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The ever-growing availability of computing power and the sustained development of advanced computational methods have contributed much to recent scientific progress. These developments present new challenges driven by the sheer amount of calculations and data to manage. Next-generation exascale supercomputers will harden these challenges, such that automated and scalable solutions become crucial. In recent years, we have been developing AiiDA (aiida.net), a robust open-source high-throughput infrastructure addressing the challenges arising from the needs of automated workflow management and data provenance recording. Here, we introduce developments and capabilities required to reach sustained performance, with AiiDA supporting throughputs of tens of thousands processes/hour, while automatically preserving and storing the full data provenance in a relational database making it queryable and traversable, thus enabling high-performance data analytics. AiiDA’s workflow language provides advanced automation, error handling features and a flexible plugin model to allow interfacing with external simulation software. The associated plugin registry enables seamless sharing of extensions, empowering a vibrant user community dedicated to making simulations more robust, user-friendly and reproducible.

The Open Databases Integration for Materials Design (OPTIMADE) consortium aims to make materials databases interoperational by developing a common REST API. This repository contains the specification of the OPTIMADE API. optimade.rst: The API specification. AUTHORS: List of contributors. optimade.org: Public OPTIMADE web site OPTIMADE wiki: Information for developers The subdirectory schemas/ contains OpenAPI schemas for the main OPTIMADE API and index meta-database as implemented by the optimade-python-tools repository. Note: These schemas are an approximation of the full human-readable specification and may be missing certain constraints. Furthermore, they may not be up to date in the develop branch of this repository.

The Open Databases Integration for Materials Design (OPTIMADE) consortium aims to make materials databases interoperational by developing a common REST API. This repository contains the specification of the OPTIMADE API. optimade.rst: The API specification. AUTHORS: List of contributors. optimade.org: Public OPTIMADE web site OPTIMADE wiki: Information for developers The subdirectory schemas/ contains OpenAPI schemas for the main OPTIMADE API and index meta-database as implemented by the optimade-python-tools repository. Note: These schemas are an approximation of the full human-readable specification and may be missing certain constraints. Furthermore, they may not be up to date in the develop branch of this repository.

Poster presented at the ChemCuration 2019 online conference.

This dissertation describes fully automated means to extract geometric information – interatomic bond lengths, bond and dihedral angles – from small-molecule crystal structures, and to use this information for the validation of novel crystal structures. Crystallography Open Database (COD), regularly updated open-access resource of small-molecule crystal structures, has been chosen as the source of input data. Software has been developed to prefilter the records from the COD, transform them to a form appropriate for geometric analysis, extract and organise the geometric parameters. Statistical models chosen to describe the groups of chemically similar observations can be used for Bayesian method-based outlier detection: previously unseen, or seen relatively rarely, geometric observations in molecules in consideration are spotted and marked for further analysis. Software implementing this principle has been developed and a Web based user interface has been presented. The method for structure validation has been tested with novel, retracted and deliberately deformed small-molecule crystal structures. The main conclusions of this dissertation are that the COD is a proper resource for small-molecule geometric information, developed methods and software tools are sufficient to organise the data from the source database into a library of molecular geometry, which is in turn capable to spot unusual geometric features in small-molecule crystal structures.

This dissertation describes fully automated means to extract geometric information – interatomic bond lengths, bond and dihedral angles – from small-molecule crystal structures, and to use this information for the validation of novel crystal structures. Crystallography Open Database (COD), regularly updated open-access resource of small-molecule crystal structures, has been chosen as the source of input data. Software has been developed to prefilter the records from the COD, transform them to a form appropriate for geometric analysis, extract and organise the geometric parameters. Statistical models chosen to describe the groups of chemically similar observations can be used for Bayesian method-based outlier detection: previously unseen, or seen relatively rarely, geometric observations in molecules in consideration are spotted and marked for further analysis. Software implementing this principle has been developed and a Web based user interface has been presented. The method for structure validation has been tested with novel, retracted and deliberately deformed small-molecule crystal structures. The main conclusions of this dissertation are that the COD is a proper resource for small-molecule geometric information, developed methods and software tools are sufficient to organise the data from the source database into a library of molecular geometry, which is in turn capable to spot unusual geometric features in small-molecule crystal structures.

Modern advances in computational technology have facilitated great strides in a wide variety of scientific disciplines and have led to the production of a wealth of valuable research data. However, the extraordinarily quick growth of computational capabilities has left the scientific world wanting for a simple yet effective way of managing these new workflows and the vast amount of data that they produce. We present AiiDA[1], a highly-automated and robust workflow engine written in Python, designed for high-throughput computational science. AiiDA automatically tracks data provenance (and stores it in the form of a directed graph) while managing and automating simulations running either locally or on supercomputers. All data and calculations are stored in a database and can be efficiently queried thanks to a simple but powerful query language. AiiDA enables computational science that is fully reproducible and facilitates sharing of research results. Its symbiotic counterpart, the Materials Cloud [2], is an interactive online platform powered by AiiDA. It is designed to enable seamless sharing and dissemination of resources, of raw data with their provenance as well as of curated open research data in computational science, and to provide cloud resources for simulations. The combination of AiiDA and Materials Cloud (whose architecture is shown in the figure) provides an Open Science Framework fully compliant with the FAIR (findable, accessible, interoperable and reusable) data principles [3]. The diligent and dedicated data management of AiiDA, combined with the user-friendly and ergonomic Materials Cloud, form an invaluable tool to any computational scientist. [1] G. Pizzi et al., Comp. Mat. Sci. 111, 218 (2016) - www.aiida.net [2] http://www.materialscloud.org [3] M. D. Wilkinson et al, Sci. Data 3, 160018 (2016), Preprint submitted to RO2018 workshop at IEEE eScience Conference 2018, {"references":["G. Pizzi et al., Comp. Mat. Sci. 111, 218 (2016) - www.aiida.net","http://www.materialscloud.org","M. D. Wilkinson et al, Sci. Data 3, 160018 (2016)"]}

Additional file 1. List of automatic changes performed by the fixalot script and examples of COD entries with wrong compound names in the CIF.

Computer descriptions of chemical molecular connectivity are necessary for searching chemical databases and for predicting chemical properties from molecular structure. In this article, the ongoing work to describe the chemical connectivity of entries contained in the Crystallography Open Database (COD) in SMILES format is reported. This collection of SMILES is publicly available for chemical (substructure) search or for any other purpose on an open-access basis, as is the COD itself. The conventions that have been followed for the representation of compounds that do not fit into the valence bond theory are outlined for the most frequently found cases. The procedure for getting the SMILES out of the CIF files starts with checking whether the atoms in the asymmetric unit are a chemically acceptable image of the compound. When they are not (molecule in a symmetry element, disorder, polymeric species,etc.), the previously published cif_molecule program is used to get such image in many cases. The program package Open Babel is then applied to get SMILES strings from the CIF files (either those directly taken from the COD or those produced by cif_molecule when applicable). The results are then checked and/or fixed by a human editor, in a computer-aided task that at present still consumes a great deal of human time. Even if the procedure still needs to be improved to make it more automatic (and hence faster), it has already yielded more than 160,000 curated chemical structures and the purpose of this article is to announce the existence of this work to the chemical community as well as to spread the use of its results., The authors are grateful to the Junta de Andalucía (Research Group FQM-195) for financial support of the publication costs of this article.

Covalent radii are widely used to determine covalent bonds in small molecule crystals. However, there is no univocal published covalent radii table and most of these tables are derived based on data from databases that restrict usage of their data or derived results. Therefore, during this research five different methods of covalent radii derivation from open-access data were analysed. The methods are based on determination of the covalent bond length by locating the Van der Waals gap in distributions of distances between atoms. This is achieved by fitting a Gaussian mixture model to distribution of distances between atoms and using simplex method to determine the lowest density region between chosen components. As covalent bond length is equal to the sum of covalent radii of atoms that create the bond, covalent radii are derived by creating an overdetermined system of equations and solving it using the least squares method. During the research, automated tool for covalent radii derivation was created and covalent radii were derived using data from Crystallography Open Database, which is an open-source database. Calculated covalent radii were evaluated by comparing the results with published covalent radii tables.