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 language | English |
|---|---|
| Article number | e06213 |
| Journal | Advanced Energy Materials |
| Volume | 16 |
| Issue number | 19 |
| DOIs |
|
| State | Published - 20 May 2026 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
Keywords
- Bayesian optimization
- automated synthesis
- battery
- machine learning
- solid-state electrolyte
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