Semantic Scholar Open Access 2011 500 sitasi

Finding Density Functionals with Machine Learning

John C. Snyder M. Rupp K. Hansen K. Müller K. Burke

Lihat Sumber DOI

Abstrak

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.

Topik & Kata Kunci

Computer Science Physics Mathematics Medicine

Penulis (5)

John C. Snyder

M. Rupp

K. Hansen

K. Müller

K. Burke

Format Sitasi

APA MLA BibTeX

Snyder, J.C., Rupp, M., Hansen, K., Müller, K., Burke, K. (2011). Finding Density Functionals with Machine Learning. https://doi.org/10.1103/PhysRevLett.108.253002

Akses Cepat

Lihat di Sumber doi.org/10.1103/PhysRevLett.108.253002

Informasi Jurnal

Tahun Terbit: 2011
Bahasa: en
Total Sitasi: 500×
Sumber Database: Semantic Scholar
DOI: 10.1103/PhysRevLett.108.253002
Akses: Open Access ✓