Journal
PHYSICAL REVIEW LETTERS
Volume 108, Issue 25, Pages -Publisher
AMER PHYSICAL SOC
DOI: 10.1103/PhysRevLett.108.253002
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Funding
- NSF [CHE-1112442]
- NRF Korea [2010-220-C00017, R31-10008]
- EU PASCAL2
- DFG [MU 987/4-2]
- EU Marie Curie [273039]
- Direct For Mathematical & Physical Scien [1112442] Funding Source: National Science Foundation
- Division Of Chemistry [1112442] Funding Source: National Science Foundation
- National Research Foundation of Korea [R31-2012-000-10008-0, 220-2010-1-C00017] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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Machine learning is used to approximate density functionals. For the model problem of the kinetic energy of noninteracting fermions in 1D, mean absolute errors below 1 kcal/mol on test densities similar to the training set are reached with fewer than 100 training densities. A predictor identifies if a test density is within the interpolation region. Via principal component analysis, a projected functional derivative finds highly accurate self-consistent densities. The challenges for application of our method to real electronic structure problems are discussed.
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