Quantum-chemical insights from interpretable atomistic neural networks

By Kristof T. Schütt, Michael Gastegger, Alexandre Tkatchenko, K. Müller. 2018

35 citations recorded by the source corpus.

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.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology

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Citations in the corpus

A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI (2019, background): “[66] introduced atomistic neural network architecture, in which each atom is like a node in a graph with a set of feature vectors.”
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications (2021, background): “The work of [165] and [166] showed that accurate predictions are possible and obtained also physical meaningful insights from the model.”
Toward Interpretable Machine Learning: Transparent Deep Neural Networks and Beyond (2020, unknown): “The work of [127], [128]”