Satisfying renewable energy markets impels engineering of highly energy-dense, temperature-adaptive, sustainable, safe, and costeffective batteries. Identifying which parameters are critical to this endeavor entails connecting technological advancements to battery components-including cathodes, anodes, and electrolytes-that are fundamentally characterized correctly. Data-driven battery design reinforces overarching technological improvements through multiscale investigations of fundamental material properties and phenomena. This encompasses computational simulations, machine learning, and economics. Li-ion, Li-metal, Li-S, and anodefree Li cell materials are selected to favorably tune properties for battery applications. This review first develops a fundamental computational approach to materials selection and property tuning, merging precise atomistic simulation, machine learning, and data-driven techniques. Subsequently, it reconciles that approach with accelerating anodic, cathodic, and electrolytic design in Libased battery applications. Beyond extending discussion to generalized battery performance metrics and all-solid-state battery development, this review ultimately provides recommendations on how future research can be improved by implementing the described methodologies.

Sandip Maiti

Matthew T. Curnan

Kakali Maiti

Seokhyun Choung

Jeong Woo Han

Chem

CHEM 是 CELL 的姊妹期刊，为开创性和有见地的研究提供了场所，并展示了化学及其子学科的基础研究如何帮助找到应对未来全球挑战的潜在解决方案。

CHEM is a sister journal of CELL, dedicated to providing a platform for groundbreaking and insightful research. The journal showcases how fundamental research in chemistry and its sub-disciplines can help identify potential solutions to future global challenges.

CHEM 是 CELL 的姊妹期刊，致力于为开创性和有见地的研究提供一个平台。该期刊展示了化学及其子学科的基础研究如何为应对未来全球挑战提供潜在解决方案。

Category	Quartile
化学	1
化学, 化学综合	1

Accelerating Li-based battery design by computationally engineering materials

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Journal Info

Category	Quartile
CHEMISTRY, MULTIDISCIPLINARY	1