BEGIN:VCALENDAR VERSION:2.0 PRODID:-//Memento EPFL// BEGIN:VEVENT SUMMARY:CECAM workshop: "Open Databases Integration for Materials Design" DTSTART;VALUE=DATE:20240610 DTSTAMP:20260416T122833Z UID:c18022c5ef61b91e1345ae0b54c541bab4a778dfbb7ce549981c2991 CATEGORIES:Conferences - Seminars DESCRIPTION:You can find all the relevant information (speakers\, abstract s\, program\,...) on the event website: https://www.cecam.org/workshop-de tails/open-databases-integration-for-materials-design-1280\n\nRegistration s for this workshop are mandatory\, please apply here:  https://members. cecam.org/workshops/participate/1280\nA CECAM account is required to apply . If you do not already have one\, please create one here: https://member s.cecam.org/register\n\n*** REGISTRATION DEADLINE *** : 10th May 2024\n \nDescription\nThe conventional method for designing custom materials for specific applications is based on trial-and-error. Researchers typically r ely on intuition and expertise to suggest compounds that are subsequently synthesized and examined to determine if they possess the target propertie s. Testing a single compound typically takes several months\, and more oft en than not\, the results are unfavorable\, necessitating multiple rounds of experimentation to iteratively arrive at the desired material. Conseque ntly\, research and development are intricate\, time-intensive\, and expen sive. Recognizing these challenges\, President Obama declared the Material s Genome Initiative on June 24\, 2011\, with the aim of prioritizing innov ative research in the United States.\nOver the past decade there have been groundbreaking advances in materials design. The significant increase in computer power and the creation of robust first-principles electronic stru cture codes have enabled the automation of extensive calculations. This br eakthrough paved the way for the thriving field of high-throughput ab ini tio computations [1]. The strategy is simple yet powerful. The outcomes o f high-throughput calculations are carefully curated in extensive database s (DBs) that document the computed properties of both existing and hypothe tical materials. These DBs can then be screened to identify materials or c hemicals  that possess specific desired properties\, eliminating the need for guesswork in materials and chemicals design. Moreover\, these DBs can be leveraged to construct predictive machine learning models that can be harnessed for design. This potential motivated the development of numerous open-domain DBs. Simultaneously\, the experimental community has actively developed its own DBs containing material properties [2]. Some of these D Bs are openly accessible\, such as those provided by the National Institut e of Standards and Technology\, while others are commercially marketed by companies. Given the multitude of available materials DBs\, it is impracti cal to provide an exhaustive list\, but certain initiatives offer links to as many DBs as possible (refer to Refs. [3] and [4]\, for example).\nHowe ver\, the materials database landscape remains fragmented. In some instanc es\, a Representational State Transfer (REST) Application Program Interfac e (API) is available [5] to interact with the database using scripts\, alt hough the documentation may not always be comprehensive. Until recently\, it was only possible to query one database at a time\, and furthermore the APIs would differ across different databases. Additionally\, the absence of a standardized protocol made it challenging to access curated materials data on a large scale. The establishment of flexible\, uniform\, and mach ine-readable data standards is crucial to facilitate data sharing and syst ematic data mining.\nTo address the issue of scattered databases\, the OPT IMADE consortium [6] was established to create a comprehensive API capable of accessing all materials databases. The consortium brought together the developers and maintainers of the leading databases:\n\n AFLOW distribute d materials property repository: http://aflow.org\n ChemDataExtractor in Cambridge: http://chemdataextractor.org\n Materials Cloud: http://materi alscloud.org\n Materials Project: http://materialsproject.org\n NOMAD (No vel Materials Discovery) Archive: https://nomad-lab.eu/prod/rae/gui/searc h\n Open Quantum Materials Database: http://oqmd.org\n Computational Mate rials Repository: http://cmr.fysik.dtu.dk\n Open Materials Database: htt p://openmaterialsdb.se\n Crystallography Open Database: http://www.crysta llography.net/cod\n\nUnder the OPTIMADE initiative\, a community has forme d through seven workshops\, developed the OPTIMADE API\, and plans to expa nd the community and scope of the API in the future. Through discussions h eld during these workshops\, during monthly video calls\, and via the mail ing list\, the first  (v1.0) and second (v1.1) stable version of the OPTI MADE API specifications were released [7\,8]. All the aforementioned datab ases have successfully implemented the OPTIMADE API\, granting scientists immediate access to an extensive range of materials data.\nA research pape r detailing the API has been published in the esteemed peer-reviewed journ al\, Scientific Data [9]\, which has catalyzed the further adoption and ut ilization of the API. In addition\, a website [6] has been established\, f eaturing a mailing list\, a wiki\, and a GitHub repository. The optimade-p ython-tools [10] have been developed and made available as the official re ference implementation for Python servers\, making it straightforward for new adopters of OPTIMADE to join the initiative. Several online tutorials [11\,12] have been organized\, and one hybrid tutorial took place at CECAM in 2023 [13]. Thanks to the postdoc working at CECAM (Johan Bergsma)\, th e specification has been extended to trajectories. This will appear in the third (v1.2) stable version of the OPTIMADE API specifications\, which sh ould be released in the coming months.\n\nReferences\n[1] S. Curtarolo et al.\, Nat. Mater. 12\, 191 (2013)\; N. Marzari\, Nat. Mater. 15\, 381 (201 6)\n[2] K.H. Hellwege and L.C. Green\, Am. Journ. Phys 35\, 291 (1967)\; A . Belsky et al.\, Acta Cryst. B 58\, 364 (2002)\; P. Villars et al.\, J. A lloys Compd. 367\, 293 (2004)\; S. Gražulis et al.\, J. Appl. Cryst. 42\, 726 (2009)\; Y. Xu\, M. Yamazaki\, and P. Villars\, Jpn. J. Appl. Phys. 5 0\, 11RH02 (2011)\; A. Zakutayev\, Sci Data 5\, 180053 (2018).\n[3] https ://github.com/tilde-lab/awesome-materials-informatics\n[4] https://github .com/blaiszik/Materials-Databases \n[5] see e.g. R.H. Taylor et al.\, Com put. Mater. Sci. 93\, 178 (2014)\; S.P. Ong et al.\, Comput. Mater. Sci. 9 7\, 209 (2015)\; F. Rose et al.\, Comput. Mater. Sci. 137\, 362 (2017) \n [6] http://www.optimade.org\n[7] http://www.optimade.org/optimade \n[8] C. W. Andersen et al.\, The OPTIMADE Specification (Version 1.0). Zenodo (2020). http://doi.org/10.5281/zenodo.4195051\n[9] C. W. Andersen et al. \, Sci. Data 8\, 217 (2021). https://doi.org/10.1038/s41597-021-00974-z\n [10] M. Evans et al.\, J. Open Source Softw. 6\, 3458 (2021).\n[11] https ://th.fhi-berlin.mpg.de/meetings/nomad-tutorials/index.php?n=Meeting.Tutor ial6\n[12] https://www.youtube.com/watch?v=1OflR9qBP_A  \n[13] https:/ /www.cecam.org/workshop-details/1208 LOCATION:BCH 2103 https://plan.epfl.ch/?room==BCH%202103 STATUS:CONFIRMED END:VEVENT END:VCALENDAR