Integrating Automated Electrochemistry and High‐Throughput Characterization with Machine Learning to Explore Si─Ge─Sn Thin‐Film Lithium Battery Anodes
Source abstract
Abstract
Abstract High‐performance batteries need accelerated discovery and optimization of new anode materials. Herein, we explore the Si─Ge─Sn ternary alloy system as a candidate fast‐charging anode materials system by utilizing a scanning droplet cell (SDC) as an autonomous electrochemical characterization tool with the goal of subsequent upscaling. As the SDC is performing experiments sequentially, an exploration of the entire ternary space is unfeasible due to time constraints. Thus, closed‐loop optimization, guided by real‐time data analysis and sequential learning algorithms, is utilized to direct experiments. The lead material identified is scaled up to a coin cell to validate the findings from the autonomous millimeter‐scale thin‐film electrochemical experimentation. Explainable machine learning (ML) models incorporating data from high‐throughput Raman spectroscopy and X‐ray diffraction (XRD) are used to elucidate the effect of short and long‐range ordering on material performance.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Machine Learning in Materials Science · Advancements in Battery Materials · Semiconductor materials and interfaces
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Helmholtz Institute Ulm (HIU) Helmholtzstr. 11 89081 Ulm Germany
Karlsruhe Institute of Technology (KIT) 76021 Karlsruhe Germany
Technical University of Munich (TUM) TUM School of Natural Sciences Department of Chemistry Chair of Digital Catalysis Lichtenbergstr. 4 85748 Garching Germany
Technical University of Munich (TUM) Munich Data Science Institute (MDSI) Walther‐von‐Dyck‐Str. 10 85748 Garching Germany
Technical University of Munich (TUM) Munich Institute of Robotics and Machine Intelligence (MIRMI) Georg‐Brauchle‐Ring 60 80992 Munich Germany
Ruhr University Bochum (RUB) Institute for Materials Chair for Materials Discovery and Interfaces Universitätsstr. 150 44780 Bochum Germany
Technical University of Munich (TUM) Munich Institute of Integrated Materials Energy and Process Engineering (MEP) Lichtenbergstr. 4 a 85748 Garching Germany
Munich Center for Machine Learning (MCML) Oettingenstraße 67 80538 Munich Germany
Ulm University (UUlm) 89069 Ulm Germany
Funding
Deutsche Forschungsgemeinschaft · Horizon 2020 Framework Programme
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