A1 Refereed original research article in a scientific journal
Software interoperability in electronic structure methods for atomistic materials simulations; 
Authors: Carrete, Jesús; García-Fernández, Pablo; Grüning, Myrta; He, Xu; Pionteck, Mike N.; Protik, Nakib H.; Todorović, Milica; Togo, Atsushi; Wang, Xing; Poloni, Roberta; Pruneda, Miguel; Cammarata, Antonio
Publisher: Elsevier BV
Publication year: 2026
Journal: Computational Materials Science
Article number: 114900
Volume: 273
ISSN: 0927-0256
eISSN: 1879-0801
DOI: https://doi.org/10.1016/j.commatsci.2026.114900
Publication's open availability at the time of reporting: Open Access
Publication channel's open availability : Partially Open Access publication channel
Web address : https://doi.org/10.1016/j.commatsci.2026.114900
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/526964636
Self-archived copy's licence: CC BY NC
Self-archived copy's version: Publisher`s PDF
Atomistic materials simulations increasingly rely on complex, multi-stage workflows that combine electronic structure first principles methods, effective models, machine-learning techniques, and high-performance computing, making software interoperability a central requirement rather than a technical convenience. This perspective highlights how modular, interoperable software ecosystems overcome the limitations of monolithic electronic structure codes, enabling automation, reproducibility, and scalability across diverse applications such as lattice dynamics, electronic, magnetic, optical, and transport properties, as well as data-driven materials optimization. We discuss how standardized workflows, robust data provenance, and interoperable infrastructures are essential for high-throughput studies, AI-ready databases, and the transition toward autonomous materials discovery. This is the result of the debates of the Symposium F “Advanced interoperability in atomistic simulations of materials”, which took place within the 2025 European Materials Research Society fall meeting.
Keywords:
ab initio, automation, Database, high-throughput, machine-learning, material discovery
Downloadable publication This is an electronic reprint of the original article. |  |
Funding information in the publication:
A.C. acknowledges support of the Czech Science Foundation (project No. 24-12643L), cofunding by the European Union under the project “Robotics and advanced industrial production” (reg. no. CZ.02.01.01/00/22_008/0004590), and the Ministry of Education, Youth and Sports of the Czech Republic through the e-INFRA CZ (ID:90254).
M.P. acknowledges support from Grant PID2022-139776NB-C61 funded by the Spanish MCIN/AEI/10.13039/501100011033 and by the ERDF, A way of making Europe, Grant 2021SGR01519 from Generalitat de Catalunya, and by EU MaX CoE (Grant 101093374).
A.T. acknowledges support by JSPS KAKENHI Grant Numbers JP24K08021, JP24H00190, JP25H01246, and JP25H01252; some of the calculations in this study were performed on the Numerical Materials Simulator at NIMS.
We gratefully thank Giovanni Pizzi for insightful discussions on workflow interoperability and provenance semantics.
X.W. acknowledge financial support by the NCCR MARVEL, a National Centre of Competence in Research, funded by the Swiss National Science Foundation (grant number 205602).
N.H.P. acknowledges funding from the “Deutsche Forschungsgemeinschaft” (DFG, German Research Foundation) for an “Emmy Noether” research grant (Grant No. 534386252).
M.G. acknowledges funding from the UKRI Horizon Europe Guarantee funding scheme (EP/Y032659/1).
