A self-refining training loop, where a neural network samples molecular conformations from its own predicted energy and trains on them, reduces the need for large labeled DFT datasets in amortized density functional theory.
Unifying machine learning and quantum chemistry -- a deep neural network for molecular wavefunctions
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abstract
Machine learning advances chemistry and materials science by enabling large-scale exploration of chemical space based on quantum chemical calculations. While these models supply fast and accurate predictions of atomistic chemical properties, they do not explicitly capture the electronic degrees of freedom of a molecule, which limits their applicability for reactive chemistry and chemical analysis. Here we present a deep learning framework for the prediction of the quantum mechanical wavefunction in a local basis of atomic orbitals from which all other ground-state properties can be derived. This approach retains full access to the electronic structure via the wavefunction at force field-like efficiency and captures quantum mechanics in an analytically differentiable representation. On several examples, we demonstrate that this opens promising avenues to perform inverse design of molecular structures for target electronic property optimisation and a clear path towards increased synergy of machine learning and quantum chemistry.
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2025 1verdicts
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Self-Refining Training for Amortized Density Functional Theory
A self-refining training loop, where a neural network samples molecular conformations from its own predicted energy and trains on them, reduces the need for large labeled DFT datasets in amortized density functional theory.