Perspectives in Computational Catalysis: Data-Driven and Operando Approaches
Agenda
Towards Data-Driven Methodologies for Predictive Modelling of Catalytic Materials
The Nanomaterials Modelling group at Charles University develops atomistic simulation methods that integrate machine learning with quantum chemistry to achieve predictive insights into catalytic materials under realistic operating conditions. Earlier work has produced transferable reactive interatomic potentials for zeolites and oxide catalysts, accelerated rare-event sampling through dimensionality reduction and enhanced techniques, and established property predictors linking structural features to spectroscopic signatures. These tools have enabled the description of complex solvent–framework interactions, confined diffusion, and unforeseen reactive events beyond the reach of conventional ab initio approaches.
Current research extends this foundation with machine-learning-based property predictors (ranging from kernel ridge regression of NMR shifts to neural-network tensor prediction), rapid construction of case-specific interatomic potentials via foundational models, and efficient uncertainty quantification. In parallel, new pipelines for large-scale data processing and analysis are being implemented. The presentation will outline these methodological advances, demonstrate their synergy with application-oriented studies, and discuss their potential for predictive, operando-relevant modelling of catalytic systems in close collaboration with experiment.
Speaker: RNDr. Lukaš Grajciar, Ph.D.
Lukáš Grajciar received his MSc and PhD degrees in chemistry from the Charles University in Prague in 2009 and 2013, respectively, developing and applying dispersion-corrected DFT methods for adsorption in zeolites and metal-organic frameworks. At his postdoctoral position at Jena University in Germany (2013-2016), he become involved in development of high-performance algorithms for ab initio treatment of large molecules and periodic system within the TURBOMOLE program, including implementation of a new tool for global structure optimization of clusters in confinement. Since 2017, he is a researcher at the Charles University in Prague, investigating reactivity of zeolites using biased ab initio molecular dynamics.
Towards Operando Modelling of Oxide-based NanoCatalysts via Reactive Machine Learning
The Nanomaterials Modelling group at Charles University develops atomistic computational methods to investigate materials of high industrial relevance and to optimize already exploited systems. The advent of machine learning has enabled significant acceleration of simulations, the adoption of more realistic and complex material models, and more advanced spectroscopic characterisation.
This presentation highlights recent applications of machine-learning interatomic potentials and regression models to oxide-based catalytic materials. Case studies include the dynamics of confined water in porous aluminosilicate zeolites, the determination of aluminium siting in zeolites via solid-state NMR, and the role of alloying, oxidation, and defects in stabilising sub-nanometre bimetallic noble metal clusters. The results demonstrate that dynamical modelling with machine-learning methods uncovers unexpected binding modes, reproduces complex experimental NMR spectra with high fidelity, and provides mechanistic insight into sintering and oxidation processes at the smallest scales.
Speaker: Christopher James Heard, Ph.D.
Christopher Heard completed his B.A. and M.Sci. (2010) at the University of Cambridge, followed by PhD studies under Prof. Roy Johnston at the University of Birmingham (2014). There he developed and employed computational global optimisation tools for the determination of structures and electronic properties of free and oxide-supported metal clusters. This was followed up with a postdoctoral position at Chalmers University in Sweden, which involved the modelling of heterogeneous catalysis at metal and metal oxide interfaces, with atomistic ab- initio and microkinetic modelling techniques. As part of the CUCAM project at Charles University in Prague, his current research interests involve the stability and reactivity of zeolitic and layered oxide materials with ab-initio thermodynamic methods under realistic conditions, and the investigation of metal cluster encapsulation within nanoporous materials.
This course was supported by the Ministry of Education, Youth and Sports of the Czech Republic through the e-INFRA CZ (ID:90254).
All presentations and educational materials of this course are provided under the Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) license.
