DeePKS: a comprehensive data-driven approach towards chemically accurate density functional theory
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Abstract (arXiv)
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.
Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Aug 2020.
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Citing papers per year: 2021: 4; 2022: 3; 2023: 4; 2024: 3; 2025: 0; 2026: 1.
15 papers.
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- 2024DySLIM: Dynamics Stable Learning by Invariant Measure for Chaotic Systemscited by 29
- 2021Learning the exchange-correlation functional from nature with fully differentiable density functional theorycited by 23
- 2022Evolving symbolic density functionalscited by 19
- 2021Differentiable Physics: A Position Piececited by 12
- 2024Self-Consistency Training for Density-Functional-Theory Hamiltonian Predictioncited by 12
- 2023Overcoming the Barrier of Orbital-Free Density Functional Theory for Molecular Systems Using Deep Learningcited by 11
- 2021Machine learning to tame divergent density functional approximations: a new path to consensus materials design principlescited by 6
- 2023D4FT: A Deep Learning Approach to Kohn-Sham Density Functional Theorycited by 6
- 2021How Well Does Kohn-Sham Regularizer Work for Weakly Correlated Systems?cited by 3
- 2022Qualitative neural network approximation over R and C: Elementary proofs for analytic and polynomial activationcited by 2
- 2023Machine learning for accuracy in density functional approximationscited by 2
- 2023Grad DFT: a software library for machine learning enhanced density functional theorycited by 1
- 2026Scaling Density Functional Theory with Gaussian Splatting25 Sep 2026
- 2024NeuralSCF: Neural network self-consistent fields for density functional theory22 Jun 2024
- 2024Self-Consistency Training for Density-Functional-Theory Hamiltonian Prediction14 Mar 2024
- 2024DySLIM: Dynamics Stable Learning by Invariant Measure for Chaotic Systems6 Feb 2024
- 2023Machine learning for accuracy in density functional approximations1 Nov 2023
- 2023Overcoming the Barrier of Orbital-Free Density Functional Theory for Molecular Systems Using Deep Learning28 Sep 2023
- 2023Grad DFT: a software library for machine learning enhanced density functional theory23 Sep 2023
- 2023D4FT: A Deep Learning Approach to Kohn-Sham Density Functional Theory1 Mar 2023
- 2022DeePKS+ABACUS as a Bridge between Expensive Quantum Mechanical Models and Machine Learning Potentials21 Jun 2022
- 2022Qualitative neural network approximation over R and C: Elementary proofs for analytic and polynomial activation25 Mar 2022
- 2022Evolving symbolic density functionals3 Mar 2022
- 2021How Well Does Kohn-Sham Regularizer Work for Weakly Correlated Systems?28 Oct 2021
3 more
- 2021Differentiable Physics: A Position Piece14 Sep 2021
- 2021Machine learning to tame divergent density functional approximations: a new path to consensus materials design principles24 Jun 2021
- 2021Learning the exchange-correlation functional from nature with fully differentiable density functional theory8 Feb 2021
- 2024DySLIM: Dynamics Stable Learning by Invariant Measure for Chaotic Systemscited by 29
- 2021Learning the exchange-correlation functional from nature with fully differentiable density functional theorycited by 23
- 2022Evolving symbolic density functionalscited by 19
- 2021Differentiable Physics: A Position Piececited by 12
- 2024Self-Consistency Training for Density-Functional-Theory Hamiltonian Predictioncited by 12
- 2023Overcoming the Barrier of Orbital-Free Density Functional Theory for Molecular Systems Using Deep Learningcited by 11
- 2021Machine learning to tame divergent density functional approximations: a new path to consensus materials design principlescited by 6
- 2023D4FT: A Deep Learning Approach to Kohn-Sham Density Functional Theorycited by 6
- 2021How Well Does Kohn-Sham Regularizer Work for Weakly Correlated Systems?cited by 3
- 2022Qualitative neural network approximation over R and C: Elementary proofs for analytic and polynomial activationcited by 2
- 2023Machine learning for accuracy in density functional approximationscited by 2
- 2023Grad DFT: a software library for machine learning enhanced density functional theorycited by 1
12 most cited of 15 All 15 (JSON)
- 2026Scaling Density Functional Theory with Gaussian Splatting25 Sep 2026
- 2024NeuralSCF: Neural network self-consistent fields for density functional theory22 Jun 2024
- 2024Self-Consistency Training for Density-Functional-Theory Hamiltonian Prediction14 Mar 2024
- 2024DySLIM: Dynamics Stable Learning by Invariant Measure for Chaotic Systems6 Feb 2024
- 2023Machine learning for accuracy in density functional approximations1 Nov 2023
- 2023Overcoming the Barrier of Orbital-Free Density Functional Theory for Molecular Systems Using Deep Learning28 Sep 2023
- 2023Grad DFT: a software library for machine learning enhanced density functional theory23 Sep 2023
- 2023D4FT: A Deep Learning Approach to Kohn-Sham Density Functional Theory1 Mar 2023
- 2022DeePKS+ABACUS as a Bridge between Expensive Quantum Mechanical Models and Machine Learning Potentials21 Jun 2022
- 2022Qualitative neural network approximation over R and C: Elementary proofs for analytic and polynomial activation25 Mar 2022
- 2022Evolving symbolic density functionals3 Mar 2022
- 2021How Well Does Kohn-Sham Regularizer Work for Weakly Correlated Systems?28 Oct 2021
12 newest of 15 All 15 (JSON)