A complete description of thermodynamic stabilities of molecular crystals

TL;DR

This paper introduces an efficient computational framework that combines electronic structure calculations, machine learning, and advanced free energy methods to accurately predict the thermodynamic stability of molecular crystals, including pharmaceuticals.

Contribution

It presents a novel integrated approach for ab initio Gibbs free energy calculations of molecular crystals, enabling reliable stability predictions for complex organic materials.

Findings

Predicted thermodynamic stabilities agree with experimental data.

Machine-learning potentials facilitate rapid and accurate free energy calculations.

The framework is applicable to diverse polymorphic compounds.

Abstract

Predictions of relative stabilities of (competing) molecular crystals are of great technological relevance, most notably for the pharmaceutical industry. However, they present a long-standing challenge for modeling, as often minuscule free energy differences are sensitively affected by the description of electronic structure, the statistical mechanics of the nuclei and the cell, and thermal expansion. The importance of these effects has been individually established, but rigorous free energy calculations for general molecular compounds, which simultaneously account for all effects,have hitherto not been computationally viable. Here we present an efficient "end to end" frame-work that seamlessly combines state-of-the art electronic structure calculations, machine-learning potentials, and advanced free energy methods to calculate ab initio Gibbs free energies for general organic molecular materials. The facile generation of machine-learning potentials for a diverse set of polymorphic compounds, benzene, glycine, and succinic acid, and predictions of thermodynamic stabilities in qualitative and quantitative agreement with experiments highlights that predictive thermodynamic studies of industrially-relevant molecular materials are no longer a daunting task.