# Computational Modeling and Experimental Approaches for Understanding the Mechanisms of [FeFe]‐Hydrogenase

## TL;DR

This paper reviews how computational modeling and experiments help understand [FeFe]-hydrogenase, a natural catalyst for hydrogen production, to inspire better artificial catalysts.

## Contribution

The paper provides a synthesis of recent computational and experimental insights into [FeFe]-hydrogenase structure and function.

## Key findings

- Computational modeling is effective in elucidating the reduction chemistry of [FeFe]-hydrogenases.
- DFT calculations have advanced understanding of active site identification and hydrogen metabolism reaction cycles.
- Hydrogenase structures and mechanisms are key to designing high-performance artificial catalysts.

## Abstract

Learning from nature has emerged as a promising strategy for catalyst development, wherein the remarkable performance of catalysts selected by nature over billions of years of evolution serves as a basis for the creative design of high‐performance catalysts. Hydrogenases, with their exceptional catalytic activity in hydrogen oxidation and production, have been employed as prototypes for human learning to achieve better catalyst design. A comprehensive understanding of hydrogenases' structures and catalytic mechanisms is crucial to replicate and exceed their performance. Computational modeling has proven to be a powerful tool for elucidating the reduction chemistry of [FeFe]‐hydrogenases. This review overviews recent computational and experimental efforts, focusing on density functional theory (DFT) calculations applied to [FeFe] hydrogenases. It summarizes current knowledge on identifying active sites in [FeFe] hydrogenases and the reaction cycles involved in hydrogen metabolism.

A comprehensive understanding of hydrogenases' structures and catalytic mechanisms is crucial to replicate and exceed their performance. Computational modeling has proven to be a powerful tool for elucidating the reduction chemistry of [FeFe]‐hydrogenases. This review overviews recent computational and experimental efforts, focusing on DFT calculations applied to [FeFe] hydrogenases. It summarizes current knowledge on identifying active sites in [FeFe] hydrogenases and the reaction cycles involved in hydrogen metabolism.