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Streets of London, Evocative Watercolours by H. Tidmarsh PDF Download. PDF Online. The Law of One: Bk. The World's 60 Best Roast Dishes Tuff PDF Download. Anna Croft, Prof. Henry Rzepa. The user can browse through collections and get an idea of the type of calculation and the quality of metadata. Are the data coupled to publication? In CrystalEye almost all records are coupled to primary publications which can be read by the user assuming that they have access to the journal.
There is no technical barrier why this should not be done for articles and theses in computational chemistry. This is harder in compchem until the community develops a culture of publishing data concurrently with articles. Have the entries been annotated? This feature will shortly be available in Quixote, probably through blogging tools. Are there criteria for depositing an entry in the particular Quixote repository? Since we expect there to be many repositories, some of them can develop quality criteria for deposition.
Some, perhaps the majority, may have human curators. In the first instance it will be important that users can assess the quality of a particular Quixote repository and we are appealing to any scientist who have collections of computational chemistry data that they would be prepared to make available. We expect that there will be a range of levels of quality in Quixote repositories. For example a crawler visiting random web sites for data might store these in an "unvalidated" repository. Users could examine this for new interesting entries and make their own decisions as to their value.
The web has many evolved systems for the creation of quality metrics popularity, usage, recommendations, etc. A journal might set up their own repository as is done for crystallography. A department could expose its outputs and thereby gain metrics and esteem and the contents would be judged on the assessment of the creators. A small amount is added as appendixes to guide the reader. In any communal system requiring interoperability and heterogeneous contributions it is critical to agree concepts and construct the appropriate infrastructure.
Chemistry has few formal shared ontologies and Quixote explores the scope and implementation of this for QC. This is a community activity with medium-strong central management - the community has an input but there are formal procedures. It works extremely well and is universally adopted by crystallographers, instrument manufacturers, and publishers. The vocabulary and semantics have been developed over 20 years, are robust and capable of incremental extension.
We take this as a very strong exemplar for Quixote and more widely QC. We believe that almost all QC codes carry out calculations and create outpus which are isomorphic with other codes in the community. Thus an "electric dipole", "heat of formation" or a "wavefunction" is basically the same abstract concept across the field. The values and the representation will be code-dependent but with the appropriate conversions of say units, coordinate systems and labelling, it is possible to compare the output of one code with another.
This is a primary goal of Quixote, and we work by analysing the inputs and outputs of programs as well as top-down abstractions. It also means that Quixote is primarily concerned with what goes into and comes out of a calculation rather than what is held inside the machine the data model and the algorithms. From the human resource point of view, the Quixote project operates on a decentralised approach with no central site and with all participants contributing when available, and in whatever quantity they can donate at a particular time.
For that reason, different parts of the project progress at variable speeds and technically independently. This means that there is very little effort required in collating and synthesising other than the general ontological problem of agreeing within a community the meaning deployment and use of terms and concepts. The work is currently driven cf. This drives the need to write parsers, collate labels into dictionaries, and collate results.
The participants created tutorial material, wiki pages, examples and discussions which over the week focused us to a core set of between dictionary entries that should relate to any computational chemistry output. The initial approach has been to parse logfiles with JUMBO-Parser, as this can be applied to any legacy logfiles and does not require alterations of code.
At a later date we shall promote the use of CML-output libraries in major codes. At this stage it is probably the best approach to analyse the concepts and their structure. Ideally every part of every line is analysed and the semantic content extracted. In practice each new logfile instance can bring novel structure and syntax but it is straightforward to determine which sections have been parsed and which have not.
Parsing failure may be because a parser has not been written for those sections, or because the syntax varies between different problems and runs. The parser writer can then determine whether the un-parsed sections are important enough to devote effort to, or whether they are of minor importance and can be effectively deleted.
The process is highly iterative. The parser templates do not cover all possible document sections and initially some parts remain unparsed. The parsers are then amended and re-run; it is relatively simple in XML to determine which parts still need work. Each time a parse fails, the section is added as a failing unit test to the template and these also act as tutorial material and a primary source of semantics for the dictionary entries. Quixote is designed as a bottom-up community project and co-ordinated through the modern metaphors of wikis, mailing lists, Etherpads and distributed autonomous implementations.
Recognition of common document fragments in the logfile e. We create a template for each such chunk , which contains records , with regexes for each record that we wish to match and from which we will extract information. These templates can be nested, often representing the internal structure of the program e.
Each template is then used to match any chunks in the document, which are then regarded as completed and unavailable to other templates. The strategy allows for nesting and a small amount of back-tracking. Chunks of document that are not parsed may then be extracted by writing additional parsers, very often to clean up records such as error messages or timing information. This document is rarely fit for purpose in Quixote or other CML conventions and a second phase of transformation is applied.
This carries out the following:. Annotation of modules to reflect semantic purpose, e. This approach means that failures are relatively silent a strange document does not crash the process and that changes can be made external to the software by modifying the transformation files. As with the templates this should make it easier for the community to maintain the process e. To help in the parsing, there are a large number of unit and regression tests.
The dictionaries are in a constant state of update and consist of a reference implementation on the CML site and a working dictionary associated with the JUMBO-Converters distribution. As concepts are made firm in the latter, they are transferred to the reference dictionary. The current compchem dictionary is shown in Appendix B. It contains about 90 terms which are independent of the codes. We expect that about the same amount again will be added to deal with other properties and solid state concepts.
Lensfield2 requires a build file, defining the various sets of input files and the conversions to be applied to them. Like make , for instance, Lensfield2 is able to detect when files have changed, and update the products of conversions depending on them. However, unlike make where this is just done through comparison of files 'last-modified times, Lensfield2 records the complete build-state, so is able to detect any change in configuration, such as when the parameterisation of builds has changed, and when versions of tools involved in the various steps of the workflow are updated or if intermediate files are altered.
Lensfield2 has been successfully used in running the parser and subsequent software over the 40, files in the test datasets v. It is important that the methods for "uploading" and "downloading" files are as flexible as possible. Some collaborators may not have privilleges to run their own server, so they need to be able to upload material to a resource run by other collaborators. However, if the protocols are complex then they may be put off taking part.
Similarly, others may wish to delegate this to software agents which poll resources and aggregate material for uploading. Similar variability exists in the download process. We do not expect a single solution to cover everything, and the more emphasis on security, the more effort required. In this phase of Quixote, we are publishing our work to the whole world and do not expect problems of corruption or misappropriation.
We have therefore relied on simple proven solutions such as RESTful systems. Quixote is built on CML compchem and, in our system, is further transformed to provide RDF used for accessing subcomponents and expressing searches. Chempound repository graphical interface. The entries are indexed on 4 main criteria: I environment program, host, dates, etc. II initialization molecular structure, basis sets, methods, algorithms, parameters, etc.
III calculation the progression of optimization IV finalization molecular structure, properties, times, etc. Alongside, they will also store basic metadata authorship, usage rights, related works, etc. This usage of institutional repositories distributes data management responsibilities among the institutions where the creators of the raw output files work. This provides an efficient basic data management support to the creators, and lets topic-specific repositories such as Quixote's chem to focus on leveraging the specialized CML semantics extracted from the raw files, while still linking back to the original raw files at the institutional repositories.
This schema also favors re-use of the same primary data by different specialized research topic repositories. Yet antother temporary advantage of this approach is that, as the data collection increases, resource discoverability becomes a real challenge - even for the researcher herself. Even if much data can be extracted from the datafiles, some title and description metadata could be very useful to issue searches and can be provided by the person submitting the files to the repository. In the development phase, other researchers - as well as the dataset creator - would be able to discover and access a given unprocessed dataset without needing to wait for it to get processed and transferred into the final Chempound data repository.
Designing a DSpace-based raw data repository will also allow for defining a de facto standardized metadata collection for compchem data description that may be very useful for harmonisation of data description in this specific research area - and might eventually evolve into some kind of standard for the discipline. At the present stage, we have done some preliminary work along metadata collection definition. A set of metadata has been defined and is being discussed in order to provide thorough descriptions of raw compchem datasets potentially extendable to data from other research areas.
Once the metadata set for bibliographical description of raw datasets is agreed, fields contained therein will be mapped to existing or new qualified DublinCore QDC metadata and a draft format will thus be defined. This format will be implemented at a DSpace-based repository, where trial-and-error storing loops with real datasets will be performed for metadata collection completion and fine -tuning - besides accounting for particular cases. Avogadro is an open source, cross-platform desktop application to manipulate and visualize chemical data in 3D.
It is available on all major operating systems, and uses Open Babel for much of its file input and output as well as basic forcefields and cheminformatics techniques. Avogadro was already capable of downloading chemical structures from the NIH structure resolver service, editing structures and optimizing those structures. These dialogs allow the user to change input parameters before producing input files to be run by the code. The output files from several of these codes can also be read directly, this functionality was recently split out into OpenQube - a library to read quantum computational code log files, and calculate molecular orbitals, electron density and other output.
Ultimately, much of this functionality will move into the Quixote parsers, with the OpenQube library concentrating on multithreaded calculation of electronic structure parameters. As JUMBO and other tools can extract electronic structure, spectra and vibrational data, this plugin is being developed to extract them from the CML document.
Experimental support for interacting with a local queue manager is also being actively developed, sending input files to the queue manager, and retrieving log files one the calculation is complete. Some data management features are being added, and as Chempound has a web API a plugin for upload, searching and downloading of structures will be added.
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A MongoDB-based application has been prototyped, using a document store approach to storing chemical data. This approach coupled with Chempound repositories and seamless integration in the GUI will significantly lower barriers for both deposition and retrieval of relevant computational chemistry output. Avogadro forms a central part of the computational chemistry workflow, but is in desparate need of high quality chemical data. The data available from existing online chemical repositories is a good start, but having high quality, discoverable computational chemistry output would significantly improve efficiency in the field.
Widespread access to optimized chemical structures using high level theories and large basis sets would benefit everyone from teaching right through to academic research and industry. The Quixote system is based on the Chempound package, which provides a complete set of components for ingestion of CML, conversion to RDF and customisable display of webpages using Freemarker templates.
The Chempound system contains customisable modules for many types of chemical object and, in this case, is supported by the compchem module. This provides everything necessary for the default installation but, if customisation is required, the configuration and resource files in compchem-common, compchem-handler and compchem-importer can be edited.
The Quixote project can manage input and output from any of the main compchem packages including plane-wave and solid-state approaches. The amount of semantic information in the output files can vary from a relatively small amount of metadata for indexing to a complete representation of every information output in the logfile. The community can decide at which point on the spectrum it wishes to extract information and can also retrospectively enhance this by running improved parsers and converters over the archived logfiles and output files.
The amount of detail depends at the moment on the amount of effort that has been put into the parser. The current project is working hard to ensure inter-operability of dictionary terms and concepts by collating a top-level dictionary resource. When this is complete, the files will be re-parsed to reflect the standard semantics. In the first pass, with the per-code parsers, we have been able to get a high conversion rate and a large number of semantic concepts from the most developed parsers.
The use cases below represent work to date showing that the approach is highly tractable and can be expected to scale across all types of compchem output and types of calculation. This shows the structure of jobs and the typical fields to be found in most calculations. The first use case consisted of files in Gaussian logfile format contributed by Dr. Anna Croft of the University of Bangor. These were deliberately sent without any human description with the challenge that we could use machine methods to determine their scope and motivation.
The average time for conversion was between seconds depending on the size of file. These files have now been indexed, mainly from the information in the archive section of the logfile but also with the initial starting geometry and control information. A large number of the files appear to be a systematic study of the attack by halogen radicals on aromatic nuclei. This use case comprised of over files which Henry Rzepa and collaborators have produced over the years and which have been stored Openly in the Imperial College repository helix. A considerable proportion of the files emanate from student projects, many of which tackle hitherto novel chemical problems.
It is our intention to create a machine-readable catalogues of these files and to determine from first principles their content and, where possible, their intent. All except 18 of these have been converted satisfactorily. One problem encountered was that the parser had used a large number of regexes which, when concatenated, scaled exponentially, so that some of the conversions took over a minute. We are now re-writing the parser to use linear time methods. These files cover a wider range of chemistry than the Croft and Rzepa contributions, as many of them use plane-wave calculations on solid state problems.
These calculations represent an exhaustive study whose results and aims have been discussed elsewhere [ 14 ] , of more than ab initio potential energy surfaces PESs of the model dipeptide HCO-L-Ala-NH 2. The model chemistries investigated are constructed as homo- and heterolevels involving possibly different RHF and MP2 calculations for the geometry and the energy. This totals more than Gaussian logfiles, all generated at the standard level of verbosity, some of them corresponding to single-point energy calculations, some of them to energy optimizations.
The use of JUMBO-converters through Lensfield 2 has allowed to parse the totality of these files, through a complicated folder tree, generating the corresponding raw XML and structured compchem CML with a very high rate of captured concepts. The total time required to do the parsing was about five hours in an iMac desktop machine with a 2. In the spirit of Quixote this is not intended to be a central permanent resource but one of many repositories. It is available for an indefinite time as a demonstration of the power and flexibility of the system but not set up as a permanent "archive".
It may be possible to couple such repositories to more conventional archive-oriented repositories which act as back-end storage and preservation. Each day, countless calculations are run by thousands of computational chemistry researchers around the world, on everything from ageing, dusty desktops to the most powerful supercomputers on the planet.
It might be supposed that this would lead to a deluge of valuable data, but the surprising fact remains that most of this data, if it is archived at all, usually lies hidden away on hard disks or buried on tape backups; often lost to the original researcher and never seen by the wider chemistry community at all. However, it is widely accepted that if the results of all these calculations were publicly accessible it would be extremely valuable as it would:. In the rare cases when data is made openly available, the output of calculations are inevitably produced in a code-specific format; there being no currently accepted output standard.
This means that interpreting or reusing the data requires knowledge of the code, or the use of specific software that understands the output. A standard semantic format will:. GUIs to operate on the input and output of any code supporting the format, vastly increasing their utility and range,. The benefits of a common data standard and results databases are obvious, but several previous efforts have failed to address them, largely because of an inability to settle on a data standard or provide any useful tools that would make it worthwhile for code developers to expend the time to make their codes compatible.
The Quixote project aims to tackle both of these problems in a pragmatic way, building an infrastructure that can be used to both archive and search calculations on a local hard-drive, or expose the data on publicly accessible servers to make it available to the wider community. The vision with which we started the Quixote project some months ago is one in which all data generated in computational QC research projects is used with maximal efficiency, is immediately made available online and aggregated into global search indexes, a vision in which no work is duplicated by researchers and everyone can get an overall picture of what has been calculated for a given system, for a given scientific question, in a matter of minutes, a vision in which all players collaborate to achieve maximum interoperability between the different stages of the scientific process of discovery, in which commonly agreed, semantically rich formats are used, and all publications expose the data as readable and reusable supplementary material, thus enforcing reproducibility of the results; a vision in which good practices are wide spread in the community, and the greatest benefit is earned from the effort invested by everyone working in the field.
With the prototype presented in this article, which has been validated by real use cases, we believe this vision is beginning to be accomplished. The methodological approach in Quixote is novel: The data standard will be consolidated around the tools and encourage its adoption by providing code and tool developers with an obvious reason for adopting the data standard; the "If you build it, they will come" approach. The project is rooted in the belief that scientific codes and data should be "Open", and we are therefore focussing our efforts on using existing Open Source solutions and standards where possible, and then developing any additional tools within the project.
The Quixote project is itself completely Open, de-centralised and community-driven. It is composed of passionate researchers from around the globe that are happy to collaborate with anyone who shares our aims. A template to parse the output from the link output in Gaussian logfiles. The code for beta eigenvalues has been omitted for clarity. Alpha occ. Alpha virt. The trailing part of the line is. Note that the result is.
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The current dictionary for code-independent computational chemistry. A few entries are shown in full; most show the id's and the terms. The full dictionary is maintained within the current Bitbucket content. Concepts in this dictionary are general throughout computational.
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Some of. The dictionary is intended for public comment. Units and unitTypes are often unknown or very difficult. Remember the crystallographers. A quantum chemistry calculation is often comprised of a series. The job concept represents a computational job performed by quantum. The job. An initialisation module represents the concept of the model. A calculation module represents the concept of the model calculation or. A finalisation module represents the concept of the model results for.
The computing environment concept refers to a hardware platform,. The environment also includes the. This information is not related to input and output of the model but is. May be represented as a lower. The log files describes two chained jobs, the first an optimization and the second the calculation of frequencies and thermochemistry.
All significant information is captured, but much is repetitious and much is omitted here for brevity. Some fields have been truncated for clarity - no precision is lost in parsing. The "g". A1 T2 T A1 T2 T2 T Theory and Applications of Computational Chemistry: The first forty years. Molecular Physics. Comput Phys Commun. Comp Mat Sci. Mol Phys. Phys Chem Chem Phys. J Chem Theory Comput. Jensen F: Introduction to Computational Chemistry. Pople JA: Nobel lecture: Quantum chemical models. Rev Mod Phys. J Comput Chem. Chem Eur J. Handbook of numerical analysis. Volume X: Special volume: Computational chemistry.
Feller D: The role of databases in support of Computational Chemistry. Collect Czech Chem Commun. J Mol Graph Model. Phys Rev A. Basic principles. J Chem Inf Comput Sci. J Chem Inf Model. J Digit Inform.
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Download references. We also thank the ZCAM, and especially its Director, Michel Mareschal, for hosting and co-organizing the vibrant workshop in which the Quixote project was born. Finally, thanks to Charlotte Bolton for the careful editing of the manuscript.