Reducing the Quantum Many-electron Problem to Two Electrons with Machine Learning

Abstract

An outstanding challenge in chemical computation is the many-electron problem where computational methodologies scale prohibitively with system size. The energy of any molecule can be expressed as a weighted sum of the energies of two-electron wave functions that are computable from only a two-electron calculation. Despite the physical elegance of this extended ``aufbau'' principle, the determination of the distribution of weights -- geminal occupations -- for general molecular systems has remained elusive. Here we introduce a new paradigm for electronic structure where approximate geminal-occupation distributions are ``learned'' via a convolutional neural network. We show that the neural network learns the $N$-representability conditions, constraints on the distribution for it to represent an $N$-electron system. By training on hydrocarbon isomers with only 2-7 carbon atoms, we are able to predict the energies for isomers of octane as well as hydrocarbons with 8-15 carbons. The present work demonstrates that machine learning can be used to reduce the many-electron problem to an effective two-electron problem, opening new opportunities for accurately predicting electronic structure.

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Cited by in corpus (5)

• Data-driven Refinement of Electronic Energies from Two-Electron Reduced-Density-Matrix Theory
• Softmax parameterization of the occupation numbers for natural orbital functionals based on electron pairing approaches
• Analogy between Boltzmann machines and Feynman path integrals
• Learning Minimal Representations of Fermionic Ground States
• Refining ensemble $N$-representability of one-body density matrices from partial information