Fabrication of High-Quality Thin Solid-State Electrolyte Films Assisted by Machine Learning

Yu-Ting Chen; Marc Duquesnoy; Darren H. S. Tan; Jean-Marie Doux; Hedi Yang; Grayson Deysher; Phillip Ridley; Alejandro A. Franco; Ying Shirley Meng; Zheng Chen

doi:10.1021/acsenergylett.1c00332

journal article·2021-04-01Open access

Fabrication of High-Quality Thin Solid-State Electrolyte Films Assisted by Machine Learning

Yu-Ting Chen, Marc Duquesnoy, Darren H. S. Tan, Jean-Marie Doux, Hedi Yang, Grayson Deysher, Phillip Ridley, Alejandro A. Franco, Ying Shirley Meng, Zheng Chen

ACS Energy Letters

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Source abstract

Abstract

Solid-state electrolytes (SSEs) are promising candidates to circumvent flammability concerns of liquid electrolytes. However, enhancing energy densities by thinning SSE layers and enabling scalable coating processes remain challenging. While previous studies have addressed thin and flexible SSEs, mainly ionic conductivity was considered for performance evaluation, and no systematic research on the effects of manufacturing conditions on the quality of SSE films was performed. Here, both uniformity and ionic conductivity are considered for evaluating the SSE films under the guidance of machine learning (ML). Three algorithms, principal component analysis, K-means clustering, and support vector machine, are employed to decipher the interdependencies between manufacturing conditions and film performance. Guided by ML, a 40 μm SSE film with high ionic conductivity and good uniformity is used to construct a LiNi0.8Co0.1Mn0.1O2 || Li6PS5Cl || LiIn cell demonstrating 100 cycles. This study presents an efficient ML-assisted approach to optimize scalable production of high-quality SSE films.

Source: openalex_inverted_index · Quality: baseline

Research context

Energy storage

OpenAlex topics: Advanced Battery Materials and Technologies · Advancements in Battery Materials · Advanced Battery Technologies Research

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Program of Materials Science and Engineering, University of California San Diego, La Jolla, California 92093, United States

Laboratoire de Réactivité et Chimie des Solides (LRCS), Université de Picardie Jules Verne, UMR CNRS 7314, Hub de l’Energie, 80039 Amiens, France

Réseau sur le Stockage Electrochimique de l’Energie (RS2E), FR CNRS 3459, Hub de l’Energie, 80039 Amiens, France

Alistore-ERI European Research Institute, FR CNRS 3104, Hub de l’Energie, 80039 Amiens, France

Department of NanoEngineering, University of California San Diego, La Jolla, California 92093, United States

Institut Universitaire de France, 75005 Paris, France

Sustainable Power & Energy Center (SPEC), University of California San Diego, La Jolla, California 92093, United States

Program of Chemical Engineering, University of California San Diego, La Jolla, California 92093, United States

Funding

Institut Universitaire de France · H2020 European Research Council · Sixth Framework Programme · LG Chem · Jacob School of Engineering, University of California, San Diego · Energy & Biosciences Institute

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