Title: Study of Entropy-Driven Polymorphic Stability for Aspirin Using Accurate Neural Network Interatomic Potential

Abstract: In this study, we present a systematic computational investigation to analyze the long debated crystal stability of two well known aspirin polymorphs, labeled as Form I and Form II. Specifically, we developed a strategy to collect training configurations covering diverse interatomic interactions between representative functional groups in the aspirin crystals. Utilizing a state-of-the-art neural network interatomic potential (NNIP) model, we developed an accurate machine learning potential to simulate aspirin crystal dynamics under finite temperature conditions with $\sim$0.46 kJ/mol/molecule accuracy. Employing the trained NNIP model, we performed thermodynamic integration to assess the free energy difference between aspirin Forms I and II, accounting for the anharmonic effects in a large supercell consisting of 512 molecules. For the first time, our results convincingly demonstrated that Form I is more stable than Form II at 300 K, ranging from 0.74 to 1.83 kJ/mol/molecule, aligning with the experimental observations. Unlike the majority of previous simulations based on (quasi)harmonic approximations in a small super cell, which often found the degenerate energies between aspirin I and II, our findings underscore the importance of anharmonic effects in determining polymorphic stability ranking. Furthermore, we proposed the use of rotational degrees of freedom of methyl and ester/phenyl groups in the aspirin crystal, as characteristic motions to highlight rotational entropic contribution that favors the stability of Form I. Beyond the aspirin polymorphism, we anticipate that such entropy-driven stabilization can be broadly applicable to many other organic systems and thus our approach, suggesting our approach holds a great promise for stability studies in small molecule drug design.