Electronic structure at coarse-grained resolutions from supervised machine learning

Nicholas E. Jackson, Alec S. Bowen, Lucas W. Antony, Michael A. Webb, Venkatram Vishwanath, Juan J. de Pablo

Research output: Contribution to journalArticle

9 Scopus citations

Abstract

Computational studies aimed at understanding conformationally dependent electronic structure in soft materials require a combination of classical and quantum-mechanical simulations, for which the sampling of conformational space can be particularly demanding. Coarse-grained (CG) models provide a means of accessing relevant time scales, but CG configurations must be back-mapped into atomistic representations to perform quantum-chemical calculations, which is computationally intensive and inconsistent with the spatial resolution of the CG models. A machine learning approach, denoted as artificial neural network electronic coarse graining (ANN-ECG), is presented here in which the conformationally dependent electronic structure of a molecule is mapped directly to CG pseudo-atom configurations. By averaging over decimated degrees of freedom, ANN-ECG accelerates simulations by eliminating backmapping and repeated quantum-chemical calculations. The approach is accurate, consistent with the CG spatial resolution, and can be used to identify computationally optimal CG resolutions.

Original languageEnglish (US)
Article numbereaav1190
JournalScience advances
Volume5
Issue number3
DOIs
StatePublished - 2019
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Physics and Astronomy (miscellaneous)
  • General

Fingerprint Dive into the research topics of 'Electronic structure at coarse-grained resolutions from supervised machine learning'. Together they form a unique fingerprint.

  • Cite this

    Jackson, N. E., Bowen, A. S., Antony, L. W., Webb, M. A., Vishwanath, V., & de Pablo, J. J. (2019). Electronic structure at coarse-grained resolutions from supervised machine learning. Science advances, 5(3), [eaav1190]. https://doi.org/10.1126/sciadv.aav1190