Machine learning
Machine learning:
Machine learning is a method to interpolate between known data by fitting the correlations of a system. This computational tool seems to keep coming up in our research, so we have listed it here with its own page.
Note that machine learning techniques (by kernels or networks) are able to capture a broad range of correlations, far broader than the local correlations where tensor networks are most useful (i.e., for local interactions). Tensor network informed machine learning tends to under-perform regular gradient based neural network training, so we do not combine the two very much.
With quantum computing:
T.E. Baker and D. Poulin, "Density functionals and Kohn-Sham potentials with minimal wavefunction preparations on a quantum computer" Phys. Rev. Research 2, 043238 (2020) [online] [arxiv:2008.05592] [pdf] [bibtex]
With density functional theory:
J. Hollingsworth, L. Li (李力), T.E. Baker, and K. Burke, "Can exact conditions improve machine-learned density functionals?" J. Chem. Phys. 148, 241743 (2018) [online] [pdf] [bibtex]
Doctoral thesis, Methods of Calculation with the Exact Density Functional using the Renormalization Group (2017) [online] [pdf] [bibtex]
A. Tkatchenko, M. Afzal, C, Anderson, T. Baker, R. Banisch, S. Chiama, C. Draxl, M. Haghighatlari, F. Heidar-Zadeh, M. Hirn, J. Hoja, O. Isayev, R. Kondor, L. Li, Y. Li, G. Martyna, M. Meila, K.S. Ruiz, M. Rupp, H. Sauceda, A. Shapeev, M. Stöhr, K.-R. Müller, S. Shankar, Recent Progress and Open Problems--Program on Machine Learning & Many-Particle Systems [online] [pdf] [bibtex]
L. Li (李力), T.E. Baker, S.R. White, and K. Burke, "Pure density functional for strong correlations and the thermodynamic limit from machine learning" Phys. Rev. B 94, 245129 (2016) [online] [arxiv:1609.03705] [pdf] [bibtex]
Learning ocean currents in Avila Bay: