Journal
NATURE COMMUNICATIONS
Volume 11, Issue 1, Pages -Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41467-020-17265-7
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Funding
- U.S. Department of Energy, Office of Science, Basic Energy Sciences [DE-SC0001137, DE-SC0019394]
- Molecular Sciences Software Institute under NSF [ACI-1547580]
- National Science Foundation [1531492]
- U.S. Department of Energy (DOE) [DE-SC0001137] Funding Source: U.S. Department of Energy (DOE)
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Density functional theory (DFT) is the standard formalism to study the electronic structure of matter at the atomic scale. In Kohn-Sham DFT simulations, the balance between accuracy and computational cost depends on the choice of exchange and correlation functional, which only exists in approximate form. Here, we propose a framework to create density functionals using supervised machine learning, termed NeuralXC. These machine-learned functionals are designed to lift the accuracy of baseline functionals towards that provided by more accurate methods while maintaining their efficiency. We show that the functionals learn a meaningful representation of the physical information contained in the training data, making them transferable across systems. A NeuralXC functional optimized for water outperforms other methods characterizing bond breaking and excels when comparing against experimental results. This work demonstrates that NeuralXC is a first step towards the design of a universal, highly accurate functional valid for both molecules and solids. Increasing the non-locality of the exchange and correlation functional in DFT theory comes at a steep increase in computational cost. Here, the authors develop NeuralXC, a supervised machine learning approach to generate density functionals close to coupled-cluster level of accuracy yet computationally efficient.
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