These are preliminary reports that have not been peer-reviewed. They should not be regarded as conclusive, guide clinical practice/health-related behavior, or be reported in news media as established information. For more information, please see our FAQs.
manuscript.pdf (1.41 MB)

Size-Extensive Molecular Machine Learning with Global Descriptors

submitted on 18.10.2019, 11:44 and posted on 22.10.2019, 21:38 by Hyunwook Jung, Sina Stocker, Christian Kunkel, Harald Oberhofer, Byungchan Han, Karsten Reuter, Johannes T. Margraf

Machine learning (ML) models are increasingly used to predict molecular prop- erties in a high-throughput setting at a much lower computational cost than con- ventional electronic structure calculations. Such ML models require descriptors that encode the molecular structure in a vector. These descriptors are generally designed to respect the symmetries and invariances of the target property. However, size- extensivity is usually not guaranteed for so-called global descriptors. In this contri- bution, we show how extensivity can be build into ML models with global descriptors such as the Many-Body Tensor Representation. Properties of extensive and non- extensive models for the atomization energy are systematically explored by training on small molecules and testing on small, medium and large molecules. Our result shows that the non-extensive model is only useful in the size-range of its training set, whereas the extensive models provide reasonable predictions across large size differences. Remaining sources of error for the extensive models are discussed.


Email Address of Submitting Author


Technische Universität München



ORCID For Submitting Author


Declaration of Conflict of Interest

no conflict of interest