Using reweighting and free energy surface interpolation to predict solid-solid phase diagrams

TL;DR

This paper presents a novel computational method combining reweighting and free energy surface interpolation to accurately predict solid-solid phase diagrams, demonstrated on benzene, with improved scalability and uncertainty estimation.

Contribution

The study introduces a new multistate reweighting approach for predicting phase diagrams, enhancing accuracy and scalability over previous methods.

Findings

Successfully applied to benzene phase diagram

Improves scalability with system size

Provides uncertainty estimates for phase boundaries

Abstract

Many physical properties of small organic molecules are dependent on the current crystal packing, or polymorph, of the material, including bioavailability of pharmaceuticals, optical properties of dyes, and charge transport properties of semiconductors. Predicting the most stable crystalline form requires determining the crystalline form with the lowest relative Gibbs free energy. Effective computational prediction of the most stable polymorph could save significant time and effort in the design of novel molecular crystalline solids or predict their behavior under new conditions. In this study, we introduce a new approach using multistate reweighting to address the problem of determining solid-solid phase diagrams, and apply this approach to the phase diagram of solid benzene. For this approach, we perform sampling at a selection of temperature and pressure states in the region of interest. We use multistate reweighting methods to determine the reduced free energy differences between $T$ and $P$ states within a given polymorph. The relative stability of the polymorphs at the sampled states can be successively interpolated from these points to create the phase diagram by combining these reduced free energy differences with a reference Gibbs free energy difference between polymorphs. The method also allows for straightforward estimation of uncertainties in the phase boundary. We also find that when properly implemented, multistate reweighting for phase diagram determination scales better with size of system than previously estimated.