Accurate lattice energies of organic molecular crystals from periodic turbomole calculations

Buchholz, Hannes Konrad; Stein, Matthias

doi:10.1002/jcc.25205

Cited by 18 publications

(27 citation statements)

References 66 publications

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“…CP corrections also showed that a basis set error (BSSE) for the def2-TZVP basis set was, at most, 7% of the interaction energy of molecular compounds in the crystal. This reduced the calculated lattice energy and brought it even closer to experiment [23]. Further work on the implementation of the BSSE correction into periodic DFT with Gaussian basis functions is ongoing.…”

Section: Methodsmentioning

confidence: 94%

“…The lattice energies for the organic molecules of the X23 benchmark set and related chiral compounds were found to have almost converged using the medium-sized, triple-zeta def2-TZVP basis set; the larger def2-TZVPP basis set did not significantly change the calculated lattice energies for representatives of either the vdW, mixed, and hydrogen-bonded molecular classes [23]. CP corrections also showed that a basis set error (BSSE) for the def2-TZVP basis set was, at most, 7% of the interaction energy of molecular compounds in the crystal.…”

Section: Methodsmentioning

confidence: 95%

“…The 3 × 3 × 3 k-points specify the unit cell surrounded by the adjacent replicates of the cell, and interactions with atoms from neighboring unit cells are taken into account. It could be shown that a k-point sampling of 3 × 3 × 3 is necessary to achieve a convergence of lattice energies for molecular crystals [23].…”

Section: Methodsmentioning

confidence: 99%

“…Previous results have shown that periodic DFT calculations with atom-centered basis functions in Turbomole are able to give reliable lattice energies for the molecules of the X23 benchmark set, and energy differences for enantiopure vs. racemic crystal forms using the PBE-D3 functional [23].…”

Section: = − (1)mentioning

confidence: 99%

“…A careful assessment of the performance of the B97-D exchange correlation functional for the X23 small molecular crystal benchmark set is presented; it was shown to be superior for chiral model compounds [23], but was not systematically evaluated. It outperforms the previous PBE-D3 results, in particular for weak vdW and mixed vdW/hydrogen bonding interactions, and gives a root mean square error of 5 kJ/mol in lattice energies.…”

Section: = − (1)mentioning

confidence: 99%

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Intermolecular Interactions in Molecular Organic Crystals upon Relaxation of Lattice Parameters

Stein

Heimsaat

2019

Crystals

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Crystal structure prediction is based on the assumption that the most thermodynamically stable structure will crystallize first. The existence of other structures such as polymorphs or from counterenantiomers requires an accurate calculation of the electronic energy. Using atom-centered Gaussian basis functions in periodic Density Functional Theory (DFT) calculations in Turbomole, the performance of two dispersion-corrected functionals, PBE-D3 and B97-D, is assessed for molecular organic crystals of the X23 benchmark set. B97-D shows a MAE (mean absolute error) of 4 kJ/mol, compared to 9 kJ/mol for PBE-D3. A strategy for the convergence of lattice energies towards the basis set limit is outlined. A simultaneous minimization of molecular structures and lattice parameters shows that both methods are able to reproduce experimental unit cell parameters to within 4–5%. Calculated lattice energies, however, deviate slightly more from the experiment, i.e., by 0.4 kJ/mol after unit cell optimization for PBE-D3 and 0.5 kJ/mol for B97-D. The accuracy of the calculated lattice energies compared to the experimental values demonstrates the ability of current DFT methods to assist in the quest for possible polymorphs and enantioselective crystallization processes.

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Section: Methodsmentioning

confidence: 94%

Section: Methodsmentioning

confidence: 95%

Section: Methodsmentioning

confidence: 99%

Section: = − (1)mentioning

confidence: 99%

Section: = − (1)mentioning

confidence: 99%

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Intermolecular Interactions in Molecular Organic Crystals upon Relaxation of Lattice Parameters

Stein

Heimsaat

2019

Crystals

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Using atomic clustering based on structural and electronic descriptors that consider surrounding environment to evaluate local properties of DFT functionals

Nakajima,

Ohmura,

Seino

2024

J Comput Chem

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We developed a method for evaluating the accuracies of the local properties of DFT functionals in detail using a clustering method based on machine learning and structural/electronic descriptors. We generated 36 clusters consistent with human intuition using 30,436 carbon atoms from the QM9 dataset. The results were used to evaluate 13C NMR chemical shifts calculated using 84 DFT functionals. Carbon atoms were grouped based on their similar environments, reducing errors within these groups. This enables more accurate assessment of the accuracy using a specific DFT functional. Therefore, the present atomic clustering provides more detailed insight into accuracy verification.

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Band Gap Prediction for Large Organic Crystal Structures with Machine Learning

et al. 2019

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Machine‐learning models are capable of capturing the structure–property relationship from a dataset of computationally demanding ab initio calculations. Over the past two years, the Organic Materials Database (OMDB) has hosted a growing number of calculated electronic properties of previously synthesized organic crystal structures. The complexity of the organic crystals contained within the OMDB, which have on average 82 atoms per unit cell, makes this database a challenging platform for machine learning applications. In this paper, the focus is on predicting the band gap which represents one of the basic properties of a crystalline material. With this aim, a consistent dataset of 12 500 crystal structures and their corresponding DFT band gap are released, freely available for download at https://omdb.mathub.io/dataset. An ensemble of two state‐of‐the‐art models reach a mean absolute error (MAE) of 0.388 eV, which corresponds to a percentage error of 13% for an average band gap of 3.05 eV. Finally, the trained models are employed to predict the band gap for 260 092 materials contained within the Crystallography Open Database (COD) and made available online so that the predictions can be obtained for any arbitrary crystal structure uploaded by a user.

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Accurate lattice energies of organic molecular crystals from periodic turbomole calculations

Cited by 18 publications

References 66 publications

Intermolecular Interactions in Molecular Organic Crystals upon Relaxation of Lattice Parameters

Intermolecular Interactions in Molecular Organic Crystals upon Relaxation of Lattice Parameters

Using atomic clustering based on structural and electronic descriptors that consider surrounding environment to evaluate local properties of DFT functionals

Band Gap Prediction for Large Organic Crystal Structures with Machine Learning

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