High-throughput screening and machine learning for the discovery and synthesis of iron-based electrocatalysts for oxygen evolution reaction

The OER is a key reaction in water splitting, crucial for green hydrogen production. Currently, precious-metal catalysts like IrO2 and RuO2 are effective but prohibitively expensive and rare, limiting their application. Iron-based electrocatalysts, given their abundance and favorable catalytic properties, offer a promising alternative but require further optimization to achieve performance levels competitive with state-of-the-art catalysts.
The discovery of these new materials traditionally relies on trial-and-error experimentation, which can be time-consuming and resource-intensive. However, advances in ML and density functional theory (DFT) calculations accelerate the virtual discovery by fast acquisition of catalytic performance and synthesis conditions computationally. We can quickly explore a vast chemical space and exploit towards the identification of the most promising iron-based materials for OER and optimizing their synthesis parameters for successful experimental validation.
The aim of this project is to accelerate the discovery and optimization of iron-based electrocatalysts for the Oxygen Evolution Reaction (OER) using high-throughput computational screening and machine learning (ML) models. This project focuses on predicting both the catalytic properties and the synthesis conditions of potential catalysts, reducing experimental trial-and-error, aiming to discover novel materials not previously published and allowing for the validation of only the most promising candidates.

Faculty Supervisor:

Bruno Pollet

Student:

Partner:

Massachusetts Institute of Technology

Discipline:

Engineering

Sector:

Education

University:

Université du Québec à Trois-Rivières

Program: