Perfecting Liquid-State Theories with Machine Intelligence
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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.
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References · 6
- 2019Analytical classical density functionals from an equation learning networkcited by 7
- 2020Physics-constrained Bayesian inference of state functions in classical density-functional theorycited by 4
- 2023Probabilistic forecast of nonlinear dynamical systems with uncertainty quantificationcited by 4
- 2023Ab initio uncertainty quantification in scattering analysis of microscopycited by 3
- 2023Physics-informed Bayesian inference of external potentials in classical density-functional theorycited by 3
- 2023Physics-informed machine learning of the correlation functions in bulk fluidscited by 1
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