Solving Intractable Chemical Problems by Tensor Decomposition

Authors

  • Nina Glaser Department of Chemistry and Applied Biosciences, ETH Zürich, CH-8093 Zürich
  • Markus Reiher Department of Chemistry and Applied Biosciences, ETH Zürich, CH-8093 Zürich

DOI:

https://doi.org/10.2533/chimia.2024.215

PMID:

38676612

Keywords:

Compression, Machine Learning, Tensor decomposition, Tensor networks

Abstract

Many complex chemical problems encoded in terms of physics-based models become computationally intractable for traditional numerical approaches due to their unfavorable scaling with increasing molecular size. Tensor decomposition techniques can overcome such challenges by decomposing unattainably large numerical representations of chemical problems into smaller, tractable ones. In the first two decades of this century, algorithms based on such tensor factorizations have become state-of-the-art methods in various branches of computational chemistry, ranging from molecular quantum dynamics to electronic structure theory and machine learning. Here, we consider the role that tensor decomposition schemes have played in expanding the scope of computational chemistry. We relate some of the most prominent methods to their common underlying tensor network formalisms, providing a unified perspective on leading tensor-based approaches in chemistry and materials science.

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Published

2024-04-24