The Computational Materials Repository

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12 Introductionis an integral part in daily routine of biologist researchers.Other biological databases are Ensemble[2], which stores information aboutthe genome, or the Protein Data Bank (PDB)[3] - which contain published3D structures of proteins; a good example of collaborate efforts is the HumanGenome Project[4] that was initiated in 1990 and aimed at sequencing thewhole human genome. Many institutes around the world were participating insequencing, analyzing, annotating and submitting data.The electronic structure calculation community is not there yet. Especiallywe are far from a cross-linked freely available database where researchers canupload their data and put it in context with already available data in order toderive new results - however partial solutions exist: The Virtual Materials DesignFramework (VMDF)[5], a tool to filter and analyze aggregated sets of electronicstructure data introduces a first step towards user-friendly analysis of data, theCAMd database[6] or AFlowLib[7] that present a selection of aggregated dataon the Internet and make it available through a web interface - or ICSD, theInorganic Crystal Structure Database[8, 9] that collects validated experimentallydetermined data, and provides a software tool and a web interface to performsearches - unfortunately not for free. During the last eight months more elaboratetools and databases have seen the light of day: ESTEST[10] aims at validationand verification of electronic structure calculations of different codes; MaterialsGenome[11, 12] has a high-throughput infrastructure to perform screenings andpresents results through toolboxes that are accessible on the Internet - andthe Quixote[13, 14] project that focuses on quantum chemistry data presents acollaborative open source framework to collect and process data from differentsources.The reasons why we are not there is that it is hard to find a commonapplicable scheme that is able to cover most aspects. The electronic structuredata is for instance more heterogeneous - there are numerous file formats - andthe the analysis depends greatly on the perused goal. The challenges of handlingdata in an efficient and reusable way can be divided in a few mature tasks asrepresented in Fig. 1.1.
13Figure 1.1: Major tasks when aggregating data - the arrows represent the work flow.Calculating produces results that need to be collected in a commonly readableformat. These results could then be stored in a database for further analysis. Thepresentation can be done in a web-interface or any other tool that is able to retrievedata from the storage. Important is to notice that analysis can produce derived datathat should be added to the storage as well.With the Computational Materials Repository (CMR) that is presentedin this document, we provide a modular open source system that addressesthe challenges of collecting, storing, making data available through interfacesand propose and implement basic ideas how to perform analysis. The focusis in particular on DFT codes because they represent a favorable trade-offbetween speed and accuracy for the treatment of “few-hundred-atom” systemshighly relevant for understanding physical and chemical properties of materials.We propose a file-format (db-files) that holds extracted data syntactically andsemantically as close as possible to the original output file - and introduce groupsto indicate connections between data. The Python user interface allows to searchand query data which then can be analysed. In order to cope with differentassumptions on names and units, CMR implements mappings. A mappingenables to rename and convert for example the field CartesianPositions inBohr units, to positions in Angstrom.To get a user-friendly way of looking at the data, the PHP/HTML interfacepresents the data with the help of a MySQL database and a webserver as shownin Fig. 1.2. When the data is published it is possible to create a custom view asshown in Fig. 1.3 to make the results easier accessible for public.
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90 BIBLIOGRAPHY[11] Anubhav Jain, G
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