG-MAP - Going BIG to MAP an Accelerated Path to Sustainable Battery Materials Discovery
Authors: Vegge Tejs 
Published: December 2024

The large-scale European research initiative, the Battery Interface Genome – Materials Acceleration Platform (BIG-MAP), set forth to reinvent the ways we invent batteries in Europe by developing a distributed framework for accelerated autonomous discovery of sustainable battery materials and interfaces. A central element in this process is the development of a closed-loop MAP infrastructure, where multiple and geographically distributed laboratories or tenants can work jointly using autonomous workflows to co-optimize materials properties. Here, we show an example using the Fast INtention-Agnostic LEarning Server (FINALES) framework [1] to orchestrate a two-pronged optimization task, where both optimization tasks vary the composition of a battery electrolyte composed of ethylene carbonate (EC), ethyl methyl carbonate (EMC), and lithium hexafluorophosphate (LiPF6). However, one targets the optimization of ionic conductivity, while the other aims to maximize the end-of-life (EOL) of coin cells [2]. A second critical component is the “BIG”, where establishing spatio-temporal structure-property relations of the dynamic processes at solid-liquid interfaces is key to developing more efficient and durable batteries. Fundamental and performance-limiting interfacial processes like forming the Solid-Electrolyte Interphase (SEI) span numerous time- and length scales, and despite decades of research, the fundamental understanding of the 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, and machine learning (ML) potentials offer a potential solution to this challenge [4]. 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]. Finally, we present new approaches using foundation models [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 [8]. We also discuss the development of active learning methods to accelerate the segmentation of microstructures in non-destructive 3D imaging techniques, such as X-ray nano-holo-tomography, and enable the visualization of battery electrodes. 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 patterns from chemical spectra (Figure 1) [9].

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Figure 1