Machine learning for electrocatalyst and photocatalyst design and discovery
Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels,
reducing the impact of global warming, and providing solutions to environmental pollution …
reducing the impact of global warming, and providing solutions to environmental pollution …
Bridging the complexity gap in computational heterogeneous catalysis with machine learning
Heterogeneous catalysis underpins a wide variety of industrial processes including energy
conversion, chemical manufacturing and environmental remediation. Significant advances …
conversion, chemical manufacturing and environmental remediation. Significant advances …
Open catalyst 2020 (OC20) dataset and community challenges
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 …
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
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 …
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 …
kinetics that controls catalyst performance, and microkinetic modeling is a tool that can help …
A critical review of machine learning of energy materials
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 …
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 …
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
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 …
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
Materials databases generated by high-throughput computational screening, typically using
density functional theory (DFT), have become valuable resources for discovering new …
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 …
economics to physics. In the area of materials science and computational heterogeneous …