Orbital-free Bond Breaking via Machine Learning
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Abstract (arXiv)
Machine learning is used to approximate the kinetic energy of one dimensional diatomics as a functional of the electron density. The functional can accurately dissociate a diatomic, and can be systematically improved with training. Highly accurate self-consistent densities and molecular forces are found, indicating the possibility for ab-initio molecular dynamics simulations.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Jun 2013.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 3; 2024: 0; 2025: 3; 2026: 1.
8 papers.
- 2021Learning the exchange-correlation functional from nature with fully differentiable density functional theorycited by 23
- 2023Overcoming the Barrier of Orbital-Free Density Functional Theory for Molecular Systems Using Deep Learningcited by 11
- 2023KineticNet: Deep learning a transferable kinetic energy functional for orbital-free density functional theorycited by 7
- 2023Variational principle to regularize machine-learned density functionals: the non-interacting kinetic-energy functionalcited by 2
- 2025Machine learning-guided construction of an analytic kinetic energy functional for orbital free density functional theorycited by 1
- 2025Understanding the Capabilities of Molecular Graph Neural Networks in Materials Science Through Multimodal Learning and Physical Context Encodingcited by 1
- 2025Predicting fermionic densities using a Projected Quantum Kernel methodcited by 0
- 2026Surrogate Functionals for Machine-Learned Orbital-Free Density Functional Theorycited by 0
- 2026Surrogate Functionals for Machine-Learned Orbital-Free Density Functional Theory22 Apr 2026
- 2025Understanding the Capabilities of Molecular Graph Neural Networks in Materials Science Through Multimodal Learning and Physical Context Encoding17 May 2025
- 2025Predicting fermionic densities using a Projected Quantum Kernel method18 Apr 2025
- 2025Machine learning-guided construction of an analytic kinetic energy functional for orbital free density functional theory8 Feb 2025
- 2023Overcoming the Barrier of Orbital-Free Density Functional Theory for Molecular Systems Using Deep Learning28 Sep 2023
- 2023Variational principle to regularize machine-learned density functionals: the non-interacting kinetic-energy functional30 Jun 2023
- 2023KineticNet: Deep learning a transferable kinetic energy functional for orbital-free density functional theory8 May 2023
- 2021Learning the exchange-correlation functional from nature with fully differentiable density functional theory8 Feb 2021