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.

  • 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
  • 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
  • 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

Contact

Head of Group
Dr. Giovanni Pizzi
Phone: +41 56 310 58 24
Email: giovanni.pizzi@psi.ch