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Koch KN, Teo AJ, Wheeler KA. Dual space divergence in small molecule quasiracemates: benzoyl leucine and phenylalanine assemblies. Chem Commun (Camb) 2024; 60:2800-2803. [PMID: 38362749 DOI: 10.1039/d3cc06212k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/17/2024]
Abstract
Quasiracemic materials constructed with two points of structural difference were used to understand the role molecular shape plays in molecular assembly. Hot stage, crystallographic and occupied cavity space assessments provide insight into how imposed CH3/Cl and H/CF3 structural variations placed on benzoyl leucine and phenylalanine scaffolds result in a remarkably high occurrence of cocrystal formation.
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Affiliation(s)
- Katelyn N Koch
- Department of Chemistry, Whitworth University, 300 West Hawthorne Road, Spokane, Washington, 99251, USA.
| | - Aaron J Teo
- Department of Chemistry, Whitworth University, 300 West Hawthorne Road, Spokane, Washington, 99251, USA.
| | - Kraig A Wheeler
- Department of Chemistry, Whitworth University, 300 West Hawthorne Road, Spokane, Washington, 99251, USA.
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2
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Beran GJO. Frontiers of molecular crystal structure prediction for pharmaceuticals and functional organic materials. Chem Sci 2023; 14:13290-13312. [PMID: 38033897 PMCID: PMC10685338 DOI: 10.1039/d3sc03903j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 11/02/2023] [Indexed: 12/02/2023] Open
Abstract
The reliability of organic molecular crystal structure prediction has improved tremendously in recent years. Crystal structure predictions for small, mostly rigid molecules are quickly becoming routine. Structure predictions for larger, highly flexible molecules are more challenging, but their crystal structures can also now be predicted with increasing rates of success. These advances are ushering in a new era where crystal structure prediction drives the experimental discovery of new solid forms. After briefly discussing the computational methods that enable successful crystal structure prediction, this perspective presents case studies from the literature that demonstrate how state-of-the-art crystal structure prediction can transform how scientists approach problems involving the organic solid state. Applications to pharmaceuticals, porous organic materials, photomechanical crystals, organic semi-conductors, and nuclear magnetic resonance crystallography are included. Finally, efforts to improve our understanding of which predicted crystal structures can actually be produced experimentally and other outstanding challenges are discussed.
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Affiliation(s)
- Gregory J O Beran
- Department of Chemistry, University of California Riverside Riverside CA 92521 USA
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3
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Wang B, Hilleke KP, Hajinazar S, Frapper G, Zurek E. Structurally Constrained Evolutionary Algorithm for the Discovery and Design of Metastable Phases. J Chem Theory Comput 2023; 19:7960-7971. [PMID: 37856841 DOI: 10.1021/acs.jctc.3c00594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2023]
Abstract
Metastable materials are abundant in nature and technology, showcasing remarkable properties that inspire innovative materials design. However, traditional crystal structure prediction methods, which rely solely on energetic factors to determine a structure's fitness, are not suitable for predicting the vast number of potentially synthesizable phases that represent a local minimum corresponding to a state in thermodynamic equilibrium. Here, we present a new approach for the prediction of metastable phases with specific structural features and interface this method with the XtalOpt evolutionary algorithm. Our method relies on structural features that include the local crystalline order (e.g, the coordination number or chemical environment), and symmetry (e.g, Bravais lattice and space group) to filter the breeding pool of an evolutionary crystal structure search. The effectiveness of this approach is benchmarked on three known metastable systems: XeN8, with a two-dimensional polymeric nitrogen sublattice, brookite TiO2, and a high pressure BaH4 phase, which was recently characterized. Additionally, a newly predicted metastable melaminate salt, P1̅ WC3N6, was found to possess an energy that is lower than that of two phases proposed in a recent computational study. The method presented here could help in identifying the structures of compounds that have already been synthesized, and in developing new synthesis targets with desired properties.
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Affiliation(s)
- Busheng Wang
- Department of Chemistry, State University of New York at Buffalo, Buffalo, New York 14260-3000, United States
| | - Katerina P Hilleke
- Department of Chemistry, State University of New York at Buffalo, Buffalo, New York 14260-3000, United States
| | - Samad Hajinazar
- Department of Chemistry, State University of New York at Buffalo, Buffalo, New York 14260-3000, United States
| | - Gilles Frapper
- Applied Quantum Chemistry Group, E4 Team, IC2MP UMR 7285, Université de Poitiers, CNRS, Poitiers 86073, France
| | - Eva Zurek
- Department of Chemistry, State University of New York at Buffalo, Buffalo, New York 14260-3000, United States
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4
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Manna S, Wang Y, Hernandez A, Lile P, Liu S, Mueller T. A database of low-energy atomically precise nanoclusters. Sci Data 2023; 10:308. [PMID: 37210383 DOI: 10.1038/s41597-023-02200-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 04/28/2023] [Indexed: 05/22/2023] Open
Abstract
The chemical and structural properties of atomically precise nanoclusters are of great interest in numerous applications, but the structures of the clusters can be computationally expensive to predict. In this work, we present the largest database of cluster structures and properties determined using ab-initio methods to date. We report the methodologies used to discover low-energy clusters as well as the energies, relaxed structures, and physical properties (such as relative stability, HOMO-LUMO gap among others) for 63,015 clusters across 55 elements. We have identified clusters for 593 out of 1595 cluster systems (element-size pairs) explored by literature that have energies lower than those reported in literature by at least 1 meV/atom. We have also identified clusters for 1320 systems for which we were unable to find previous low-energy structures in the literature. Patterns in the data reveal insights into the chemical and structural relationships among the elements at the nanoscale. We describe how the database can be accessed for future studies and the development of nanocluster-based technologies.
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Affiliation(s)
- Sukriti Manna
- Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Yunzhe Wang
- Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Alberto Hernandez
- Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Peter Lile
- Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Shanping Liu
- Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Tim Mueller
- Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA.
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5
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Global analysis of the energy landscapes of molecular crystal structures by applying the threshold algorithm. Commun Chem 2022; 5:86. [PMID: 36697680 PMCID: PMC9814927 DOI: 10.1038/s42004-022-00705-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 07/15/2022] [Indexed: 01/28/2023] Open
Abstract
Polymorphism in molecular crystals has important consequences for the control of materials properties and our understanding of crystallization. Computational methods, including crystal structure prediction, have provided important insight into polymorphism, but have usually been limited to assessing the relative energies of structures. We describe the implementation of the Monte Carlo threshold algorithm as a method to provide an estimate of the energy barriers separating crystal structures. By sampling the local energy minima accessible from multiple starting structures, the simulations yield a global picture of the crystal energy landscapes and provide valuable information on the depth of the energy minima associated with crystal structures. We present results from applying the threshold algorithm to four polymorphic organic molecular crystals, examine the influence of applying space group symmetry constraints during the simulations, and discuss the relationship between the structure of the energy landscape and the intermolecular interactions present in the crystals.
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6
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Balodis M, Cordova M, Hofstetter A, Day GM, Emsley L. De Novo Crystal Structure Determination from Machine Learned Chemical Shifts. J Am Chem Soc 2022; 144:7215-7223. [PMID: 35416661 PMCID: PMC9052749 DOI: 10.1021/jacs.1c13733] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Determination of the three-dimensional atomic-level structure of powdered solids is one of the key goals in current chemistry. Solid-state NMR chemical shifts can be used to solve this problem, but they are limited by the high computational cost associated with crystal structure prediction methods and density functional theory chemical shift calculations. Here, we successfully determine the crystal structures of ampicillin, piroxicam, cocaine, and two polymorphs of the drug molecule AZD8329 using on-the-fly generated machine-learned isotropic chemical shifts to directly guide a Monte Carlo-based structure determination process starting from a random gas-phase conformation.
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Affiliation(s)
- Martins Balodis
- Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
| | - Manuel Cordova
- Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland.,National Centre for Computational Design and Discovery of Novel Materials MARVEL, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne CH-1015, Switzerland
| | - Albert Hofstetter
- Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
| | - Graeme M Day
- School of Chemistry, University of Southampton, Highfield SO17 1BJ, Southampton, United Kingdom
| | - Lyndon Emsley
- Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland.,National Centre for Computational Design and Discovery of Novel Materials MARVEL, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne CH-1015, Switzerland
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7
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Momenzadeh Abardeh Z, Salimi A, Oganov AR. Crystal structure prediction of N-halide phthalimide compounds: Halogen bond synthon as a touchstone. CrystEngComm 2022. [DOI: 10.1039/d2ce00476c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
We address crystal structure prediction problem by combining evolutionary algorithm USPEX (used to predict sets of low-energy crystal structures) and synthon approach (extracting preferable supramolecular synthons from Cambridge Structural Database,...
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8
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Arslan Irmak E, Liu P, Bals S, Van Aert S. 3D Atomic Structure of Supported Metallic Nanoparticles Estimated from 2D ADF STEM Images: A Combination of Atom-Counting and a Local Minima Search Algorithm. SMALL METHODS 2021; 5:e2101150. [PMID: 34928008 DOI: 10.1002/smtd.202101150] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 10/22/2021] [Indexed: 06/14/2023]
Abstract
Determining the 3D atomic structure of nanoparticles (NPs) is critical to understand their structure-dependent properties. It is hereby important to perform such analyses under conditions relevant for the envisioned application. Here, the 3D structure of supported Au NPs at high temperature, which is of importance to understand their behavior during catalytic reactions, is investigated. To overcome limitations related to conventional high-resolution electron tomography at high temperature, 3D characterization of NPs with atomic resolution has been performed by applying atom-counting using atomic resolution annular dark-field scanning transmission electron microscopy (ADF STEM) images followed by structural relaxation. However, at high temperatures, thermal displacements, which affect the ADF STEM intensities, should be taken into account. Moreover, it is very likely that the structure of an NP investigated at elevated temperature deviates from a ground state configuration, which is difficult to determine using purely computational energy minimization approaches. In this paper, an optimized approach is therefore proposed using an iterative local minima search algorithm followed by molecular dynamics structural relaxation of candidate structures associated with each local minimum. In this manner, it becomes possible to investigate the 3D atomic structure of supported NPs, which may deviate from their ground state configuration.
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Affiliation(s)
- Ece Arslan Irmak
- EMAT and NANOlab Center of Excellence, University of Antwerp, Groenenborgerlaan 171, 2020 Antwerp, Belgium
| | - Pei Liu
- EMAT and NANOlab Center of Excellence, University of Antwerp, Groenenborgerlaan 171, 2020 Antwerp, Belgium
| | - Sara Bals
- EMAT and NANOlab Center of Excellence, University of Antwerp, Groenenborgerlaan 171, 2020 Antwerp, Belgium
| | - Sandra Van Aert
- EMAT and NANOlab Center of Excellence, University of Antwerp, Groenenborgerlaan 171, 2020 Antwerp, Belgium
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9
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Gunde M, Salles N, Hémeryck A, Martin-Samos L. IRA: A Shape Matching Approach for Recognition and Comparison of Generic Atomic Patterns. J Chem Inf Model 2021; 61:5446-5457. [PMID: 34704748 DOI: 10.1021/acs.jcim.1c00567] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
We propose a versatile, parameter-less approach for solving the shape matching problem, specifically in the context of atomic structures when atomic assignments are not known a priori. The algorithm Iteratively suggests Rotated atom-centered reference frames and Assignments (iterative rotations and assignments (IRA)). The frame for which a permutationally invariant set-set distance, namely, the Hausdorff distance, returns a minimal value is chosen as the solution of the matching problem. IRA is able to find rigid rotations, reflections, translations, and permutations between structures with different numbers of atoms, for any atomic arrangement and pattern, periodic or not. When distortions are present between the structures, optimal rotation and translation are found by further applying a standard singular value decomposition-based method. To compute the atomic assignments under the one-to-one assignment constraint, we develop our own algorithm, constrained shortest distance assignments (CShDA). The overall approach is extensively tested on several structures, including distorted structural fragments. The efficiency of the proposed algorithm is shown as a benchmark comparison against two other shape matching algorithms. We discuss the use of our approach for the identification and comparison of structures and structural fragments through two examples: a replica-exchange trajectory of a cyanine molecule, in which we show how our approach could aid the exploration of relevant collective coordinates for clustering the data, and a SiO2 amorphous model, in which we compute distortion scores, and compare them with a classical strain-based potential. The source code and benchmark data are available at https://github.com/mammasmias/IterativeRotationsAssignments.
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Affiliation(s)
- Miha Gunde
- LAAS-CNRS, Université de Toulouse, CNRS, 7 avenue du Colonel Roche, 31031 Toulouse, France.,CNR-IOM, Democritos National Simulation Center, Istituto Officina dei Materiali, c/o SISSA, via Bonomea 265, IT-34136 Trieste, Italy
| | - Nicolas Salles
- CNR-IOM, Democritos National Simulation Center, Istituto Officina dei Materiali, c/o SISSA, via Bonomea 265, IT-34136 Trieste, Italy
| | - Anne Hémeryck
- LAAS-CNRS, Université de Toulouse, CNRS, 7 avenue du Colonel Roche, 31031 Toulouse, France
| | - Layla Martin-Samos
- CNR-IOM, Democritos National Simulation Center, Istituto Officina dei Materiali, c/o SISSA, via Bonomea 265, IT-34136 Trieste, Italy
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10
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Banerjee A, Jasrasaria D, Niblett SP, Wales DJ. Crystal Structure Prediction for Benzene Using Basin-Hopping Global Optimization. J Phys Chem A 2021; 125:3776-3784. [PMID: 33881850 PMCID: PMC8279651 DOI: 10.1021/acs.jpca.1c00903] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 04/07/2021] [Indexed: 11/29/2022]
Abstract
Organic molecules can be stable in distinct crystalline forms, known as polymorphs, which have significant consequences for industrial applications. Here, we predict the polymorphs of crystalline benzene computationally for an accurate anisotropic model parametrized to reproduce electronic structure calculations. We adapt the basin-hopping global optimization procedure to the case of crystalline unit cells, simultaneously optimizing the molecular coordinates and unit cell parameters to locate multiple low-energy structures from a variety of crystal space groups. We rapidly locate all the well-established experimental polymorphs of benzene, each of which corresponds to a single local energy minimum of the model. Our results show that basin-hopping can be both an efficient and effective tool for polymorphic crystal structure prediction, requiring no a priori experimental knowledge of cell parameters or symmetry.
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Affiliation(s)
- Atreyee Banerjee
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, Lensfield
Road, Cambridge CB2 1EW, United Kingdom
- Max
Planck Institute for Polymer Research, 55128 Mainz, Germany
| | - Dipti Jasrasaria
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, Lensfield
Road, Cambridge CB2 1EW, United Kingdom
- Department
of Chemistry, University of California, Berkeley, California 94609, United States
| | - Samuel P. Niblett
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, Lensfield
Road, Cambridge CB2 1EW, United Kingdom
- Department
of Chemistry, University of California, Berkeley, California 94609, United States
- Materials
Science Division, Lawrence Berkeley National
Laboratory, Berkeley, California 94609, United States
| | - David J. Wales
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, Lensfield
Road, Cambridge CB2 1EW, United Kingdom
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