DeePKS: A Comprehensive Data-Driven Approach toward Chemically Accurate Density Functional Theory
Abstract
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. Here, 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.
- Authors:
-
[1];
[1];
[2];
- Princeton University, NJ (United States)
- Institute of Applied Physics and Computational Mathematics, Beijing (China)
- Publication Date:
- Research Org.:
- Princeton Univ., NJ (United States)
- Sponsoring Org.:
- USDOE; National Science Foundation of China; National Key Research and Development Program of China
- OSTI Identifier:
- 1999126
- Grant/Contract Number:
- SC0019394; N00014-13-1-0338; 11871110; 2016YFB0201200; 2016YFB0201203
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Journal of Chemical Theory and Computation
- Additional Journal Information:
- Journal Volume: 17; Journal Issue: 1; Journal ID: ISSN 1549-9618
- Publisher:
- American Chemical Society
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; Aromatic compounds; Density functional theory; Energy; Hydrocarbons; Molecules
Citation Formats
Chen, Yixiao, Zhang, Linfeng, Wang, Han, and E, Weinan. DeePKS: A Comprehensive Data-Driven Approach toward Chemically Accurate Density Functional Theory. United States: N. p., 2020.
Web. doi:10.1021/acs.jctc.0c00872.
Chen, Yixiao, Zhang, Linfeng, Wang, Han, & E, Weinan. DeePKS: A Comprehensive Data-Driven Approach toward Chemically Accurate Density Functional Theory. United States. https://doi.org/10.1021/acs.jctc.0c00872
Chen, Yixiao, Zhang, Linfeng, Wang, Han, and E, Weinan. Wed .
"DeePKS: A Comprehensive Data-Driven Approach toward Chemically Accurate Density Functional Theory". United States. https://doi.org/10.1021/acs.jctc.0c00872. https://www.osti.gov/servlets/purl/1999126.
@article{osti_1999126,
title = {DeePKS: A Comprehensive Data-Driven Approach toward Chemically Accurate Density Functional Theory},
author = {Chen, Yixiao and Zhang, Linfeng and Wang, Han and E, Weinan},
abstractNote = {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. Here, 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.},
doi = {10.1021/acs.jctc.0c00872},
journal = {Journal of Chemical Theory and Computation},
number = 1,
volume = 17,
place = {United States},
year = {Wed Dec 09 00:00:00 EST 2020},
month = {Wed Dec 09 00:00:00 EST 2020}
}
Free Publicly Available Full Text
Publisher's Version of Record
Other availability
Search WorldCat to find libraries that may hold this journal
Save to My Library
You must Sign In or Create an Account in order to save documents to your library.
Works referenced in this record:
Generalized Gradient Approximation Made Simple
journal, October 1996
- Perdew, John P.; Burke, Kieron; Ernzerhof, Matthias
- Physical Review Letters, Vol. 77, Issue 18, p. 3865-3868
Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
journal, January 2012
- Rupp, Matthias; Tkatchenko, Alexandre; Müller, Klaus-Robert
- Physical Review Letters, Vol. 108, Issue 5
Ground State Energy Functional with Hartree–Fock Efficiency and Chemical Accuracy
journal, August 2020
- Chen, Yixiao; Zhang, Linfeng; Wang, Han
- The Journal of Physical Chemistry A, Vol. 124, Issue 35
Quadratic configuration interaction. A general technique for determining electron correlation energies
journal, November 1987
- Pople, John A.; Head‐Gordon, Martin; Raghavachari, Krishnan
- The Journal of Chemical Physics, Vol. 87, Issue 10
Finding Density Functionals with Machine Learning
journal, June 2012
- Snyder, John C.; Rupp, Matthias; Hansen, Katja
- Physical Review Letters, Vol. 108, Issue 25
Improving the Performance of Long-Range-Corrected Exchange-Correlation Functional with an Embedded Neural Network
journal, September 2017
- Liu, Qin; Wang, JingChun; Du, PengLi
- The Journal of Physical Chemistry A, Vol. 121, Issue 38
P y SCF: the Python-based simulations of chemistry framework : The PySCF program
journal, September 2017
- Sun, Qiming; Berkelbach, Timothy C.; Blunt, Nick S.
- Wiley Interdisciplinary Reviews: Computational Molecular Science, Vol. 8, Issue 1
ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost
journal, January 2017
- Smith, J. S.; Isayev, O.; Roitberg, A. E.
- Chemical Science, Vol. 8, Issue 4
Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning
journal, July 2019
- Smith, Justin S.; Nebgen, Benjamin T.; Zubatyuk, Roman
- Nature Communications, Vol. 10, Issue 1
FCHL revisited: Faster and more accurate quantum machine learning
journal, January 2020
- Christensen, Anders S.; Bratholm, Lars A.; Faber, Felix A.
- The Journal of Chemical Physics, Vol. 152, Issue 4
Generalized Gradient Approximation That Recovers the Second-Order Density-Gradient Expansion with Optimized Across-the-Board Performance
journal, July 2011
- Peverati, Roberto; Zhao, Yan; Truhlar, Donald G.
- The Journal of Physical Chemistry Letters, Vol. 2, Issue 16
Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach
journal, April 2015
- Ramakrishnan, Raghunathan; Dral, Pavlo O.; Rupp, Matthias
- Journal of Chemical Theory and Computation, Vol. 11, Issue 5
Solving many-electron Schrödinger equation using deep neural networks
journal, December 2019
- Han, Jiequn; Zhang, Linfeng; E., Weinan
- Journal of Computational Physics, Vol. 399
Density functionals for coulomb systems
journal, September 1983
- Lieb, Elliott H.
- International Journal of Quantum Chemistry, Vol. 24, Issue 3
Universal variational functionals of electron densities, first-order density matrices, and natural spin-orbitals and solution of the v-representability problem
journal, December 1979
- Levy, M.
- Proceedings of the National Academy of Sciences, Vol. 76, Issue 12
Coupled-cluster method for multideterminantal reference states
journal, October 1981
- Jeziorski, Bogumil; Monkhorst, Hendrik J.
- Physical Review A, Vol. 24, Issue 4
Bypassing the Kohn-Sham equations with machine learning
journal, October 2017
- Brockherde, Felix; Vogt, Leslie; Li, Li
- Nature Communications, Vol. 8, Issue 1
Less is more: Sampling chemical space with active learning
journal, June 2018
- Smith, Justin S.; Nebgen, Ben; Lubbers, Nicholas
- The Journal of Chemical Physics, Vol. 148, Issue 24
VESTA 3 for three-dimensional visualization of crystal, volumetric and morphology data
journal, October 2011
- Momma, Koichi; Izumi, Fujio
- Journal of Applied Crystallography, Vol. 44, Issue 6
Inhomogeneous Electron Gas
journal, November 1964
- Hohenberg, P.; Kohn, W.
- Physical Review, Vol. 136, Issue 3B, p. B864-B871
Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces
journal, April 2007
- Behler, Jörg; Parrinello, Michele
- Physical Review Letters, Vol. 98, Issue 14
Visualization and analysis of atomistic simulation data with OVITO–the Open Visualization Tool
journal, December 2009
- Stukowski, Alexander
- Modelling and Simulation in Materials Science and Engineering, Vol. 18, Issue 1
Machine learning accurate exchange and correlation functionals of the electronic density
journal, July 2020
- Dick, Sebastian; Fernandez-Serra, Marivi
- Nature Communications, Vol. 11, Issue 1
A universal density matrix functional from molecular orbital-based machine learning: Transferability across organic molecules
journal, April 2019
- Cheng, Lixue; Welborn, Matthew; Christensen, Anders S.
- The Journal of Chemical Physics, Vol. 150, Issue 13
Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons
journal, April 2010
- Bartók, Albert P.; Payne, Mike C.; Kondor, Risi
- Physical Review Letters, Vol. 104, Issue 13
Hohenberg-Kohn theorem for nonlocal external potentials
journal, September 1975
- Gilbert, T. L.
- Physical Review B, Vol. 12, Issue 6
Transferable Machine-Learning Model of the Electron Density
journal, December 2018
- Grisafi, Andrea; Fabrizio, Alberto; Meyer, Benjamin
- ACS Central Science, Vol. 5, Issue 1
Regression Clustering for Improved Accuracy and Training Costs with Molecular-Orbital-Based Machine Learning
journal, October 2019
- Cheng, Lixue; Kovachki, Nikola B.; Welborn, Matthew
- Journal of Chemical Theory and Computation, Vol. 15, Issue 12
Solving the electronic structure problem with machine learning
journal, February 2019
- Chandrasekaran, Anand; Kamal, Deepak; Batra, Rohit
- npj Computational Materials, Vol. 5, Issue 1
Note on an Approximation Treatment for Many-Electron Systems
journal, October 1934
- Møller, Chr.; Plesset, M. S.
- Physical Review, Vol. 46, Issue 7
Machine learning of accurate energy-conserving molecular force fields
journal, May 2017
- Chmiela, Stefan; Tkatchenko, Alexandre; Sauceda, Huziel E.
- Science Advances, Vol. 3, Issue 5
Ab initio solution of the many-electron Schrödinger equation with deep neural networks
journal, September 2020
- Pfau, David; Spencer, James S.; Matthews, Alexander G. D. G.
- Physical Review Research, Vol. 2, Issue 3
Deep-neural-network solution of the electronic Schrödinger equation
journal, September 2020
- Hermann, Jan; Schätzle, Zeno; Noé, Frank
- Nature Chemistry, Vol. 12, Issue 10
Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics
journal, April 2018
- Zhang, Linfeng; Han, Jiequn; Wang, Han
- Physical Review Letters, Vol. 120, Issue 14
Strongly Constrained and Appropriately Normed Semilocal Density Functional
journal, July 2015
- Sun, Jianwei; Ruzsinszky, Adrienn; Perdew, John P.
- Physical Review Letters, Vol. 115, Issue 3
Validation of electronic structure methods for isomerization reactions of large organic molecules
journal, January 2011
- Luo, Sijie; Zhao, Yan; Truhlar, Donald G.
- Physical Chemistry Chemical Physics, Vol. 13, Issue 30
Self-Consistent Equations Including Exchange and Correlation Effects
journal, November 1965
- Kohn, W.; Sham, L. J.
- Physical Review, Vol. 140, Issue 4A, p. A1133-A1138
Completing density functional theory by machine learning hidden messages from molecules
journal, May 2020
- Nagai, Ryo; Akashi, Ryosuke; Sugino, Osamu
- npj Computational Materials, Vol. 6, Issue 1
Design and analysis of machine learning exchange-correlation functionals via rotationally invariant convolutional descriptors
journal, June 2019
- Lei, Xiangyun; Medford, Andrew J.
- Physical Review Materials, Vol. 3, Issue 6
Molecular force fields with gradient-domain machine learning: Construction and application to dynamics of small molecules with coupled cluster forces
journal, March 2019
- Sauceda, Huziel E.; Chmiela, Stefan; Poltavsky, Igor
- The Journal of Chemical Physics, Vol. 150, Issue 11
Generalized Kohn-Sham schemes and the band-gap problem
journal, February 1996
- Seidl, A.; Görling, A.; Vogl, P.
- Physical Review B, Vol. 53, Issue 7
Thermalized (350K) QM7b, GDB-13, water, and short alkane quantum chemistry dataset including MOB-ML features
February 2019
- Cheng, Lixue; Welborn, Matthew; Christensen, Anders S.
Molecular Force Fields with Gradient-Domain Machine Learning: Construction and Application to Dynamics of Small Molecules with Coupled Cluster Forces
text, January 2019
- Sauceda, Huziel E.; Chmiela, Stefan; Poltavsky, Igor
- arXiv
Thermalized (350K) QM7b, GDB-13, water, and short alkane quantum chemistry dataset including MOB-ML features
February 2019
- Cheng, Lixue; Welborn, Matthew; Christensen, Anders S.