A non-empirical intermolecular force-field for trinitrobenzene and its application in crystal structure prediction

2021 ◽  
Vol 154 (9) ◽  
pp. 094123
Author(s):  
Alex A. Aina ◽  
Alston J. Misquitta ◽  
Sarah L. Price
Keyword(s):  
Crystal Structure ◽  
Force Field ◽  
Structure Prediction ◽  
Crystal Structure Prediction ◽  
Intermolecular Force

scholarly journals Comparing hypothetical structures generated in the third Cambridge blind test of crystal structure prediction

2005 ◽  
Vol 61 (5) ◽  
pp. 528-535 ◽  
Author(s):  
Bouke P. van Eijck
Keyword(s):  
Crystal Structure ◽  
Force Field ◽  
Structure Prediction ◽  
Crystal Structure Prediction ◽  
Blind Test ◽  
Good Correspondence ◽  
The Third ◽  
New Energy ◽  
Order Of Magnitude ◽  
True Energy

In the third Cambridge blind test of crystal structure prediction, participants submitted extended lists of up to 100 hypothetical structures. In this paper these lists are analyzed for the two small semi-rigid molecules, hydantoin and azetidine, by performing a new energy minimization using an accurate force field, and grouping these newly minimized structures into clusters of equivalent structures. Many participants found the same low-energy structures, but no list appeared to be complete even for the structures with one independent molecule in the asymmetric unit. This may well be due to the fact that a cutoff at even 100 structures cannot ensure the presence of a structure that has a relatively high ranking in another force field. Moreover, some structures should have possibly been discarded because they correspond to transition states rather than true energy minima. The r.m.s. deviation between energies in corresponding clusters was calculated to compare the reported relative crystal energies for each pair of participants. Some groups of force fields show a reasonably good correspondence, yet the order of magnitude of their discrepancies is comparable to the energy differences between, say, the first ten structures of lowest energy. Therefore, even if we assume that energy is a sufficient criterion, it is not surprising that crystal structure predictions are still inconsistent and unreliable.

Modelling the crystal structure of the 2-hydronitronylnitroxide radical (HNN): observed and computer-generated polymorphs

1999 ◽  
Vol 55 (4) ◽  
pp. 543-553 ◽  
Author(s):  
G. Filippini ◽  
A. Gavezzotti ◽  
J. J. Novoa
Keyword(s):  
Crystal Structure ◽  
Force Field ◽  
Crystal Structures ◽  
Structure Prediction ◽  
Crystal Structure Prediction ◽  
X Ray ◽  
Narrow Energy Range ◽  
Coulomb Type ◽  
Atomic Coordinates ◽  
Hydrogen Bonded

The crystal structures of two polymorphs of 4,4,5,5-tetramethyl-4,5-dihydro-1H-imidazol-1-oxyl 3-oxide (the 2-hydronitronylnitroxide radical, HNN) are analyzed by packing energy criteria. Other unobserved polymorphic crystal structures are generated using a polymorph predictor package and three different force fields, one of which is without explicit Coulomb-type terms. The relative importance of several structural motifs (hydrogen-bonded dimers, shape-interlocking dimers or extended hydrogen-bonded chains) is discussed. As usual, many crystal structures within a narrow energy range are generated by the polymorph predictor, confirming that ab initio crystal-structure prediction is still problematic. Comparisons of powder patterns generated from the atomic coordinates of the X-ray structure and from computational crystal structures confirm that although the energy ranking depends on the force field used, the X-ray structure of the \alpha polymorph was found to be among the most stable ones produced by the polymorph predictor, even using the chargeless force field.

scholarly journals Computational Pharmaceutical Materials Science: Beyond Static Structures.

2014 ◽  
Vol 70 (a1) ◽  
pp. C1541-C1541
Author(s):  
Jacco van de Streek ◽  
Kristoffer Johansson ◽  
Xiaozhou Li
Keyword(s):  
Crystal Structure ◽  
Molecular Dynamics ◽  
Force Field ◽  
Crystal Structures ◽  
Structure Prediction ◽  
Force Fields ◽  
Individual Compound ◽  
Crystal Structure Prediction ◽  
Temperature Dependent ◽  
Mechanical Methods

The five Crystal-Structure Prediction (CSP) Blind Tests have shown that molecular-mechanics force fields are not accurate enough for crystal structure prediction[1]. The first--and only--method to successfully predict all four target crystal structures of one of the CSP Blind Tests was dispersion-corrected Density Functional Theory (DFT-D), and this is what we use for our work. However, quantum-mechanical methods (such as DFT-D), are too slow to allow simulations that include the effects of time and temperature, certainly for the size of molecules that are common in pharmaceutical industry. Including the effects of time and temperature therefore still requires molecular dynamics (MD) with less accurate force fields. In order to combine the accuracy of the successful DFT-D method with the speed of a force field to enable molecular dynamics, our group uses Tailor-Made Force Fields (TMFFs) as described by Neumann[2]. In Neumann's TMFF approach, the force field for each chemical compound of interest is parameterised from scratch against reference data from DFT-D calculations; in other words, the TMFF is fitted to mimic the DFT-D energy potential. Parameterising a dedicated force field for each individual compound requires an investment of several weeks, but has the advantage that the resulting force field is more accurate than a transferable force field. Combining crystal-structure prediction with DFT-D followed by molecular dynamics with a tailor-made force field allows us to calculate e.g. the temperature-dependent unit-cell expansion of each predicted polymorph, as well as possible temperature-dependent disorder. This is relevant for example when comparing the calculated X-ray powder diffraction patterns of the predicted crystal structures against experimental data.

scholarly journals Machine-Learnt Fragment-Based Energies for Crystal Structure Prediction

2019 ◽  
Author(s):  
David McDonagh ◽  
Chris-Kriton Skylaris ◽  
Graeme Day
Keyword(s):  
Crystal Structure ◽  
Machine Learning ◽  
Force Field ◽  
Structure Prediction ◽  
Alternative Energy ◽  
Force Fields ◽  
Training Data ◽  
Crystal Structure Prediction ◽  
Energy Models ◽  
Lattice Energies

Crystal structure prediction involves a search of a complex configurational space for local minima corresponding to stable crystal structures, which can be performed efficiently using atom-atom force fields for the assessment of intermolecular interactions. However, for challenging systems, the limitations in the accuracy of force fields prevents a reliable assessment of the relative thermodynamic stability of potential structures. Here we present a method to rapidly improve force field lattice energies by correcting two-body interactions with a higher level of theory in a fragment-based approach, and predicting these corrections with machine learning. We find corrected lattice energies with commonly used density functionals and second order perturbation theory (MP2) all significantly improve the ranking of experimentally known polymorphs where the rigid molecule model is applicable. The relative lattice energies of known polymorphs are also found to systematically improve towards experimentally determined values and more comprehensive energy models when using MP2 corrections, despite remaining at the force field geometry. Predicting two-body interactions with atom-centered symmetry functions in a Gaussian process is found to give highly accurate results with as little as 10-20% of the training data, reducing the cost of the energy correction by up to an order of magnitude. The machine learning approach opens up the possibility of using fragment-based methods to a greater degree in crystal structure prediction, providing alternative energy models where standard approaches are insufficient.

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 Machine-Learnt Fragment-Based Energies for Crystal Structure Prediction

2019 ◽  
Author(s):  
David McDonagh ◽  
Chris-Kriton Skylaris ◽  
Graeme Day
Keyword(s):  
Crystal Structure ◽  
Machine Learning ◽  
Force Field ◽  
Structure Prediction ◽  
Alternative Energy ◽  
Force Fields ◽  
Training Data ◽  
Crystal Structure Prediction ◽  
Energy Models ◽  
Lattice Energies

Crystal structure prediction involves a search of a complex configurational space for local minima corresponding to stable crystal structures, which can be performed efficiently using atom-atom force fields for the assessment of intermolecular interactions. However, for challenging systems, the limitations in the accuracy of force fields prevents a reliable assessment of the relative thermodynamic stability of potential structures. Here we present a method to rapidly improve force field lattice energies by correcting two-body interactions with a higher level of theory in a fragment-based approach, and predicting these corrections with machine learning. We find corrected lattice energies with commonly used density functionals and second order perturbation theory (MP2) all significantly improve the ranking of experimentally known polymorphs where the rigid molecule model is applicable. The relative lattice energies of known polymorphs are also found to systematically improve towards experimentally determined values and more comprehensive energy models when using MP2 corrections, despite remaining at the force field geometry. Predicting two-body interactions with atom-centered symmetry functions in a Gaussian process is found to give highly accurate results with as little as 10-20% of the training data, reducing the cost of the energy correction by up to an order of magnitude. The machine learning approach opens up the possibility of using fragment-based methods to a greater degree in crystal structure prediction, providing alternative energy models where standard approaches are insufficient.

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