Machine learning for a sustainable energy future

Z Yao, Y Lum, A Johnston, LM Mejia-Mendoza… - Nature Reviews …, 2023 - nature.com
Transitioning from fossil fuels to renewable energy sources is a critical global challenge; it
demands advances—at the materials, devices and systems levels—for the efficient …

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 …

The concept of active site in heterogeneous catalysis

C Vogt, BM Weckhuysen - Nature Reviews Chemistry, 2022 - nature.com
Catalysis is at the core of chemistry and has been essential to make all the goods
surrounding us, including fuels, coatings, plastics and other functional materials. In the near …

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 …

Molecular views on Fischer–Tropsch synthesis

KT Rommens, M Saeys - Chemical Reviews, 2023 - ACS Publications
For nearly a century, the Fischer–Tropsch (FT) reaction has been subject of intense debate.
Various molecular views on the active sites and on the reaction mechanism have been …

Interpretable machine learning for knowledge generation in heterogeneous catalysis

JA Esterhuizen, BR Goldsmith, S Linic - Nature catalysis, 2022 - nature.com
Most applications of machine learning in heterogeneous catalysis thus far have used black-
box models to predict computable physical properties (descriptors), such as adsorption or …

Electrochemical reduction of carbon dioxide to multicarbon (C 2+) products: challenges and perspectives

B Chang, H Pang, F Raziq, S Wang… - Energy & …, 2023 - pubs.rsc.org
Electrocatalytic CO2 reduction has been developed as a promising and attractive strategy to
achieve carbon neutrality for sustainable chemical production. Among various reduction …

Combining machine learning and computational chemistry for predictive insights into chemical systems

JA Keith, V Vassilev-Galindo, B Cheng… - Chemical …, 2021 - ACS Publications
Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …

Adsorption energy in oxygen electrocatalysis

J Zhang, HB Yang, D Zhou, B Liu - Chemical Reviews, 2022 - ACS Publications
Adsorption energy (AE) of reactive intermediate is currently the most important descriptor for
electrochemical reactions (eg, water electrolysis, hydrogen fuel cell, electrochemical …

Non-precious-metal catalysts for alkaline water electrolysis: operando characterizations, theoretical calculations, and recent advances

J Wang, Y Gao, H Kong, J Kim, S Choi… - Chemical Society …, 2020 - pubs.rsc.org
Recent years have witnessed an upsurge in the development of non-precious catalysts
(NPCs) for alkaline water electrolysis (AWE), especially with the strides made in …