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Roadmap for High-Throughput Ceramic Materials Synthesis and Discovery for Batteries

  • Jesse J. Hinricher
  • , Katarzyna P. Sokol
  • , Philipp Simons
  • , Kun Joong Kim
  • , Michael Foshey
  • , Yunsheng Tian
  • , Thorben Prein
  • , Lincoln J. Miara
  • , Elsa Olivetti
  • , Wojciech Matusik
  • , Jennifer L.M. Rupp
  • Massachusetts Institute of Technology
  • TUMint.Energy Research GmbH
  • Technical University of Munich
  • Massachusetts Institute of Technology
  • MIT Department of Electrical Engineering and Computer Science
  • Samsung Research America
  • Abteilung Physikalische Chemie

Research output: Contribution to journalComment/debate

Abstract

Global energy demand is projected to grow 30% within the next three decades, driven primarily by population growth and urbanization, leading to greater material needs in energy, and necessitates a new regime of accelerated research via a fundamentally improved strategy. In this perspective, we examine traditional ceramic synthesis methods for high-throughput synthesis and optimization, and highlight requirements and opportunities of synthesis routes for rapid alterations in the future. Such a strategy relies on flexible direct liquid precursor-to-solid film methods rather than traditional, but slower, solid-state methods. Application of computer-aided decision making takes in variables at all levels of fabrication and operates on both material and device characteristics to initialize and optimize the search for higher-performance devices, not just narrow materials optimization. Collectively, we provide a blueprint for accelerated ceramic materials and device improvements of next-generation materials research targeting energy storage.

Original languageEnglish
Article numbere06213
JournalAdvanced Energy Materials
Volume16
Issue number19
DOIs
StatePublished - 20 May 2026

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

Keywords

  • Bayesian optimization
  • automated synthesis
  • battery
  • machine learning
  • solid-state electrolyte

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