TY - JOUR
T1 - DeePKS
T2 - A Comprehensive Data-Driven Approach toward Chemically Accurate Density Functional Theory
AU - Chen, Yixiao
AU - Zhang, Linfeng
AU - Wang, Han
AU - Weinan, E.
N1 - Publisher Copyright:
© 2020 American Chemical Society.
PY - 2021/1/12
Y1 - 2021/1/12
N2 - We propose a general machine learning-based framework for building an accurate and widely applicable energy functional within the framework of generalized Kohn-Sham density functional theory. To this end, we develop a way of training self-consistent models that are capable of taking large datasets from different systems and different kinds of labels. We demonstrate that the functional that results from this training procedure gives chemically accurate predictions on energy, force, dipole, and electron density for a large class of molecules. It can be continuously improved when more and more data are available.
AB - We propose a general machine learning-based framework for building an accurate and widely applicable energy functional within the framework of generalized Kohn-Sham density functional theory. To this end, we develop a way of training self-consistent models that are capable of taking large datasets from different systems and different kinds of labels. We demonstrate that the functional that results from this training procedure gives chemically accurate predictions on energy, force, dipole, and electron density for a large class of molecules. It can be continuously improved when more and more data are available.
UR - http://www.scopus.com/inward/record.url?scp=85097338860&partnerID=8YFLogxK
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U2 - 10.1021/acs.jctc.0c00872
DO - 10.1021/acs.jctc.0c00872
M3 - Article
C2 - 33296197
AN - SCOPUS:85097338860
SN - 1549-9618
VL - 17
SP - 170
EP - 181
JO - Journal of Chemical Theory and Computation
JF - Journal of Chemical Theory and Computation
IS - 1
ER -