Advanced electrocatalysts with unusual active sites for electrochemical water splitting
Electrochemical water splitting represents a promising technology for green hydrogen
production. To design advanced electrocatalysts, it is crucial to identify their active sites and …
production. To design advanced electrocatalysts, it is crucial to identify their active sites and …
Operando modeling of zeolite-catalyzed reactions using first-principles molecular dynamics simulations
Within this Perspective, we critically reflect on the role of first-principles molecular dynamics
(MD) simulations in unraveling the catalytic function within zeolites under operating …
(MD) simulations in unraveling the catalytic function within zeolites under operating …
Machine Learning Big Data Set Analysis Reveals C–C Electro-Coupling Mechanism
Carbon–carbon (C–C) coupling is essential in the electrocatalytic reduction of CO2 for the
production of green chemicals. However, due to the complexity of the reaction network, there …
production of green chemicals. However, due to the complexity of the reaction network, there …
Catalysis in the digital age: Unlocking the power of data with machine learning
The design and discovery of new and improved catalysts are driving forces for accelerating
scientific and technological innovations in the fields of energy conversion, environmental …
scientific and technological innovations in the fields of energy conversion, environmental …
Comprehensive exploration of graphically defined reaction spaces
Existing reaction transition state (TS) databases are comparatively small and lack chemical
diversity. Here, this data gap has been addressed using the concept of a graphically-defined …
diversity. Here, this data gap has been addressed using the concept of a graphically-defined …
Accurate transition state generation with an object-aware equivariant elementary reaction diffusion model
Transition state search is key in chemistry for elucidating reaction mechanisms and
exploring reaction networks. The search for accurate 3D transition state structures, however …
exploring reaction networks. The search for accurate 3D transition state structures, however …
Machine learning approaches for monitoring environmental metal pollutants: Recent advances in source apportionment, detection, quantification, and risk assessment …
Metal pollutants pose significant and enduring threats to human health and the environment,
mainly due to their non-biodegradable nature. Traditional monitoring of these pollutants …
mainly due to their non-biodegradable nature. Traditional monitoring of these pollutants …
A human-machine interface for automatic exploration of chemical reaction networks
Autonomous reaction network exploration algorithms offer a systematic approach to explore
mechanisms of complex chemical processes. However, the resulting reaction networks are …
mechanisms of complex chemical processes. However, the resulting reaction networks are …
Machine-learning driven global optimization of surface adsorbate geometries
The adsorption energies of molecular adsorbates on catalyst surfaces are key descriptors in
computational catalysis research. For the relatively large reaction intermediates frequently …
computational catalysis research. For the relatively large reaction intermediates frequently …
Machine Learning‐Assisted Design of Nitrogen‐Rich Covalent Triazine Frameworks Photocatalysts
Covalent triazine frameworks (CTFs), noted for their rich nitrogen content, have attracted
significant attention as promising photocatalysts. However, the structural complexity …
significant attention as promising photocatalysts. However, the structural complexity …