Toward Intrinsically Safer Batteries: Atomistic Insights, Thermochemical Tools, and Materials Strategies

Dear Colleagues,

Thermal runaway remains one of the most pressing safety challenges in the development and deployment of modern battery technologies, especially as energy storage systems up-scale for grid-level applications and electrified transportation. While safety features such as sensors and containment structures are essential, there is a growing shift toward including intrinsic battery safety into the design of the materials, interfaces, and chemistries themselves.

In this Special Issue of Batteries, "Toward Intrinsically Safer Batteries: Atomistic Insights, Thermochemical Tools, and Materials Strategies", our goal is to highlight recent advances in materials, multiscale modeling, and experimental strategies that improve our understanding of how electrochemical and thermal instabilities originate, propagate, and can be prevented through design considerations.

We are particularly interested in contributions that address the following themes:

• Explorations of molecular- and atomic-scale decomposition pathways during battery failure;
• Proposals of new thermochemical descriptors (e.g., heat of reaction, gas expansion) to quantify risk;
• Submissions that understand the role of the SEI and interface dynamics in suppressing or enabling failure modes;
• Developments of atomistic- and continuum-scale modeling strategies (DFT, AIMD, ML) to predict failure precursors;
• Advancements in material designs for flame-retardant electrolytes, protective coatings, and engineered separators;
• Evaluations of gas evolution, heat release, and internal shorting behavior under realistic storage or charging scenarios;
• Submissions that leverage machine learning, physics-informed modeling, and digital twins to enable predictive diagnostics and safety-driven material selection.

Topics of interest include, but are not limited to, the following:

• Atomistic simulations (AIMD, DFT) of electrolyte and SEI degradation;
• Modeling of gas venting, pressure buildup, and confinement effects;
• Flame-retardant additives, polymer electrolytes, and separators for thermal suppression;
• Physics-guided machine learning for safety prediction and diagnostics;
• Machine learning-guided electrolyte and interface material discovery;
• Safety screening methods for long-duration high-capacity storage (e.g., Na-ion, Zn-air);
• TR mitigation strategies for all-solid-state and flexible batteries.

We believe that this Special Issue will serve as a vital resource for researchers in the fields of battery science, materials chemistry, combustion engineering, and electrochemistry engineering that seek to redefine safety not as a retrofit, but as a core design principle.

Dr. Fernando A. Soto
Dr. Yuxun Ren
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Batteries is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

In this Special Issue of Batteries, "Toward Intrinsically Safer Batteries: Atomistic Insights, Thermochemical Tools, and Materials Strategies", our goal is to highlight recent advances in materials, multiscale modeling, and experimental strategies that improve our understanding of how electrochemical and thermal instabilities originate, propagate, and can be prevented through design considerations.

Topics of interest include, but are not limited to, the following:

• Atomistic simulations (AIMD, DFT) of electrolyte and SEI degradation;
• Modeling of gas venting, pressure buildup, and confinement effects;
• Flame-retardant additives, polymer electrolytes, and separators for thermal suppression;
• Physics-guided machine learning for safety prediction and diagnostics;
• Machine learning-guided electrolyte and interface material discovery;
• Safety screening methods for long-duration high-capacity storage (e.g., Na-ion, Zn-air);
• TR mitigation strategies for all-solid-state and flexible batteries.