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 …
Rechargeable batteries of the future—the state of the art from a BATTERY 2030+ perspective
M Fichtner, K Edström, E Ayerbe… - Advanced Energy …, 2022 - Wiley Online Library
The development of new batteries has historically been achieved through discovery and
development cycles based on the intuition of the researcher, followed by experimental trial …
development cycles based on the intuition of the researcher, followed by experimental trial …
Descriptors for the evaluation of electrocatalytic reactions: d‐band theory and beyond
Abstract Closing the carbon‐, hydrogen‐, and nitrogen cycle with renewable electricity holds
promises for the mitigation of the facing environment and energy crisis, along with the …
promises for the mitigation of the facing environment and energy crisis, along with the …
Human-and machine-centred designs of molecules and materials for sustainability and decarbonization
Breakthroughs in molecular and materials discovery require meaningful outliers to be
identified in existing trends. As knowledge accumulates, the inherent bias of human intuition …
identified in existing trends. As knowledge accumulates, the inherent bias of human intuition …
Data‐Driven Materials Innovation and Applications
Owing to the rapid developments to improve the accuracy and efficiency of both
experimental and computational investigative methodologies, the massive amounts of data …
experimental and computational investigative methodologies, the massive amounts of data …
Toward excellence of electrocatalyst design by emerging descriptor‐oriented machine learning
Abstract Machine learning (ML) is emerging as a powerful tool for identifying quantitative
structure–activity relationships to accelerate electrocatalyst design by learning from historic …
structure–activity relationships to accelerate electrocatalyst design by learning from historic …
Catalyst Design for Electrolytic CO2 Reduction Toward Low-Carbon Fuels and Chemicals
Electrocatalytic CO2 reduction reaction (CO2RR) is an attractive way to simultaneously
convert CO2 into value-added fuels and chemicals as well as to store intermittent electricity …
convert CO2 into value-added fuels and chemicals as well as to store intermittent electricity …
[HTML][HTML] Adsorbate chemical environment-based machine learning framework for heterogeneous catalysis
Heterogeneous catalytic reactions are influenced by a subtle interplay of atomic-scale
factors, ranging from the catalysts' local morphology to the presence of high adsorbate …
factors, ranging from the catalysts' local morphology to the presence of high adsorbate …
Adsorption enthalpies for catalysis modeling through machine-learned descriptors
M Andersen, K Reuter - Accounts of Chemical Research, 2021 - ACS Publications
Conspectus Heterogeneous catalysts are rather complex materials that come in many
classes (eg, metals, oxides, carbides) and shapes. At the same time, the interaction of the …
classes (eg, metals, oxides, carbides) and shapes. At the same time, the interaction of the …