ArticleChemical science2023
Physics-inspired machine learning of localized intensive properties.
Article in Chemical science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Foundations of Gerophysics.Aging · 2026Article
- Cartesian equivariant representations for learning and understanding molecular orbitals.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Generalized convolutional many-body distribution functional representations.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- An end-to-end attention-based approach for learning on graphs.Nature communications · 2025Article
- Adapting hybrid density functionals with machine learning.Science advances · 2025Article
- Leave No Photon Behind: Artificial Intelligence in Multiscale Physics of Photocatalyst and Photoreactor Design.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Review
- Transfer learning with graph neural networks for improved molecular property prediction in the multi-fidelity setting.Nature communications · 2024Article
- Modelling local and general quantum mechanical properties with attention-based pooling.Communications chemistry · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
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