Perspectives in Computational Catalysis: Data-Driven and Operando Approaches
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F25%3A10260378" target="_blank" >RIV/61989100:27740/25:10260378 - isvavai.cz</a>
Result on the web
<a href="https://events.it4i.cz/event/368/" target="_blank" >https://events.it4i.cz/event/368/</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Perspectives in Computational Catalysis: Data-Driven and Operando Approaches
Original language description
The Nanomaterials Modelling group at Charles University developed atomistic simulation methods combining machine learning with quantum chemistry to study catalytic materials under realistic conditions and to investigate systems of high industrial relevance. Their work produced transferable reactive interatomic potentials for zeolites and oxide catalysts, accelerated rare-event sampling, and established property predictors linking structural features to spectroscopic signatures. Applications of machine-learning interatomic potentials and regression models included studies of confined water in aluminosilicate zeolites, aluminium siting determined by solid-state NMR, and the stabilisation of sub-nanometre bimetallic noble-metal clusters through alloying, oxidation, and defects. The results showed that machine-learning-driven dynamical modelling uncovered unexpected binding modes, reproduced complex experimental NMR spectra with high fidelity, and provided insight into sintering and oxidation processes, while ongoing research further extended these approaches with improved property predictors, rapid potential construction, uncertainty quantification, and large-scale data analysis pipelines.
Czech name
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Czech description
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Classification
Type
O - Miscellaneous
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
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Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů