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In The journal of physical chemistry letters ; h5-index 129.0

Determining the structural properties of condensed phase systems is a fundamental problem in theoretical statistical mechanics. Here, we present a machine learning method that is able to predict structural correlation functions with significantly improved accuracy in comparison to traditional approaches. The usefulness of this ex machina (from the machine) approach is illustrated by predicting the radial distribution function of two paradigmatic condensed phase systems: a Lennard-Jones fluid and a hard sphere fluid, and then comparing those results to the results obtained using both integral equation methods and empirically-motivated analytical functions. We find that application of the developed ex machina method typically decreases the predictive error by more than an order of magnitude in comparison to traditional theoretical methods.

Craven Galen T, Lubbers Nicholas, Barros Kipton, Tretiak Sergei

2020-May-05