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PRODID:-//Memento EPFL//
BEGIN:VEVENT
SUMMARY:CECAM Workshop: "Open Databases Integration for Materials Design"
DTSTART:20230605T130000
DTEND:20230609T124500
DTSTAMP:20260407T021048Z
UID:8c93f02ecffcd378a4dfb6b24dd21b678d76c8de30d92ac5c78c217e
CATEGORIES:Conferences - Seminars
DESCRIPTION:You can apply to participate and find all the relevant informa
 tion (speakers\, abstracts\, program\,...) on the event website: https://
 www.cecam.org/workshop-details/1208\n\nDescription\nTrial-and-error is the
  traditional approach to design bespoke materials for specific application
 s. Typically\, researchers use their intuition and experience to propose c
 ompounds that are then synthesized and tested to ascertain whether or not 
 they fulfill the target properties. It usually takes months to test a sing
 le compound\, and most often the outcome is negative so many cycles of tri
 al and improvement are required to finalize the material. The typical rese
 arch project is therefore complex\, time-consuming\, and costly.  In a sp
 eech on June 24\, 2011\, President Obama announced the Materials Genome In
 itiative\, as a major US research priority to foster innovation more effec
 tively.\nThe last decade has witnessed game changing improvements in mater
 ials design. The exponential growth of computer power and the development 
 of robust first-principles electronic structure codes make it possible to 
 perform large sets of calculations automatically. This has provided a laun
 chpad for the flourishing field of high-throughput (HT) ab initio comput
 ations [1\,2]. The concept is simple yet powerful. The results of HT calcu
 lations are curated in large databases (DBs)\, recording the computed prop
 erties of existing and hypothetical materials. These DBs can then be scree
 ned for materials with the desired properties\, removing the guesswork fro
 m materials design. Furthermore\, these DBs can be used to build machine l
 earning models that can design materials. Thanks to such HT calculations\,
  many open-domain DBs have emerged. In parallel\, the experimental communi
 ty has also continued to develop its own DBs with material properties [3\,
 4\,5\,6\,7\,8]. In the latter case\, some of the DBs are open (e.g.\, at t
 he National Institute of Standards and Technology) whilst others are marke
 ted by companies. Given the number of available materials DBs\, it is impo
 ssible to be exhaustive but some initiatives provide links to as many as p
 ossible (see e.g.\, Refs. [9] and [10]).\nNonetheless\, the landscape in m
 aterials DBs is fragmented. In some cases\, a Representational State Trans
 fer (REST) Application Program Interface (API) is available [11\,12\,13] t
 o interrogate the DB through scripts (though not always documented). But\,
  until recently\, it was only possible to interrogate one DB at a time and
  the APIs would vary from one DB to another. Furthermore\, the lack of sta
 ndards makes it complicated to access large-scale curated materials data. 
 Flexible\, uniform\, computer-readable data standards should be establishe
 d to enable data to be shared and systematically mined.\nTo take advantage
  of the disparate databases\, the OPTIMADE consortium [14] was formed with
  the goal to create a holistic API that can access all materials databases
 . The consortium has gathered the developers/maintainers of the following 
 DBs:\n\n	AFLOW distributed materials property repository: http://aflow.or
 g\n	ChemDataExtractor in Cambridge: http://chemdataextractor.org\n	Materi
 als Cloud: http://materialscloud.org\n	Materials Project: http://materia
 lsproject.org\n	NOMAD (Novel Materials Discovery) Archive: https://nomad-
 lab.eu/prod/rae/gui/search\n	Open Quantum Materials Database: http://oqmd
 .org\n	Computational Materials Repository: http://cmr.fysik.dtu.dk\n	Open
  Materials Database: http://openmaterialsdb.se\n	Crystallography Open Dat
 abase: http://www.crystallography.net/cod\n\nUnder the OPTIMADE banner\, 
 six workshops have created a community developing the OPTIMADE API\, and a
 ims to further expand the community in the future. Thanks to the discussio
 ns that have taken place during the workshops and through the mailing list
 \, the first stable version (v1.0) of the OPTIMADE API specifications has 
 been released [15\,16]. All of these databases have now implemented the OP
 TIMADE API\, providing scientists with immediate access to a wealth of mat
 erials data.\nA paper about the API was recently published in the prestigi
 ous peer reviewed journal Nature Scientific Data [17]\, which should act a
 s a launchpad for future adoption and usage of the API. Furthermore\, a we
 bsite has been set up [14] with a mailing list\, a wiki\, and a GitHub rep
 ository. Optimade-python-tools [18] have been developed and released as th
 e reference implementation for python servers\, lowering the barrier to ne
 w OPTIMADE adopters. A few tutorials have also been organized online [19\,
 20].\nThis CECAM event will consist of both a tutorial and a workshop.
  The tutorial will be mainly online\, but participants who would like to 
 participate onsite are more than welcome. They can even stay for the rest 
 of the workshop\, and any participants implementing OPTIMADE for their own
  databases will be strongly encouraged to stay on to accelerate the adopti
 on of OPTIMADE. For the workshop\, onsite participation is strongly sugge
 sted though online attendance will be possible but without any guarantee 
 on the experience. \nFor the tutorial\, the participants will be asked t
 o go through a series of videos before the meeting itself. After a Q&A ses
 sion on the videos\, the first hands-on session will comprise exercises 
 about generating OPTIMADE queries (that can be used on all databases)\, an
 d about different tools for querying the databases. During the second hand
 s-on session\, the tutees choose between three options. They can continue 
 the exercises about querying the databases. Alternatively\, they can study
  how to implement an OPTIMADE client for an existing database. Finally\, i
 f they have a specific problem of their own that can be dealt with using 
 OPTIMADE\, the developers can help them to address it. The tutees will con
 tinue to work during the second day (with the tutors available at specifi
 c moments). The tutorial will end on the morning of the third day after sh
 ort elevator pitches by the tutees during which they will present their ac
 hievements. \nThe workshop will be dedicated to the further development
  of the OPTIMADE API.\n\nReference\n[1] S. Curtarolo\, G. Hart\, M. Nardel
 li\, N. Mingo\, S. Sanvito\, O. Levy\, Nature. Mater.\, 12\, 191-201 (201
 3)\n[2] N. Marzari\, Nature. Mater.\, 15\, 381-382 (2016)\n[3] L. Green\,
  American Journal of Physics\, 35\, 291-292 (1967)\n[4] A. Belsky\, M. He
 llenbrandt\, V. Karen\, P. Luksch\, Acta. Crystallogr. Sect. B.\, 58\, 36
 4-369 (2002)\n[5] P. Villars\, M. Berndt\, K. Brandenburg\, K. Cenzual\, J
 . Daams\, F. Hulliger\, T. Massalski\, H. Okamoto\, K. Osaki\, A. Prince\,
  H. Putz\, S. Iwata\, Journal of Alloys and Compounds\, 367\, 293-297 (20
 04)\n[6] S. Gražulis\, D. Chateigner\, R. Downs\, A. Yokochi\, M. Quirós
 \, L. Lutterotti\, E. Manakova\, J. Butkus\, P. Moeck\, A. Le Bail\, J. Ap
 pl. Cryst.\, 42\, 726-729 (2009)\n[7] Y. Xu\, M. Yamazaki\, P. Villars\, 
 Jpn. J. Appl. Phys.\, 50\, 11RH02 (2011)\n[8] A. Zakutayev\, N. Wunder\, 
 M. Schwarting\, J. Perkins\, R. White\, K. Munch\, W. Tumas\, C. Phillips\
 , Sci. Data.\, 5\, 180053 (2018)\n[9] https://doi.org/10.5281/zenodo.7693
 349\n[10] https://github.com/blaiszik/Materials-Databases\n[11] R. Taylor\
 , F. Rose\, C. Toher\, O. Levy\, K. Yang\, M. Buongiorno Nardelli\, S. Cur
 tarolo\, Computational Materials Science\, 93\, 178-192 (2014)\n[12] S. O
 ng\, S. Cholia\, A. Jain\, M. Brafman\, D. Gunter\, G. Ceder\, K. Persson\
 , Computational Materials Science\, 97\, 209-215 (2015)\n[13] D. Papageor
 giou\, I. Kinloch\, R. Young\, Progress in Materials Science\, 90\, 75-12
 7 (2017)\n[14] http://www.optimade.org\n[15] http://materials-consortia.gi
 thub.io/optimade\n[16] 10.5281/zenodo.4195051\n[17] C. Andersen\, R. Armie
 nto\, E. Blokhin\, G. Conduit\, S. Dwaraknath\, M. Evans\, Á. Fekete\, A.
  Gopakumar\, S. Gražulis\, A. Merkys\, F. Mohamed\, C. Oses\, G. Pizzi\, 
 G. Rignanese\, M. Scheidgen\, L. Talirz\, C. Toher\, D. Winston\, R. Avers
 a\, K. Choudhary\, P. Colinet\, S. Curtarolo\, D. Di Stefano\, C. Draxl\, 
 S. Er\, M. Esters\, M. Fornari\, M. Giantomassi\, M. Govoni\, G. Hautier\,
  V. Hegde\, M. Horton\, P. Huck\, G. Huhs\, J. Hummelshøj\, A. Kariryaa\,
  B. Kozinsky\, S. Kumbhar\, M. Liu\, N. Marzari\, A. Morris\, A. Mostofi\,
  K. Persson\, G. Petretto\, T. Purcell\, F. Ricci\, F. Rose\, M. Scheffler
 \, D. Speckhard\, M. Uhrin\, A. Vaitkus\, P. Villars\, D. Waroquiers\, C. 
 Wolverton\, M. Wu\, X. Yang\, Sci. Data.\, 8\, 217 (2021)\n[18] M. Evans\
 , C. Andersen\, S. Dwaraknath\, M. Scheidgen\, Á. Fekete\, D. Winston\, J
 OSS.\, 6\, 3458 (2021)\n[19] https://th.fhi-berlin.mpg.de/meetings/nomad-
 tutorials/index.php?n=Meeting.Tutorial6\n[20] https://www.youtube.com/watc
 h?v=1OflR9qBP_A
LOCATION:BCH 2103 https://plan.epfl.ch/?room==BCH%202103
STATUS:CONFIRMED
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