Machine learning for electrocatalyst and photocatalyst design and discovery

H Mai, TC Le, D Chen, DA Winkler… - Chemical …, 2022 - ACS Publications
Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels,
reducing the impact of global warming, and providing solutions to environmental pollution …

Bridging the complexity gap in computational heterogeneous catalysis with machine learning

T Mou, HS Pillai, S Wang, M Wan, X Han… - Nature Catalysis, 2023 - nature.com
Heterogeneous catalysis underpins a wide variety of industrial processes including energy
conversion, chemical manufacturing and environmental remediation. Significant advances …

Open catalyst 2020 (OC20) dataset and community challenges

L Chanussot, A Das, S Goyal, T Lavril, M Shuaibi… - Acs …, 2021 - ACS Publications
Catalyst discovery and optimization is key to solving many societal and energy challenges
including solar fuel synthesis, long-term energy storage, and renewable fertilizer production …

Emerging Strategies for CO2 Photoreduction to CH4: From Experimental to Data‐Driven Design

S Cheng, Z Sun, KH Lim, TZH Gani… - Advanced Energy …, 2022 - Wiley Online Library
The solar‐energy‐driven photoreduction of CO2 has recently emerged as a promising
approach to directly transform CO2 into valuable energy sources under mild conditions. As a …

Microkinetic modeling: a tool for rational catalyst design

AH Motagamwala, JA Dumesic - Chemical Reviews, 2020 - ACS Publications
The design of heterogeneous catalysts relies on understanding the fundamental surface
kinetics that controls catalyst performance, and microkinetic modeling is a tool that can help …

A critical review of machine learning of energy materials

C Chen, Y Zuo, W Ye, X Li, Z Deng… - Advanced Energy …, 2020 - Wiley Online Library
Abstract Machine learning (ML) is rapidly revolutionizing many fields and is starting to
change landscapes for physics and chemistry. With its ability to solve complex tasks …

Machine learning for catalysis informatics: recent applications and prospects

T Toyao, Z Maeno, S Takakusagi, T Kamachi… - Acs …, 2019 - ACS Publications
The discovery and development of catalysts and catalytic processes are essential
components to maintaining an ecological balance in the future. Recent revolutions made in …

High-Entropy Alloys as Catalysts for the CO2 and CO Reduction Reactions

JK Pedersen, TAA Batchelor, A Bagger… - ACS catalysis, 2020 - ACS Publications
We present an approach for a probabilistic and unbiased discovery of selective and active
catalysts for the carbon dioxide (CO2) and carbon monoxide (CO) reduction reactions on …

Machine learned features from density of states for accurate adsorption energy prediction

V Fung, G Hu, P Ganesh, BG Sumpter - Nature communications, 2021 - nature.com
Materials databases generated by high-throughput computational screening, typically using
density functional theory (DFT), have become valuable resources for discovering new …

Machine learning for computational heterogeneous catalysis

P Schlexer Lamoureux, KT Winther… - …, 2019 - Wiley Online Library
Big data and artificial intelligence has revolutionized science in almost every field–from
economics to physics. In the area of materials science and computational heterogeneous …