Materials Software and Data Group

The Materials Software and Data group focuses on developing advanced simulation software and algorithms to understand and characterise novel materials, with particular focus on spectroscopies measured at PSI (e.g. core-level spectroscopies, muon spectroscopies). 


Simulation services are provided to researchers at PSI and worldwide, by making simulation capabilities easily accessible via an open-science platform, based on the AiiDA workflow engine, the AiiDAlab simulation platform, and the Materials Cloud web portal. The platform combines automated high-throughput simulations, accessible web interfaces, and open curated datasets. 


The long-term goal of the group is to accelerate materials discovery and characterization by enabling autonomous laboratories, that combine automated simulations with robotic experiments, driven by autonomous algorithms.

The research of the MSD group is supported by several projects. They are listed in the Project page of the LMS laboratory.

In addition, the group develops and maintains several open-source software codes, as well as the web-based open science portal Materials Cloud, listed in the Software page of the LMS laboratory.

  • Bonfà P, Onuorah IJ, Lang F, Timrov I, Monacelli L, Wang C, et al.
    Magnetostriction-driven muon localization in an antiferromagnetic oxide
    Physical Review Letters. 2024; 132(4): 046701 (7 pp.). https://doi.org/10.1103/PhysRevLett.132.046701
    DORA PSI
  • Bosoni E, Beal L, Bercx M, Blaha P, Blügel S, Bröder J, et al.
    How to verify the precision of density-functional-theory implementations via reproducible and universal workflows
    Nature Reviews Physics. 2024; 6: 45-58. https://doi.org/10.1038/s42254-023-00655-3
    DORA PSI
  • Evans ML, Bergsma J, Merkys A, Andersen CW, Andersson OB, Beltrán D, et al.
    Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange
    Digital Discovery. 2024. https://doi.org/10.1039/d4dd00039k
    DORA PSI
  • Kraus P, Bainglass E, Ramirez FF, Svaluto-Ferro E, Ercole L, Kunz B, et al.
    A bridge between trust and control: computational workflows meet automated battery cycling
    Journal of Materials Chemistry A. 2024; 12(18): 10773-10783. https://doi.org/10.1039/d3ta06889g
    DORA PSI
  • Bonacci M, Qiao J, Spallanzani N, Marrazzo A, Pizzi G, Molinari E, et al.
    Towards high-throughput many-body perturbation theory: efficient algorithms and automated workflows
    npj Computational Materials. 2023; 9(1): 74 (10 pp.). https://doi.org/10.1038/s41524-023-01027-2
    DORA PSI
  • Campi D, Mounet N, Gibertini M, Pizzi G, Marzari N
    Expansion of the Materials Cloud 2D database
    ACS Nano. 2023; 17(12): 11268-11278. https://doi.org/10.1021/acsnano.2c11510
    DORA PSI
  • Du D, Baird TJ, Bonella S, Pizzi G
    OSSCAR, an open platform for collaborative development of computational tools for education in science
    Computer Physics Communications. 2023; 282: 108546 (12 pp.). https://doi.org/10.1016/j.cpc.2022.108546
    DORA PSI
  • Ghiringhelli LM, Baldauf C, Bereau T, Brockhauser S, Carbogno C, Chamanara J, et al.
    Shared metadata for data-centric materials science
    Scientific Data. 2023; 10: 626 (18 pp.). https://doi.org/10.1038/s41597-023-02501-8
    DORA PSI
  • Medrano G, Bainglass E, Andreussi O
    Uncoupling system and environment simulation cells for fast-scaling modeling of complex continuum embeddings
    Journal of Chemical Physics. 2023; 159(5): 054103 (12 pp.). https://doi.org/10.1063/5.0150298
    DORA PSI
  • Qiao J, Pizzi G, Marzari N
    Automated mixing of maximally localized Wannier functions into target manifolds
    npj Computational Materials. 2023; 9(1): 206 (9 pp.). https://doi.org/10.1038/s41524-023-01147-9
    DORA PSI
  • Qiao J, Pizzi G, Marzari N
    Projectability disentanglement for accurate and automated electronic-structure Hamiltonians
    npj Computational Materials. 2023; 9(1): 208 (14 pp.). https://doi.org/10.1038/s41524-023-01146-w
    DORA PSI
  • Vogler M, Busk J, Hajiyani H, Jørgensen PB, Safaei N, Castelli IE, et al.
    Brokering between tenants for an international materials acceleration platform
    Matter. 2023; 6(9): 2647-2665. https://doi.org/10.1016/j.matt.2023.07.016
    DORA PSI
  • Tohidi Vahdat M, Agrawal KV, Pizzi G
    Machine-learning accelerated identification of exfoliable two-dimensional materials
    Machine Learning: Science and Technology. 2022; 3(4): 045014 (9 pp.). https://doi.org/10.1088/2632-2153/ac9bca
    DORA PSI