# Perfecting Liquid-State Theories with Machine Intelligence

arXiv: https://arxiv.org/abs/2311.05167 · HTML: https://mlsanity.com/paper/2311.05167/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 50 references that are in the map.
- First posted on arXiv 9 Nov 2023.

## Builds on (most cited first, 6 of 6)

- [Analytical classical density functionals from an equation learning network](https://mlsanity.com/paper/1910.12752/) (2019)
- [Physics-constrained Bayesian inference of state functions in classical density-functional theory](https://mlsanity.com/paper/2010.03374/) (2020)
- [Probabilistic forecast of nonlinear dynamical systems with uncertainty quantification](https://mlsanity.com/paper/2305.08942/) (2023)
- [Ab initio uncertainty quantification in scattering analysis of microscopy](https://mlsanity.com/paper/2309.02468/) (2023)
- [Physics-informed Bayesian inference of external potentials in classical density-functional theory](https://mlsanity.com/paper/2309.07065/) (2023)
- [Physics-informed machine learning of the correlation functions in bulk fluids](https://mlsanity.com/paper/2309.00767/) (2023)

## Abstract (arXiv)

Recent years have seen a significant increase in the use of machine intelligence for predicting electronic structure, molecular force fields, and the physicochemical properties of various condensed systems. However, substantial challenges remain in developing a comprehensive framework capable of handling a wide range of atomic compositions and thermodynamic conditions. This perspective discusses potential future developments in liquid-state theories leveraging on recent advancements of functional machine learning. By harnessing the strengths of theoretical analysis and machine learning techniques including surrogate models, dimension reduction and uncertainty quantification, we envision that liquid-state theories will gain significant improvements in accuracy, scalability and computational efficiency, enabling their broader applications across diverse materials and chemical systems.