Machine Learning Adaptive Basis Sets for Efficient Large Scale Density Functional Theory Simulation.

ABSTRACT: It is chemically intuitive that an optimal atom centered basis set must adapt to its atomic environment, for example by polarizing toward nearby atoms. Adaptive basis sets of small size can be significantly more accurate than traditional atom centered basis sets of the same size. The small size and well conditioned nature of these basis sets leads to large saving in computational cost, in particular in a linear scaling framework. Here, it is shown that machine learning can be used to predict such adaptive basis sets using local geometrical information only. As a result, various properties of standard DFT calculations can be easily obtained at much lower costs, including nuclear gradients. In our approach, a rotationally invariant parametrization of the basis is obtained by employing a pote

SUBMITTER: Schutt O 

PROVIDER: S-EPMC6096449 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature