Abstract
A key step in performing quantum dynamics for a chemical system is the reduction of dimensionality to allow a numerical treatment. Here, we introduce a machine learning approach for the (semi)automatic construction of reactive coordinates. After generating a meaningful data set from trajectory calculations, we train an autoencoder to find a low-dimensional set of non-linear coordinates for use in molecular quantum dynamics. We compare the wave packet dynamics of proton transfer reactions in both linear and non-linear coordinate spaces and find significant improvement for physical properties like reaction timescales.
Dokumententyp: | Zeitschriftenartikel |
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Fakultät: | Chemie und Pharmazie > Department Chemie |
Themengebiete: | 500 Naturwissenschaften und Mathematik > 540 Chemie |
ISSN: | 0302-9743 |
Sprache: | Englisch |
Dokumenten ID: | 83469 |
Datum der Veröffentlichung auf Open Access LMU: | 15. Dez. 2021, 15:08 |
Letzte Änderungen: | 15. Dez. 2021, 15:08 |