From computational screening to the synthesis of a promising OER catalyst

• Published in: Chemical science (Cambridge) 2024-07, Vol.15 (27), p.1556-157
• Main Authors: Hari Kumar, Sai Govind, Bozal-Ginesta, Carlota, Wang, Ning, Abed, Jehad, Shan, Chung Hsuan, Yao, Zhenpeng, Aspuru-Guzik, Alan
• Format: Article
• Language: English
• Subjects: Catalysts; Chemical synthesis; Chemistry; Electronic structure; Machine learning; Oxygen evolution reactions; Screening; Water splitting; Workflow
• ISSN: 2041-6520; 2041-6539
• DOI: 10.1039/d4sc00192c

The search for new materials can be laborious and expensive. Given the challenges that mankind faces today concerning the climate change crisis, the need to accelerate materials discovery for applications like water-splitting could be very relevant for a renewable economy. In this work, we introduce a computational framework to predict the activity of oxygen evolution reaction (OER) catalysts, in order to accelerate the discovery of materials that can facilitate water splitting. We use this framework to screen 6155 ternary-phase spinel oxides and have isolated 33 candidates which are predicted to have potentially high OER activity. We have also trained a machine learning model to predict the binding energies of the *O, *OH and *OOH intermediates calculated within this workflow to gain a deeper understanding of the relationship between electronic structure descriptors and OER activity. Out of the 33 candidates predicted to have high OER activity, we have synthesized three compounds and characterized them using linear sweep voltammetry to gauge their performance in OER. From these three catalyst materials, we have identified a new material, Co 2.5 Ga 0.5 O 4 , that is competitive with benchmark OER catalysts in the literature with a low overpotential of 220 mV at 10 mA cm −2 and a Tafel slope at 56.0 mV dec −1 . Given the vast size of chemical space as well as the success of this technique to date, we believe that further application of this computational framework based on the high-throughput virtual screening of materials can lead to the discovery of additional novel, high-performing OER catalysts. The search for new materials can be laborious and expensive. With computational inverse design, material properties can be predicted before synthesis, thus accelerating materials discovery.