Laboratory of AI for
Electrochemistry

Relay catalysis integrates multiple catalytic reactions to efficiently transform intermediates and enhance conversion and selectivity. However, designing these pathways and multifunctional catalysts is often lengthy and costly, heavily relying on in-depth literature analysis by experienced researchers. To address this, we developed an approach that combines a knowledge graph (KG) and large language models (LLMs) to automatically recommend multistep catalytic reaction pathways. Our method involves using an LLM-assisted workflow for data acquisition and organization, followed by the construction of a detailed catalysis knowledge graph (Cat-KG). After querying the Cat-KG, promising relay catalysis pathways are identified by applying scoring rules informed by expertise in relay catalysis. The LLM then transforms the structured pathways and reaction condition data into readable chemical equations and descriptions for chemists. This step integrates catalysis knowledge from the Cat-KG and helps avoid LLM-induced hallucinations by using reliable information. The method efficiently recommended relay catalysis pathways for ethylene, ethanol, 2,5-furandicarboxylate and other targets within minutes, identifying pathways consistent with reported ones while using different reaction conditions, validating its effectiveness. Thus, this strategy can extrapolate known and novel relay catalysis pathways, showcasing its potential for application in pathway selection.

Nanoconfinement of aqueous electrolytes is ubiquitous in geological, biological, and technological contexts, including sedimentary rocks, water channel proteins, and applications like desalination and water purification membranes. The structure and properties of water in nanoconfinement can differ significantly from bulk water, exhibiting, for instance, modified hydrogen bonds, altered dielectric constant, and distinct phase transitions.

Understanding electrochemical interfaces at a microscopic level is essential for elucidating important electrochemical processes in electrocatalysis, batteries, and corrosion. While ab initio simulations have provided valuable insights into model systems, the high computational cost limits their use in tackling complex systems of relevance to practical applications. Machine learning potentials offer a solution, but their application in electrochemistry remains challenging due to the difficulty in treating the dielectric response of electronic conductors and insulators simultaneously.

Understanding the solvation structure of electrolytes is critical for optimizing the electrochemical performance of rechargeable batteries as it directly influences properties such as ionic conductivity, viscosity, and electrochemical stability. The highly complex structures and strong interactions in high-concentration electrolytes make accurate modeling and interpretation of their “structure–property” relationships even more challenging with spectroscopic methods.