Materials Software and Data Group

The Materials Software and Data (MSD) group develops simulation software, algorithms and data infrastructure to predict and interpret the properties of novel materials, with particular focus on the experiments performed at PSI's large research facilities, especially neutron and muon spectroscopies [1].

Our methodological work combines first-principles electronic structure, in particular Wannier-function-related methods [2], with machine learning and generative AI, for instance diffusion models that reconstruct hydrogen positions in crystal structures [3]. We also contribute to building the large, curated datasets on which machine-learning interatomic potentials are trained, and are working to extend machine-learning models to the electronic Hamiltonian itself. A recurring theme across this work is robustness: we develop accurate and efficient simulation protocols and pseudopotential libraries [4], together with methods to verify that different density-functional-theory implementations agree on the quantities they compute [5].

We lead the development of the AiiDA workflow engine [6] and are one of the core development teams behind the Materials Cloud web portal [7] and the AiiDAlab simulation platform [8]. Through this open-science platform we provide simulation services to researchers at PSI and worldwide, combining automated high-throughput simulations, accessible web interfaces, and open curated databases on Materials Cloud such as MC2D and MC3D [9]. We further work to make computational materials science reproducible and interoperable, developing common workflows that run across different simulation codes and co-organising OPTIMADE, the community API for exchanging materials data.

We use this infrastructure to drive our own materials-discovery projects, e.g. in the search for novel BCS superconductors [10], thermoelectrics and non-linear Hall materials. The long-term goal of the group is to accelerate discovery and characterisation by enabling autonomous laboratories that combine automated simulations with robotic experiments, driven by autonomous algorithms. We are already applying this approach to battery optimisation, to autonomous thin-layer growth, and to the mapping of the Fermi surface of metals.

Selected publications

[1] P. Bonfà et al., Magnetostriction-driven muon localization in an antiferromagnetic oxide, Phys. Rev. Lett. 132, 046701 (2024); I. J. Onuorah et al., Automated computational workflows for muon spin spectroscopy, Digit. Discov. 4, 523 (2025).

[2] G. Pizzi et al., Wannier90 as a community code: new features and applications, J. Phys. Condens. Matter 32, 165902 (2020); A. Marrazzo et al., Wannier-function software ecosystem for materials simulations, Rev. Mod. Phys. 96, 045008 (2024).

[3] T. Reents et al., Score-based diffusion models for accurate crystal-structure inpainting and reconstruction of hydrogen positions, npj Comput. Mater. 12, 203 (2026).

[4] G. de Miranda Nascimento et al., Accurate and efficient protocols for high-throughput first-principles materials simulations, npj Comput. Mater. 12, 272 (2026).

[5] E. Bosoni et al., How to verify the precision of density-functional-theory implementations via reproducible and universal workflows, Nat. Rev. Phys. 6, 45 (2024).

[6] S. P. Huber et al., AiiDA 1.0, a scalable computational infrastructure for automated reproducible workflows and data provenance, Sci. Data 7, 300 (2020).

[7] L. Talirz et al., Materials Cloud, a platform for open computational science, Sci. Data 7, 299 (2020).

[8] X. Wang et al., Making atomistic materials calculations accessible with the AiiDAlab Quantum ESPRESSO app, npj Comput. Mater. 12, 72 (2026); A. V. Yakutovich et al., Accelerating discovery across scientific disciplines through reproducible workflows with AiiDAlab, Digit. Discov. 5, 2310 (2026).

[9] N. Mounet et al., Two-dimensional materials from high-throughput computational exfoliation of experimentally known compounds, Nature Nanotech. 13, 246 (2018); S. P. Huber et al., MC3D: The Materials Cloud computational database of experimentally known stoichiometric inorganics, Digit. Discov. 5, 1114 (2026).

[10] M. Bercx et al., Charting the landscape of Bardeen-Cooper-Schrieffer superconductors in experimentally known compounds, PRX Energy 4, 033012 (2025).

Group photo

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.