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SUMMARY:Modelling chemical reactivity in condensed phase by machine learni
 ng potentials - Dr Veronika Juraskova (University of Oxford)
DTSTART;VALUE=DATE-TIME:20250603T140000
DTEND;VALUE=DATE-TIME:20250603T150000
UID:https://talks.ox.ac.uk/talks/id/c050b6a9-15a8-4255-b8e7-82a7023d744a/
DESCRIPTION:Dynamics and solvation effects are fundamental in modelling ch
 emical processes in liquid phases\, including homogeneous catalysis and bi
 ochemical reactions. The reaction environment critically influences the st
 ructure and stability of participating species\, thereby determining react
 ion rates\, selectivity\, and mechanistic pathways. Despite their importan
 ce\, accurate computational modelling of these effects remains challenging
 \, particularly for polar solvents\, where explicit solute-solvent interac
 tions must be captured at a high level of theory\, such as hybrid DFT and 
 beyond.\n\nIn this talk\, I will present our development of reactive machi
 ne learning interatomic potentials (MLIPs) designed specifically for model
 ing chemical processes in solution. Our methodology integrates automated a
 ctive learning with enhanced sampling techniques and descriptor-based stru
 cture selection to create data-efficient training sets that accurately rep
 roduce the DFT reference. By combining the Atomic Cluster Expansion framew
 ork with either linear regression or message-passing neural networks (MACE
 )\, we demonstrate how MLIPs significantly accelerate molecular dynamics s
 imulations of solution-phase reactions\, enabling the modelling of chemica
 l processes under experimentally relevant conditions.\n\nSpeakers:\nDr Ver
 onika Juraskova (University of Oxford)
LOCATION:Hume Rothery Lecture Theatre\, Hume Rothery Building\, Parks Road
 \, Oxford 
TZID:Europe/London
URL:https://talks.ox.ac.uk/talks/id/c050b6a9-15a8-4255-b8e7-82a7023d744a/
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DESCRIPTION:Talk:Modelling chemical reactivity in condensed phase by machi
 ne learning potentials - Dr Veronika Juraskova (University of Oxford)
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