# Orbital-free Bond Breaking via Machine Learning

arXiv: https://arxiv.org/abs/1306.1812 · HTML: https://mlsanity.com/paper/1306.1812/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2013.

## Built on by (most cited first, 8 of 8)

- [Learning the exchange-correlation functional from nature with fully differentiable density functional theory](https://mlsanity.com/paper/2102.04229/) (2021)
- [Overcoming the Barrier of Orbital-Free Density Functional Theory for Molecular Systems Using Deep Learning](https://mlsanity.com/paper/2309.16578/) (2023)
- [KineticNet: Deep learning a transferable kinetic energy functional for orbital-free density functional theory](https://mlsanity.com/paper/2305.13316/) (2023)
- [Variational principle to regularize machine-learned density functionals: the non-interacting kinetic-energy functional](https://mlsanity.com/paper/2306.17587/) (2023)
- [Machine learning-guided construction of an analytic kinetic energy functional for orbital free density functional theory](https://mlsanity.com/paper/2502.05411/) (2025)
- [Surrogate Functionals for Machine-Learned Orbital-Free Density Functional Theory](https://mlsanity.com/paper/2604.20458/) (2026)
- [Understanding the Capabilities of Molecular Graph Neural Networks in Materials Science Through Multimodal Learning and Physical Context Encoding](https://mlsanity.com/paper/2505.12137/) (2025)
- [Predicting fermionic densities using a Projected Quantum Kernel method](https://mlsanity.com/paper/2504.14002/) (2025)

## 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.