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Deep-learning electronic structure calculations
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DOI：10.1038/s43588-025-00932-4.png)
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Abstract
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First-principles electronic structure calculations have profoundly advanced research in physics, chemistry and materials science, yet their further development remains constrained by the accuracy–efficiency dilemma. Here we highlight recent breakthroughs in deep-learning methodologies that address this challenge, including the deep-learning quantum Monte Carlo method for the accurate study of correlated electrons and deep-learning density functional theory for efficient large-scale material simulations. These advances extend the reach of first-principles calculations to unprecedented scales and complexity, enhancing the impact of quantum mechanics in scientific discovery. This Review explores the integration of deep learning in first-principles electronic structure calculations, addressing the accuracy–efficiency dilemma of traditional algorithms and extending first-principles methods to unprecedented scales and complexity.
Keywords:
deep learning
first-principles calculations
electronic structure
quantum Monte Carlo
density functional theory
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18.3
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3.1K
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4.0K
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