Machine learning for a sustainable energy future
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 …
demands advances—at the materials, devices and systems levels—for the efficient …
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 …
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 …
surrounding us, including fuels, coatings, plastics and other functional materials. In the near …
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 …
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 …
Various molecular views on the active sites and on the reaction mechanism have been …
Interpretable machine learning for knowledge generation in heterogeneous catalysis
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 …
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
Electrocatalytic CO2 reduction has been developed as a promising and attractive strategy to
achieve carbon neutrality for sustainable chemical production. Among various reduction …
achieve carbon neutrality for sustainable chemical production. Among various reduction …
Combining machine learning and computational chemistry for predictive insights into chemical systems
Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …
by dramatically accelerating computational algorithms and amplifying insights available from …
Adsorption energy in oxygen electrocatalysis
Adsorption energy (AE) of reactive intermediate is currently the most important descriptor for
electrochemical reactions (eg, water electrolysis, hydrogen fuel cell, electrochemical …
electrochemical reactions (eg, water electrolysis, hydrogen fuel cell, electrochemical …
Non-precious-metal catalysts for alkaline water electrolysis: operando characterizations, theoretical calculations, and recent advances
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 …
(NPCs) for alkaline water electrolysis (AWE), especially with the strides made in …