Publication: Subsystem Density Functional Theory Augmented by a Delta Learning Approach to Achieve Kohn–Sham Accuracy
Subsystem Density Functional Theory Augmented by a Delta Learning Approach to Achieve Kohn–Sham Accuracy
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Pauletti, M., Rybkin, V. V., & Iannuzzi, M. (2021). Subsystem Density Functional Theory Augmented by a Delta Learning Approach to Achieve Kohn–Sham Accuracy. Journal of Chemical Theory and Computation, 17(10), 6423–6431. https://doi.org/10.1021/acs.jctc.1c00592
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Simulations based on electronic structure theory naturally include polarization and have no transferability problems. In particular, Kohn–Sham density functional theory (KS-DFT) has become the method of reference for ab initio molecular dynamics simulations of condensed matter systems. However, the high computational cost often poses strict limits on the affordable system size as well as on the extension of sampling (number of configurations). In this work, we propose an improvement to the subsystem density functional theory approach,
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Pauletti, M., Rybkin, V. V., & Iannuzzi, M. (2021). Subsystem Density Functional Theory Augmented by a Delta Learning Approach to Achieve Kohn–Sham Accuracy. Journal of Chemical Theory and Computation, 17(10), 6423–6431. https://doi.org/10.1021/acs.jctc.1c00592