Data-driven interfacial regulations through molecular additive screening for batteries and electrocatalysis

Area of Science:

• Electrochemistry
• Materials Science
• Computational Chemistry

Background:

• Molecular additives are crucial for controlling electrochemical interfaces in batteries and electrocatalysis.
• Current additive design lacks mechanistic understanding and cross-system applicability.
• Fragmented knowledge limits the rational design of molecular additives.

Purpose of the Study:

• To establish a unified framework for understanding molecular additive mechanisms across electrochemical systems.
• To propose a data-driven strategy for discovering and designing novel molecular additives.
• To bridge the gap between fundamental understanding and practical application of additives.

Main Methods:

• Identifying and analyzing three shared interfacial mechanisms: coordination remodeling, interfacial adsorption, and competitive inhibition.
• Summarizing a data-driven additive discovery route including data mining, predictive modeling, and optimization.
• Reviewing existing literature on additives in batteries and electrocatalysis.

Main Results:

• Additive effects in batteries (metal deposition, interphase engineering) and electrocatalysis (activity tuning, selectivity) are explained by the three shared mechanisms.
• A data-driven discovery pathway is outlined, enabling efficient screening and optimization.
• Common interfacial primitives are identified for organizing molecular additives.

Conclusions:

• A unified mechanistic framework based on shared interfacial primitives facilitates additive design across diverse electrochemical systems.
• Descriptor transferability is a key opportunity for advancing data-driven additive design.
• Cross-system benchmarks are needed to ensure the generalizability of additive design principles.