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Physics-Inspired Machine Learning of Localized Intensive Properties

Title data

Chen, Ke ; Kunkel, Christian ; Cheng, Bingqing ; Reuter, Karsten ; Margraf, Johannes T.:
Physics-Inspired Machine Learning of Localized Intensive Properties.
In: Chemical Science. Vol. 14 (2023) Issue 18 . - pp. 4913-4922.
ISSN 2041-6539
DOI: https://doi.org/10.1039/D3SC00841J

Abstract in another language

Machine learning (ML) has been widely applied to chemical property prediction, most prominently for the energies and forces in molecules and materials. The strong interest in predicting energies in particular has led to a ‘local energy’-based paradigm for modern atomistic ML models, which ensures size-extensivity and a linear scaling of computational cost with system size. However, many electronic properties (such as excitation energies or ionization energies) do not necessarily scale linearly with system size and may even be spatially localized. Using size-extensive models in these cases can lead to large errors. In this work, we explore different strategies for learning intensive and localized properties, using HOMO energies in organic molecules as a representative test case. In particular, we analyze the pooling functions that atomistic neural networks use to predict molecular properties, and suggest an orbital weighted average (OWA) approach that enables the accurate prediction of orbital energies and locations.

Further data

Item Type: Article in a journal
Refereed: Yes
Institutions of the University: Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Chemistry > Chair Künstliche Intelligenz in der physiko-chemischen Materialanalytik
Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Chemistry > Chair Künstliche Intelligenz in der physiko-chemischen Materialanalytik > Chair Künstliche Intelligenz in der physiko-chemischen Materialanalytik - Univ.-Prof. Dr. Johannes Theo Margraf
Result of work at the UBT: No
DDC Subjects: 500 Science > 540 Chemistry
Date Deposited: 13 Nov 2023 12:23
Last Modified: 13 Nov 2023 12:23
URI: https://eref.uni-bayreuth.de/id/eprint/87664