Data-driven atomistic modeling of crystalline and glassy solid-state electrolytes.

Area of Science:

• Materials Science
• Computational Chemistry
• Electrochemistry

Background:

• All-solid-state batteries offer enhanced safety and energy density but require deeper understanding of solid electrolytes.
• Atomistic simulations are key to this understanding, but traditional methods are computationally expensive.

Purpose of the Study:

• To review recent advancements in machine-learning force fields (ML-FFs) for solid electrolyte research.
• To highlight the application of ML-FFs in studying crystalline and glassy solid electrolytes.
• To discuss challenges and future directions for ML-FFs in battery development.

Main Methods:

• Discussion of ML-FF frameworks and training strategies.
• Comparison of different ML-FF models, including transferability and uncertainty quantification.
• Review of best practices for data generation and validation in ML-FF development.

Main Results:

• ML-FFs enable large-scale, long-timescale simulations, surpassing ab initio methods.
• Applications reveal insights into ionic transport, defects, structure-property relationships, phase stability, and interfacial phenomena.
• ML-FFs are shown to be effective for both crystalline and glassy solid electrolytes.

Conclusions:

• ML-FFs are a powerful tool for accelerating the discovery and optimization of solid electrolytes.
• Key challenges remain, including long-range electrostatics, chemical reactivity, and multi-component systems.
• Further development of ML-FFs will be crucial for practical all-solid-state batteries.