<i>COD::CIF::Parser</i>: an error-correcting CIF parser for the Perl language

Merkys, Andrius; Vaitkus, Antanas; Butkus, Justas; Okulič-Kazarinas, Mykolas; Kairys, Visvaldas; Gražulis, S.

doi:10.1107/s1600576715022396

Cited by 193 publications

(126 citation statements)

References 21 publications

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“…In the recent decades, the amount of scientific data collected has facilitated the emergence of new data-driven approaches in the search for novel functional materials. Scientific data has been made accessible in terms of a multitude of online databases, e.g., for crystal structures [1][2][3][4], electronic structures and materials properties [5][6][7][8][9], enzymes and pharmaceutics [10,11], or superconductors [12,13]. In contrast to pure data-mining approaches, which focus on extracting knowledge from existing data * bartol@kth.se † geilhufe@kth.se ‡ stabo@dtu.dk [14][15][16], machine learning approaches try to predict target properties directly, where a highly non-linear map between a crystal structure and its functional property of interest is approximated.…”

Section: Introductionmentioning

confidence: 99%

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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Section: Introductionmentioning

confidence: 99%

Band Gap Prediction for Large Organic Crystal Structures with Machine Learning

et al. 2019

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“…3a. All reflections can be indexed according to the structure of monoclinic chalcocite (space group P2 1 /c, [34][35][36][37][38][39][40]). The small discrepancies between the Rietveld best fit and the experimental data are compatible with wurtzite structure (space group P 6 3 mc [41]).…”

Section: Powder Scansmentioning

confidence: 99%

Operando SXRD of E-ALD deposited sulphides ultra-thin films: Crystallite strain and size

Giaccherini

Russo

Carlà

et al. 2018

Applied Surface Science

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a b s t r a c tElectrochemical Atomic Layer Deposition (E-ALD), exploiting surface limited electrodeposition of atomic layers, can easily grow highly ordered ultra-thin films and 2D structures. Among other compounds Cu x Zn y S grown by means of E-ALD on Ag(111) has been found particularly suitable for the solar energy conversion due to its band gap (1.61 eV). However its growth seems to be characterized by a micrometric thread-like structure, probably overgrowing a smooth ultra-thin films. On this ground, a SXRD investigation has been performed, to address the open questions about the structure and the growth of Cu x Zn y S by means of E-ALD. The experiment shows a pseudo single crystal pattern as well as a powder pattern, confirming that part of the sample grows epitaxially on the Ag(111) substrate. The growth of the film was monitored by following the evolution of the Bragg peaks and Debye rings during the E-ALD steps. Breadth and profile analysis of the Bragg peaks lead to a qualitative interpretation of the growth mechanism. This study confirms that Zn lead to the growth of a strained Cu 2 S-like structure, while the growth of the thread-like structure is probably driven by the release of the stress from the epitaxial phase.

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“…A substructure search in the Crystallography Open Database (COD) for molecules containing the 2‐methoxy‐naphthalene moiety with hydrogen substituents in ortho and ortho’ positions of the methoxy group yielded a total of 174 hits. These 3D structures were super‐imposed and aligned on the aromatic core, as shown in Figure .…”

Section: Figurementioning

confidence: 99%

Existence of a Preferred Orientation for the Methoxy Group on an Extended Aromatic System.

et al. 2018

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The conformations of a methoxy group introduced on naphthalene in position 2 has been investigated both experimentally (searches in crystallographic data, NOE measurements by NMR) and theoretically with ab initio calculations. While expected at first glance as being equivalent and predicted as such by force field methods, the two conformations where the methoxy is in the plane of the aromatic ring and turned either towards one or the other proton of naphthalene are actually very different in energy : ΔE=1.2 to 1.8 kcal mol−1, a surprisingly high value! The delocalization of the oxygen lone pair on the π‐system of the aromatic ring differs between the two conformers and is proposed to account predominantly for such unexpectedly large ΔE. It is our opinion that given the magnitude of the energies involved, force field methods should be corrected to take into account this conformational effect.

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COD::CIF::Parser: an error-correcting CIF parser for the Perl language

Abstract: A syntax-correcting CIF parser, COD::CIF::Parser, is described that can parse CIF 1.1 files and accurately report the position and nature of the discovered syntactic problems while automatically correcting the most common and the most obvious syntactic deficiencies.

Cited by 193 publications

References 21 publications

Band Gap Prediction for Large Organic Crystal Structures with Machine Learning

Band Gap Prediction for Large Organic Crystal Structures with Machine Learning

Operando SXRD of E-ALD deposited sulphides ultra-thin films: Crystallite strain and size

Existence of a Preferred Orientation for the Methoxy Group on an Extended Aromatic System.

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