On-lattice kinetic Monte Carlo approaches for modeling molecular anisotropy in resveratrol crystallization

Abstract Stilbenes are a class of organic compounds with broad-ranging pharmaceutical and agricultural applications, which are typically isolated and purified through recrystallization. We are motivated by reducing experimental waste and optimizing yield via developing predictive simulations for processing-dependent crystal morphologies. Using resveratrol as a model stilbene system, we have developed an approach for simulating crystallization with molecular resolution using on-lattice kinetic Monte Carlo. In this work, we highlight modifications to the Stochastic Parallel PARticle Kinetic Simulator (SPPARKS) software package, which were essential to this application. Key enhancements include the incorporation of non-orthogonal cell shapes and monomer anisotropy approximations using bound hard spheres. This new SPPARKS application has been applied to resveratrol with attachment energy libraries obtained from density functional theory, resulting in excellent agreement with experimental morphology prediction.

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Publication Details

Journal
Modelling and Simulation in Materials Science and Engineering
Published
2025-06-18
DOI
https://doi.org/10.1088/1361-651x/ade176
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.00

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article

On-lattice kinetic Monte Carlo approaches for modeling molecular anisotropy in resveratrol crystallization

Rekha R. Rao, Tesia D. Janicki, Christine Cardinal Roberts, Theron Rodgers et al.
Modelling and Simulation in Materials Science and Engineering
Machine Learning in Materials Science
article

On-lattice kinetic Monte Carlo approaches for modeling molecular anisotropy in resveratrol crystallization

Rekha R. Rao, Tesia D. Janicki, Christine Cardinal Roberts, Theron Rodgers, Jonathan Leonard, Tyler R Kennelly
article en

Abstract

Abstract Stilbenes are a class of organic compounds with broad-ranging pharmaceutical and agricultural applications, which are typically isolated and purified through recrystallization. We are motivated by reducing experimental waste and optimizing yield via developing predictive simulations for processing-dependent crystal morphologies. Using resveratrol as a model stilbene system, we have developed an approach for simulating crystallization with molecular resolution using on-lattice kinetic Monte Carlo. In this work, we highlight modifications to the Stochastic Parallel PARticle Kinetic Simulator (SPPARKS) software package, which were essential to this application. Key enhancements include the incorporation of non-orthogonal cell shapes and monomer anisotropy approximations using bound hard spheres. This new SPPARKS application has been applied to resveratrol with attachment energy libraries obtained from density functional theory, resulting in excellent agreement with experimental morphology prediction.

Modelling and Simulation in Materials Science and EngineeringVol. 33(5)
Sandia National Laboratories (US)
U.S. Department of Energy
Responsible consumption and production
Openalex Percentile: Top 90%
Machine Learning in Materials Science
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On-lattice kinetic Monte Carlo approaches for modeling molecular anisotropy in resveratrol crystallization — Rekha R. Rao, Tesia D. Janicki, et al. · Modelling and Simulation in Materials Science and Engineering (2025) | TGRS Research Map | TGRS