Toward dynamic crystal structure prediction: integrating thermodynamic and kinetic modeling.
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| Title: | Toward dynamic crystal structure prediction: integrating thermodynamic and kinetic modeling. |
|---|---|
| Authors: | Reddy, Prem K.1 (AUTHOR), Dighe, Anish V.1 (AUTHOR), Bhawnani, Rajan R.1 (AUTHOR), Singh, Meenesh R.1 (AUTHOR) mrsingh@uic.edu |
| Source: | CrystEngComm. 3/16/2026, Vol. 28 Issue 11, p1727-1751. 25p. |
| Subjects: | Polymorphism (Crystallography), Crystallization kinetics, Crystallization, Thermodynamics, Molecular crystals, Simulation methods & models, Molecular self-assembly |
| Abstract: | Precise control over the physical properties of organic molecular crystals is crucial for the design and manufacture of pharmaceutical compounds. Many organic molecules exhibit polymorphism, the ability to exist in multiple solid-state forms, each possessing distinct physical properties such as bioavailability, density, and dissolution rate. These variations are not merely academic; they can render a pharmaceutical compound ineffective or even hazardous if the undesired polymorph forms. Achieving reliable control over polymorphism therefore requires a fundamental understanding of the mechanisms of crystallization and molecular self-assembly spanning from the molecular to the macroscopic scale. Insights into self-assembly at multiple scales offer the opportunity to elucidate the dynamics of weak intermolecular interactions, advancing both crystal engineering and materials design. Existing computational approaches to crystal structure prediction (CSP) primarily focus on identifying dense, energy-minimized packings of molecules within rationally selected space groups. Although such thermodynamic methods can enumerate possible polymorphs, they often fail to consistently reproduce the experimentally observed crystal form under realistic kinetic and solvent conditions. This review aims to establish a conceptual framework for a multiscale model of crystallization, integrating both energetic and kinetic perspectives. By critically assessing the computational methods that model molecular self-assembly, nucleation, and growth, this work outlines a pathway toward dynamic crystal structure prediction, a unified approach capable of predicting not just feasible polymorphs but also the dominant crystal form that emerges under specific crystallization conditions. [ABSTRACT FROM AUTHOR] |
| Copyright of CrystEngComm is the property of Royal Society of Chemistry and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 192309827 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Toward dynamic crystal structure prediction: integrating thermodynamic and kinetic modeling. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Reddy%2C+Prem+K%2E%22">Reddy, Prem K.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Dighe%2C+Anish+V%2E%22">Dighe, Anish V.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bhawnani%2C+Rajan+R%2E%22">Bhawnani, Rajan R.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Singh%2C+Meenesh+R%2E%22">Singh, Meenesh R.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> mrsingh@uic.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22CrystEngComm%22">CrystEngComm</searchLink>. 3/16/2026, Vol. 28 Issue 11, p1727-1751. 25p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Polymorphism+%28Crystallography%29%22">Polymorphism (Crystallography)</searchLink><br /><searchLink fieldCode="DE" term="%22Crystallization+kinetics%22">Crystallization kinetics</searchLink><br /><searchLink fieldCode="DE" term="%22Crystallization%22">Crystallization</searchLink><br /><searchLink fieldCode="DE" term="%22Thermodynamics%22">Thermodynamics</searchLink><br /><searchLink fieldCode="DE" term="%22Molecular+crystals%22">Molecular crystals</searchLink><br /><searchLink fieldCode="DE" term="%22Simulation+methods+%26+models%22">Simulation methods & models</searchLink><br /><searchLink fieldCode="DE" term="%22Molecular+self-assembly%22">Molecular self-assembly</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Precise control over the physical properties of organic molecular crystals is crucial for the design and manufacture of pharmaceutical compounds. Many organic molecules exhibit polymorphism, the ability to exist in multiple solid-state forms, each possessing distinct physical properties such as bioavailability, density, and dissolution rate. These variations are not merely academic; they can render a pharmaceutical compound ineffective or even hazardous if the undesired polymorph forms. Achieving reliable control over polymorphism therefore requires a fundamental understanding of the mechanisms of crystallization and molecular self-assembly spanning from the molecular to the macroscopic scale. Insights into self-assembly at multiple scales offer the opportunity to elucidate the dynamics of weak intermolecular interactions, advancing both crystal engineering and materials design. Existing computational approaches to crystal structure prediction (CSP) primarily focus on identifying dense, energy-minimized packings of molecules within rationally selected space groups. Although such thermodynamic methods can enumerate possible polymorphs, they often fail to consistently reproduce the experimentally observed crystal form under realistic kinetic and solvent conditions. This review aims to establish a conceptual framework for a multiscale model of crystallization, integrating both energetic and kinetic perspectives. By critically assessing the computational methods that model molecular self-assembly, nucleation, and growth, this work outlines a pathway toward dynamic crystal structure prediction, a unified approach capable of predicting not just feasible polymorphs but also the dominant crystal form that emerges under specific crystallization conditions. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of CrystEngComm is the property of Royal Society of Chemistry and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1039/d5ce00977d Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 25 StartPage: 1727 Subjects: – SubjectFull: Polymorphism (Crystallography) Type: general – SubjectFull: Crystallization kinetics Type: general – SubjectFull: Crystallization Type: general – SubjectFull: Thermodynamics Type: general – SubjectFull: Molecular crystals Type: general – SubjectFull: Simulation methods & models Type: general – SubjectFull: Molecular self-assembly Type: general Titles: – TitleFull: Toward dynamic crystal structure prediction: integrating thermodynamic and kinetic modeling. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Reddy, Prem K. – PersonEntity: Name: NameFull: Dighe, Anish V. – PersonEntity: Name: NameFull: Bhawnani, Rajan R. – PersonEntity: Name: NameFull: Singh, Meenesh R. IsPartOfRelationships: – BibEntity: Dates: – D: 16 M: 03 Text: 3/16/2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 14668033 Numbering: – Type: volume Value: 28 – Type: issue Value: 11 Titles: – TitleFull: CrystEngComm Type: main |
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