Quantum-Chemical Insights from Interpretable Atomistic Neural Networks

Kristof T. Schütt, Michael Gastegger, Alexandre Tkatchenko, Klaus Robert Müller

Research output: Chapter in Book/Report/Conference proceedingChapter

17 Citations (Scopus)

Abstract

With the rise of deep neural networks for quantum chemistry applications, there is a pressing need for architectures that, beyond delivering accurate predictions of chemical properties, are readily interpretable by researchers. Here, we describe interpretation techniques for atomistic neural networks on the example of Behler–Parrinello networks as well as the end-to-end model SchNet. Both models obtain predictions of chemical properties by aggregating atom-wise contributions. These latent variables can serve as local explanations of a prediction and are obtained during training without additional cost. Due to their correspondence to well-known chemical concepts such as atomic energies and partial charges, these atom-wise explanations enable insights not only about the model but more importantly about the underlying quantum-chemical regularities. We generalize from atomistic explanations to 3d space, thus obtaining spatially resolved visualizations which further improve interpretability. Finally, we analyze learned embeddings of chemical elements that exhibit a partial ordering that resembles the order of the periodic table. As the examined neural networks show excellent agreement with chemical knowledge, the presented techniques open up new venues for data-driven research in chemistry, physics and materials science.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages311-330
Number of pages20
DOIs
Publication statusPublished - 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11700 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Bibliographical note

Funding Information:
This work was supported by the Federal Ministry of Education and Research (BMBF) for the Berlin Big Data Center BBDC (01IS14013A) and the Berlin Center for Machine Learning (01IS18037A). Additional support was provided by the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement NO 792572. This research was supported by Institute for Information & Communications Technology Promotion and funded by the Korea government (MSIT) (No. 2017-0-00451, No. 2017-0-01779). A.T. acknowledges support from the European Research Council (ERC-CoG grant BeStMo).

Publisher Copyright:
© Springer Nature Switzerland AG 2019.

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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