Crystal structure prediction of energetic materials and a twisted arene with Genarris and GAtor

Bier, Imanuel; O'Connor, Dana; Hsieh, Y.C.; Wen, Wen; Hiszpanski, Anna M.; Han, T. Yong-Jin; Marom, Noa

doi:10.1039/d1ce00745a

Cited by 24 publications

(29 citation statements)

References 139 publications

(183 reference statements)

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“…Molecular crystals are of central importance as pharmaceuticals, [1][2][3] energetic materials, 4,5 and in the emerging field of organic electronics. 6,7 Due to the sensitivity of solid-state properties such as solubility and charge transport on crystal packing, one must identify all likely polymorphs when developing compounds for these applications.…”

Section: Introductionmentioning

confidence: 99%

Accurate and Efficient Polymorph Energy Ranking with XDM-corrected hybrid DFT

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Mayo

Roza

et al. 2022

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Accurate and efficient computation of relative energies of molecular-crystal polymorphs is of central importance for solid-state pharmaceuticals and in the field of crystal engineering. In recent years, dispersion corrected density-functional theory (DFT) has emerged as the pre-eminent energy-ranking method for crystal structure prediction (CSP). However, planewave implementations of these methods are hindered by poor scaling for large unit cells and are limited to semi-local functionals that suffer from delocalisation error. In this work, we demonstrate that a recent implementation of the exchange-hole dipole moment (XDM) dispersion correction in the Fritz Haber Institute ab initio molecular simulation (FHI-aims) package provides excellent performance for the energy ranking step of CSP. Thanks to its use of highly optimized numerical atom-centred orbitals, FHI-aims provides effectively linear scaling with system size and allows efficient use of hybrid density functionals with minimal basis-set incompleteness errors. We assess the performance of this methodology for the 26 compounds that formed the first 6 CSP blind tests. The hybrid results show significant improvements for 4/26 compounds, where delocalisation error affects the quality of predicted crystal-energy landscapes

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Section: Introductionmentioning

confidence: 99%

Accurate and Efficient Polymorph Energy Ranking with XDM-corrected hybrid DFT

Price

Mayo

Roza

et al. 2022

Preprint

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“…10,11 Polymorphism in organic compounds also complicates the CSP calculations, where molecules adopt different crystal structures that can be assessed experimentally. [12][13][14][15][16][17][18] If an experimental compound is thermodynamically stable ("thermodynamic polymorph"), it can be found as the global energy minimum, as opposed to metastable polymorphs that correspond to local energy minima. To find all possible polymorphs of a compound among the produced crystal structures, it is necessary to consider both kinetic and thermodynamic factors.…”

Section: Introductionmentioning

confidence: 99%

Crystal structure prediction of N-halide phthalimide compounds: halogen bonding synthons as a touchstone

2022

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“…Other applications using GAs include the identification of stable crystals and polymorphs of small molecules, , structure prediction of interfaces in multicomponent systems, and the docking of ligands in biomolecules . Previous studies leveraging GAs for crystal prediction have primarily focused on ab initio DFT calculations for chemically specific systems, , with crystal lattice representation based on symmetry groups or standard crystal lattices. , Despite the intense research activity enabled by GAs, reports on the prediction of crystal structures comprised from chiral molecules are rather limited and focus on specific chemical substances (e.g., energetic materials and plasmonic metasurfaces). In contrast, this study instead seeks to utilize our simple, tunable model to extract chirality-specific phenomena that are general and transferrable to a broad spectrum systems.…”

Section: Introductionmentioning

confidence: 99%

Crystal Prediction via Genetic Algorithms in a Model Chiral System

et al. 2022

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Chiral crystals and their constituent molecules play a prominent role in theories about the origin of biological homochirality and in drug discovery, design, and stability. Although the prediction and identification of stable chiral crystal structures is crucial for numerous technologies, including separation processes and polymorph selection and control, predictive ability is often complicated by a combination of many-body interactions and molecular complexity and handedness. In this work, we address these challenges by applying genetic algorithms to predict the ground-state crystal lattices formed by a chiral tetramer molecular model, which we have previously shown to exhibit complex fluid-phase behavior. Using this approach, we explore the relative stability and structures of the model’s conglomerate and racemic crystals, and present a structural phase diagram for the stable Bravais crystal types in the zero-temperature limit.

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Crystal structure prediction of energetic materials and a twisted arene with Genarris and GAtor

Abstract: A crystal structure prediction (CSP) workflow, based on the random structure generator, Genarris, and the genetic algorithm (GA), GAtor, is applied to the energetic materials 2,4,6-trinitrobenzene-1,3,5-triamine (TATB) and 2,4,6-trinitrobenzene-1,3-diamine (DATB),...

Cited by 24 publications

References 139 publications

Accurate and Efficient Polymorph Energy Ranking with XDM-corrected hybrid DFT

Accurate and Efficient Polymorph Energy Ranking with XDM-corrected hybrid DFT

Crystal structure prediction of N-halide phthalimide compounds: halogen bonding synthons as a touchstone

Crystal Prediction via Genetic Algorithms in a Model Chiral System

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