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SUMMARY:FAIR Data Management of Theoretical Spectroscopy and Green’s Fun
 ction Methods
DTSTART;VALUE=DATE:20260420
DTSTAMP:20260413T224218Z
UID:fa6a205d85f4c1b88d30f50c4258cd091c7ff3bae62d1bb1fafd62e9
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/fair-data-management-of-theoretical-spectro
 scopy-and-greens-function-methods-1377.\n\nRegistration is required to att
 end the full event\, take part in the social activities and present a post
 er at the poster session (if any).  However\, the EPFL community is wel
 come to attend specific lectures without registration if the topic is o
 f interest to their research. Do not hesitate to contact the CECAM Event 
 Manager if you have any question.\n\nDescription\n\nBig-data-driven metho
 dologies have emerged as a fundamental paradigm of science\, but require a
 n enormous amount of resources to achieve their promised impact. The FAIR 
 (Findable\, Accessible\, Interoperable\, and Reusable) data principles [1]
  ensure that scientific data can be shared and reutilized\, providing an e
 fficient route for accumulating data and taking advantage of these powerfu
 l techniques. FAIR data management allows essential knowledge to be system
 atically extracted from data\, accelerating discoveries and innovations ac
 ross various domains [2]. Furthermore\, open science is essential for the 
 verifiability and reproducibility of results and has been a topic of major
  discussion over the last decade. In materials science\, data-driven metho
 dologies\, coupled with the appropriate FAIR data management practices\, a
 re invaluable for the discovery of new materials due to the vast combinato
 rial space of chemical systems that emerge from the periodic table [3\, 4]
 . Such methodologies have been successfully applied\, e.g.\, to design and
  predict new materials with desired properties using ab-initio ground stat
 e simulations\, i.e.\, data generated from Density Functional Theory (DFT)
  calculations [5]. However\, there remains a critical gap in replicating t
 his success in the context of other simulation frameworks. \nTheoretical 
 spectroscopy and Green's function method simulations [6\, 7]\, including d
 ata simulated using the GW approximation\, Time-Dependent Density Function
 al Theory (TDDFT)\, the Bethe-Salpeter equation (BSE)\, Dynamical Mean-Fie
 ld Theory (DMFT)\, and Korringa-Kohn-Rostoker (KKR)\, pose especially diff
 icult challenges in the context of FAIR data management. These simulations
  not only involve extensive computational resources and produce large data
 sets with associated complex workflows but are also executed using a large
  variety of public and in-house simulation software. At the same time\, th
 ese methodologies are essential for understanding excited state properties
  of complex materials\; they are more accurate than DFT calculations and p
 rovide better comparisons with experimental results since they incorporate
  excited states and electronic correlation effects in a more consistent ma
 nner [8]. \nThere has recently been a number of individual efforts to imp
 rove the accessibility of  data produced by theoretical spectroscopy and 
 Green’s function methods through the usage of publicly accessible databa
 ses. For example\, the Computational Materials Repository (CMR) [9] contai
 ns several individual databases\, amongst which the Computational 2D Mater
 ials Database (C2DB) [10] contains GW and BSE data for a specific set of p
 arameters and properties. The MaterialsCloud [11] database has some indivi
 dual datasets published for these methodologies\, however there is not a c
 lear data structure for them. The NIST-JARVIS [12] database has a specific
  app for BeyondDFT simulations with DMFT data\, but only for a specific si
 mulation code. By making datasets findable\, these efforts aim to avoid re
 dundant computations and thus build upon existing work more efficiently. W
 hile these efforts represent an important step in the right direction\, th
 ey fall short of fully achieving their goal due to a continued lack of con
 sistency (i.e.\, interoperability) between individual databases. Moreover
 \, these self-managed databases typically lack the ability to store the co
 mplete provenance of the simulated workflow\, which is essential to ensure
  reproducibility. \nRecently\, FAIRmat [13]\, a consortium of the German 
 research data infrastructure (NFDI) association\, was formed to construct 
 a scalable data infrastructure for Materials Science that can be easily cu
 stomized for individual communities. This infrastructure consists of a pri
 mary software and repository called NOMAD [14]—a free web-service that e
 nables the organization\, analysis\, sharing\, and publishing of materials
  science data. One of the tasks within FAIRmat’s scope is to build suppo
 rt for theoretical spectroscopy and Green’s function simulations within 
 NOMAD. Support for several of these methodologies have now been successful
 ly built\, and there already exists over 10 000 entries in the NOMAD repos
 itory containing GW [15]\, BSE [16]\, and DMFT [17] data\, along with the 
 full provenance of the corresponding complex workflows. The next step to d
 eveloping a FAIR data infrastructure for these methods is to tackle the in
 teroperability problem.\nInteroperability within this domain is extremely 
 challenging due to the heterogeneous character of theoretical spectroscopy
  and Green’s function simulations. Consequently\, the adoption of common
  structures (e.g.\, describing the Green’s function\, the self-energy\, 
 or the dielectric function) is the key for improving interoperability. Thu
 s\, various members of the community\, including method developers\, mater
 ials and data scientists\, and data management experts\, must come togethe
 r to reach a consensus on specific common data structures.\n\nReferences\n
 \n[1] Computational Materials Repository (CMR) website\n[2] S. Di Cataldo\
 , P. Worm\, J. Tomczak\, L. Si\, K. Held\, Nat. Commun.\, 15\, 3952 (2024
 )\n[3] F. Meng\, B. Maurer\, F. Peschel\, S. Selcuk\, M. Hybertsen\, X. Qu
 \, C. Vorwerk\, C. Draxl\, J. Vinson\, D. Lu\, Phys. Rev. Materials\, 8\,
  013801 (2024)\n[4] M. van Setten\, F. Caruso\, S. Sharifzadeh\, X. Ren\, 
 M. Scheffler\, F. Liu\, J. Lischner\, L. Lin\, J. Deslippe\, S. Louie\, C.
  Yang\, F. Weigend\, J. Neaton\, F. Evers\, P. Rinke\, J. Chem. Theory Com
 put.\, 11\, 5665-5687 (2015)\n[5] M. Scheidgen\, L. Himanen\, A. Ladines\
 , D. Sikter\, M. Nakhaee\, Á. Fekete\, T. Chang\, A. Golparvar\, J. Márq
 uez\, S. Brockhauser\, S. Brückner\, L. Ghiringhelli\, F. Dietrich\, D. L
 ehmberg\, T. Denell\, A. Albino\, H. Näsström\, S. Shabih\, F. Dobener\,
  M. Kühbach\, R. Mozumder\, J. Rudzinski\, N. Daelman\, J. Pizarro\, M. K
 uban\, C. Salazar\, P. Ondračka\, H. Bungartz\, C. Draxl\, JOSS.\, 8\, 5
 388 (2023)\n[6] FAIRmat website\n[7] K. Choudhary\, K. Garrity\, A. Reid\,
  B. DeCost\, A. Biacchi\, A. Hight Walker\, Z. Trautt\, J. Hattrick-Simper
 s\, A. Kusne\, A. Centrone\, A. Davydov\, J. Jiang\, R. Pachter\, G. Cheon
 \, E. Reed\, A. Agrawal\, X. Qian\, V. Sharma\, H. Zhuang\, S. Kalinin\, B
 . Sumpter\, G. Pilania\, P. Acar\, S. Mandal\, K. Haule\, D. Vanderbilt\, 
 K. Rabe\, F. Tavazza\, npj. Comput. Mater.\, 6\, 173 (2020)\n[8] L. Talir
 z\, S. Kumbhar\, E. Passaro\, A. Yakutovich\, V. Granata\, F. Gargiulo\, M
 . Borelli\, M. Uhrin\, S. Huber\, S. Zoupanos\, C. Adorf\, C. Andersen\, O
 . Schütt\, C. Pignedoli\, D. Passerone\, J. VandeVondele\, T. Schulthess\
 , B. Smit\, G. Pizzi\, N. Marzari\, Sci. Data.\, 7\, 299 (2020)\n[9] S. H
 aastrup\, M. Strange\, M. Pandey\, T. Deilmann\, P. Schmidt\, N. Hinsche\,
  M. Gjerding\, D. Torelli\, P. Larsen\, A. Riis-Jensen\, J. Gath\, K. Jaco
 bsen\, J. Jørgen Mortensen\, T. Olsen\, K. Thygesen\, 2D Mater.\, 5\, 04
 2002 (2018)\n[10] M. Wilkinson\, M. Dumontier\, I. Aalbersberg\, G. Applet
 on\, M. Axton\, A. Baak\, N. Blomberg\, J. Boiten\, L. da Silva Santos\, P
 . Bourne\, J. Bouwman\, A. Brookes\, T. Clark\, M. Crosas\, I. Dillo\, O. 
 Dumon\, S. Edmunds\, C. Evelo\, R. Finkers\, A. Gonzalez-Beltran\, A. Gray
 \, P. Groth\, C. Goble\, J. Grethe\, J. Heringa\, P. ’t Hoen\, R. Hooft\
 , T. Kuhn\, R. Kok\, J. Kok\, S. Lusher\, M. Martone\, A. Mons\, A. Packer
 \, B. Persson\, P. Rocca-Serra\, M. Roos\, R. van Schaik\, S. Sansone\, E.
  Schultes\, T. Sengstag\, T. Slater\, G. Strawn\, M. Swertz\, M. Thompson\
 , J. van der Lei\, E. van Mulligen\, J. Velterop\, A. Waagmeester\, P. Wit
 tenburg\, K. Wolstencroft\, J. Zhao\, B. Mons\, Sci. Data.\, 3\, 160018 (
 2016)\n[11] L. Reining et al.\, Comptes Rendus Physique 10\, 6 (2009)\n[12
 ] V. Blum\, R. Asahi\, J. Autschbach\, C. Bannwarth\, G. Bihlmayer\, S. Bl
 ügel\, L. Burns\, T. Crawford\, W. Dawson\, W. de Jong\, C. Draxl\, C. Fi
 lippi\, L. Genovese\, P. Giannozzi\, N. Govind\, S. Hammes-Schiffer\, J. H
 ammond\, B. Hourahine\, A. Jain\, Y. Kanai\, P. Kent\, A. Larsen\, S. Leht
 ola\, X. Li\, R. Lindh\, S. Maeda\, N. Makri\, J. Moussa\, T. Nakajima\, J
 . Nash\, M. Oliveira\, P. Patel\, G. Pizzi\, G. Pourtois\, B. Pritchard\, 
 E. Rabani\, M. Reiher\, L. Reining\, X. Ren\, M. Rossi\, H. Schlegel\, N. 
 Seriani\, L. Slipchenko\, A. Thom\, E. Valeev\, B. Van Troeye\, L. Vissche
 r\, V. Vlcek\, H. Werner\, D. Williams-Young\, T. Windus\, Electron. Struc
 t.\, (2024)\n[13] L. Ghiringhelli\, C. Baldauf\, T. Bereau\, S. Brockhause
 r\, C. Carbogno\, J. Chamanara\, S. Cozzini\, S. Curtarolo\, C. Draxl\, S.
  Dwaraknath\, Á. Fekete\, J. Kermode\, C. Koch\, M. Kühbach\, A. Ladines
 \, P. Lambrix\, M. Himmer\, S. Levchenko\, M. Oliveira\, A. Michalchuk\, R
 . Miller\, B. Onat\, P. Pavone\, G. Pizzi\, B. Regler\, G. Rignanese\, J. 
 Schaarschmidt\, M. Scheidgen\, A. Schneidewind\, T. Sheveleva\, C. Su\, D.
  Usvyat\, O. Valsson\, C. Wöll\, M. Scheffler\, Sci. Data.\, 10\, 626 (2
 023)\n[14] J. Schmidt\, M. Marques\, S. Botti\, M. Marques\, npj. Comput. 
 Mater.\, 5\, 83 (2019)\n[15] L. Himanen\, A. Geurts\, A. Foster\, P. Rink
 e\, Advanced Science\, 6\, (2019)\n[16] M. Scheffler\, M. Aeschlimann\, M
 . Albrecht\, T. Bereau\, H. Bungartz\, C. Felser\, M. Greiner\, A. Groß\,
  C. Koch\, K. Kremer\, W. Nagel\, M. Scheidgen\, C. Wöll\, C. Draxl\, Nat
 ure\, 604\, 635-642 (2022)\n[17] C. Draxl\, M. Scheffler\, MRS Bull.\, 4
 3\, 676-682 (2018)
LOCATION:BCH 2103 https://plan.epfl.ch/?room==BCH%202103
STATUS:CONFIRMED
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