Study of Entropy-Driven Polymorphic Stability for Aspirin Using Accurate Neural Network Interatomic Potential
Shinnosuke Hattori, Qiang Zhu

TL;DR
This study uses a neural network interatomic potential to accurately simulate aspirin polymorph stability, revealing that entropy and anharmonic effects favor Form I over Form II at 300 K, aligning with experimental data.
Contribution
We developed a machine learning potential to assess aspirin polymorph stability, demonstrating the significance of anharmonic effects and entropy in determining crystal stability.
Findings
Form I is more stable than Form II at 300 K.
Anharmonic effects are crucial for accurate stability predictions.
Rotational entropy contributes to polymorphic stability.
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 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…
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Taxonomy
TopicsComputational Drug Discovery Methods · Analytical Chemistry and Chromatography · Crystallization and Solubility Studies
