Transferable force field for crystal structure predictions, investigation of performance and exploration of different rescoring strategies using DFT-D methods

2016 ◽  
Vol 72 (4) ◽  
pp. 460-476 ◽  
Author(s):  
Anders Broo ◽  
Sten O. Nilsson Lill
Keyword(s):  
Crystal Structure ◽  
Force Field ◽  
Crystal Structures ◽  
Density Functional ◽  
Geometry Optimization ◽  
Field Methods ◽  
Functional Theory ◽  
Ranking List ◽  
Drug Molecules ◽  
New Type

A new force field, here called AZ-FF, aimed at being used for crystal structure predictions, has been developed. The force field is transferable to a new type of chemistry without additional training or modifications. This makes the force field very useful in the prediction of crystal structures of new drug molecules since the time-consuming step of developing a new force field for each new molecule is circumvented. The accuracy of the force field was tested on a set of 40 drug-like molecules and found to be very good where observed crystal structures are found at the top of the ranked list of tentative crystal structures. Re-ranking with dispersion-corrected density functional theory (DFT-D) methods further improves the scoring. After DFT-D geometry optimization the observed crystal structure is found at the leading top of the ranking list. DFT-D methods and force field methods have been evaluated for use in predicting properties such as phase transitions upon heating, mechanical properties or intrinsic crystalline solubility. The utility of using crystal structure predictions and the associated material properties in risk assessment in connection with form selection in the drug development process is discussed.

scholarly journals Substituent Effects in 3,3’ Bipyrazole Derivatives. X-ray Crystal Structures, Molecular Properties and DFT Analysis

2021 ◽  
Vol 68 (3) ◽  
pp. 718-727
Author(s):  
Ibrahim Bouabdallah ◽  
Tarik Harit ◽  
Mahmoud Rahal ◽  
Fouad Malek ◽  
Monique Tillard ◽  
...  
Keyword(s):  
Crystal Structure ◽  
Density Functional Theory ◽  
Solid State ◽  
Dft Calculations ◽  
Single Crystal ◽  
Crystal Structures ◽  
Density Functional ◽  
Substituent Effects ◽  
Functional Theory ◽  
X Ray

The single crystal X-ray structure of new 1,1’-bis(2-nitrophenyl)-5,5’-diisopropyl-3,3’-bipyrazole, 1, is triclinic P I–, a = 7.7113(8), b = 12.3926(14), c = 12.9886(12) Å, α = 92.008(8), β = 102.251(8), γ = 99.655(9)°. The structural arrangement is compared to that of 5,5’-diisopropyl-3,3’-bipyrazole, 5, whose single crystal structure is found tetragonal I41/a, a = b = 11.684(1), c = 19.158(1) Å. The comparison is also extended to the structures previously determined for 1,1’-bis(2-nitrophenyl)-5,5’-propyl-3,3’-bipyrazole, 2, 1,1’-bis(4-nitrophenyl)-5,5’-diisopropyl-3,3’-bipyrazole, 3, and 1,1’-bis(benzyl)-5,5’-diisopropyl-3,3’-bipyrazole, 4. Density Functional Theory (DFT) calculations are used to investigate the molecular geometries and to determine the global reactivity parameters. The geometry of isolated molecules and the molecular arrangements in the solid state are analyzed according to the nature of the groups connected to the bipyrazole core.

scholarly journals Resolving alternative organic crystal structures using density functional theory and NMR chemical shifts

2020 ◽  
Vol 11 (11) ◽  
pp. 2987-2992 ◽  
Author(s):  
Cory M. Widdifield ◽  
James D. Farrell ◽  
Jason C. Cole ◽  
Judith A. K. Howard ◽  
Paul Hodgkinson
Keyword(s):  
Crystal Structure ◽  
Density Functional Theory ◽  
Crystal Structures ◽  
Density Functional ◽  
Chemical Shifts ◽  
Organic Crystal ◽  
Functional Theory ◽  
Nmr Chemical Shifts ◽  
Structure Determinations

DFT optimisation often resolves conflicting crystal structure determinations, with NMR shifts helping in cases where optimisation diverges to different structures.

scholarly journals Multi-fidelity Statistical Machine Learning for Molecular Crystal Structure Prediction

2020 ◽  
Author(s):  
Olga Egorova ◽  
Roohollah Hafizi ◽  
David C. Woods ◽  
Graeme Day
Keyword(s):  
Crystal Structure ◽  
Machine Learning ◽  
Force Field ◽  
Crystal Structures ◽  
Structure Prediction ◽  
Density Functional ◽  
Region Of Interest ◽  
State Density ◽  
Statistical Machine Learning ◽  
Hybrid Functional

The prediction of crystal structures from first principles requires highly accurate energies for large numbers of putative crystal structures. The accuracy of solid state density functional theory (DFT) calculations is often required, but hundreds or more structures can be present in the low energy region of interest, so that the associated computational costs are prohibitive. Here, we apply statistical machine learning to predict expensive hybrid functional DFT (PBE0) calculations using a multi-fidelity approach to re-evalute the energies of crystal structures predicted with an inexpensive force field. The method uses an autoregressive Gaussian process, making use of less expensive GGA DFT (PBE) calculations to bridge the gap between the force field and PBE0 energies. The method is benchmarked on the crystal structure landscapes of three small, hydrogen bonding organic molecules and shown to produce accurate predictions of energies and crystal structure ranking using small numbers of the most expensive calculations; the PBE0 energies can be predicted with errors of less than 1 kJ/mol with between 4.2-6.8% of the cost of the full calculations. As the model that we have developed is probabilistic, we discuss how the uncertainties in predicted energies impact on assessment of the energetic ranking of crystal structures.

scholarly journals Crystal structure prediction of indomethacin

2014 ◽  
Vol 70 (a1) ◽  
pp. C1540-C1540
Author(s):  
Xiaozhou Li ◽  
Kristoffer Johansson ◽  
Andrew Bond ◽  
Jacco van de Streek
Keyword(s):  
Crystal Structure ◽  
Force Field ◽  
Crystal Structures ◽  
Structure Prediction ◽  
Molecular Crystal ◽  
Density Functional ◽  
Computational Cost ◽  
Stable Form ◽  
Crystal Structure Prediction ◽  
Blind Test

Indomethacin is a non-steroidal anti-inflammatory and antipyretic agent. Because different packing arrangements of the same drug can greatly affect drug properties such as colours, solubility, stability, melting point, dissolution rate and so forth, it is important to predict its polymorphs. The computational prediction of the stable form will reduce undesirable risks in both clinical trials and manufacturing. Reported polymorphs of indomethacin include α, β, γ, δ, ε, η and ζ [1], of which only the thermodynamically stable form γ and the metastable form α are determined. Density functional theory with dispersion-correction (DFT-D) has been used extensively to study molecular crystal structures[2]. It gives better results with a compromise between the computational cost and accuracy towards the reproduction of molecular crystal structures. In the fourth blind test of crystal structure prediction in 2007, the DFT-D method gave a very successful result that predicted all four structures correctly. Rather than using transferable force fields, a dedicated tailor-made force field (TMFF) parameterised by DFT-D calculations[3] is used for every chemical compound. The force field is used to generate a set of crystal structures and delimit a candidate window for energy ranking. The powder diffraction patterns of predicted polymorphs are calculated to compare with experimental data.

scholarly journals Multi-fidelity Statistical Machine Learning for Molecular Crystal Structure Prediction

2020 ◽  
Author(s):  
Olga Egorova ◽  
Roohollah Hafizi ◽  
David C. Woods ◽  
Graeme Day
Keyword(s):  
Crystal Structure ◽  
Machine Learning ◽  
Force Field ◽  
Crystal Structures ◽  
Structure Prediction ◽  
Density Functional ◽  
Region Of Interest ◽  
State Density ◽  
Statistical Machine Learning ◽  
Hybrid Functional

The prediction of crystal structures from first principles requires highly accurate energies for large numbers of putative crystal structures. The accuracy of solid state density functional theory (DFT) calculations is often required, but hundreds or more structures can be present in the low energy region of interest, so that the associated computational costs are prohibitive. Here, we apply statistical machine learning to predict expensive hybrid functional DFT (PBE0) calculations using a multi-fidelity approach to re-evalute the energies of crystal structures predicted with an inexpensive force field. The method uses an autoregressive Gaussian process, making use of less expensive GGA DFT (PBE) calculations to bridge the gap between the force field and PBE0 energies. The method is benchmarked on the crystal structure landscapes of three small, hydrogen bonding organic molecules and shown to produce accurate predictions of energies and crystal structure ranking using small numbers of the most expensive calculations; the PBE0 energies can be predicted with errors of less than 1 kJ/mol with between 4.2-6.8% of the cost of the full calculations. As the model that we have developed is probabilistic, we discuss how the uncertainties in predicted energies impact on assessment of the energetic ranking of crystal structures.

Synthesis, Conformational Analysis and Crystal Structure of New Thioxo, Oxo, Seleno Diastereomeric Cyclophosphamides Containing 1,3,2-dioxaphosphorinane

2019 ◽  
Vol 23 (2) ◽  
pp. 205-213
Author(s):  
Dorra Kanzari-Mnallah ◽  
Med L. Efrit ◽  
Jiří Pavlíček ◽  
Frédéric Vellieux ◽  
Habib Boughzala ◽  
...  
Keyword(s):  
Molecular Structure ◽  
Crystal Structure ◽  
Density Functional ◽  
Chemical Shifts ◽  
Structural Determination ◽  
Conformational Study ◽  
Functional Theory ◽  
One Step ◽  
High Resolution Mass Spectroscopy ◽  
Resolution Mass

Thioxo, Oxo and Seleno diastereomeric cyclophosphamides containing 1,3,2- dioxaphosphorinane are prepared by a one-step chemical reaction. Their structural determination is carried out by means of Nuclear Magnetic Resonance NMR (31P, 1 H, 13C) and High-Resolution Mass Spectroscopy (HRMS). The conformational study of diastereomeric products is described. Density Functional Theory (DFT) calculations allowed the identification of preferred conformations. Experimental and calculated 31P, 13C, 1H NMR chemical shifts are compared. The molecular structure of the 2-Benzylamino-5-methyl-5- propyl-2-oxo-1,3,2-dioxaphosphorinane (3d) has been determined by means of crystal Xray diffraction methods.

scholarly journals Computational Study of the Electron Spectra of Vapor-Phase Indole and Four Azaindoles

Molecules ◽  
2021 ◽  
Vol 26 (7) ◽  
pp. 1947
Author(s):  
Delano P. Chong
Keyword(s):  
Density Functional Theory ◽  
Vapor Phase ◽  
Density Functional ◽  
Computational Study ◽  
Geometry Optimization ◽  
The Other ◽  
Functional Theory ◽  
Electron Spectra

After geometry optimization, the electron spectra of indole and four azaindoles are calculated by density functional theory. Available experimental photoemission and excitation data for indole and 7-azaindole are used to compare with the theoretical values. The results for the other azaindoles are presented as predictions to help the interpretation of experimental spectra when they become available.

Crystal structure of pomalidomide Form I, C13H11N3O4

2021 ◽  
pp. 1-6
Author(s):  
James A. Kaduk ◽  
Amy M. Gindhart ◽  
Thomas N. Blanton
Keyword(s):  
Crystal Structure ◽  
Hydrogen Bonds ◽  
Powder Diffraction ◽  
Density Functional ◽  
Double Layers ◽  
Carbonyl Groups ◽  
Hydrogen Atoms ◽  
Powder Diffraction File ◽  
Functional Theory ◽  
Amine Hydrogen

The crystal structure of pomalidomide Form I has been solved and refined using synchrotron X-ray powder diffraction data and optimized using density functional theory techniques. Pomalidomide Form I crystallizes in the space group P-1 (#2) with a = 7.04742(9), b = 7.89103(27), c = 11.3106(6) Å, α = 73.2499(13), β = 80.9198(9), γ = 88.5969(6)°, V = 594.618(8) Å3, and Z = 2. The crystal structure is characterized by the parallel stacking of planes parallel to the bc-plane. Hydrogen bonds link the molecules into double layers also parallel to the bc-plane. Each of the amine hydrogen atoms acts as a donor to a carbonyl group in an N–H⋯O hydrogen bond, but only two of the four carbonyl groups act as acceptors in such hydrogen bonds. Other carbonyl groups participate in C–H⋯O hydrogen bonds. The powder pattern has been submitted to ICDD® for inclusion in the Powder Diffraction File™ (PDF®).

scholarly journals Role of Chemistry and Crystal Structure on the Electronic Defect States in Cs-Based Halide Perovskites

Materials ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1032
Author(s):  
Anirban Naskar ◽  
Rabi Khanal ◽  
Samrat Choudhury
Keyword(s):  
Crystal Structure ◽  
Density Functional ◽  
Covalent Bond ◽  
Density Functional Theory Calculations ◽  
Antisite Defect ◽  
Defect States ◽  
Functional Theory ◽  
Electronic Defect ◽  
Constituent Elements

The electronic structure of a series perovskites ABX3 (A = Cs; B = Ca, Sr, and Ba; X = F, Cl, Br, and I) in the presence and absence of antisite defect XB were systematically investigated based on density-functional-theory calculations. Both cubic and orthorhombic perovskites were considered. It was observed that for certain perovskite compositions and crystal structure, presence of antisite point defect leads to the formation of electronic defect state(s) within the band gap. We showed that both the type of electronic defect states and their individual energy level location within the bandgap can be predicted based on easily available intrinsic properties of the constituent elements, such as the bond-dissociation energy of the B–X and X–X bond, the X–X covalent bond length, and the atomic size of halide (X) as well as structural characteristic such as B–X–B bond angle. Overall, this work provides a science-based generic principle to design the electronic states within the band structure in Cs-based perovskites in presence of point defects such as antisite defect.

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