The Laboratory for Materials Simulations develops the theory, algorithms, software and data required for the study of advanced materials at PSI.
Lab News & Scientific Highlights
International collaboration lays the foundation for future AI for materials via the OPTIMADE standard
Artificial intelligence (AI) is accelerating the development of new materials. A prerequisite for AI in materials research is large-scale use and exchange of data on materials, which is facilitated by a broad international standard. A major international collaboration including researchers from the LMS laboratory now presents an extended version of the OPTIMADE standard.
A potential shortcut
Today, machine learning and artificial intelligence are part of the toolkit for most researchers at PSI. In many cases these methods are fundamentally transforming the way we do science.
"Magnetostriction-Driven Muon Localization in an Antiferromagnetic Oxide" published in Phys. Rev. Lett.
A study involving PSI scientists from the LMS lab, and just published in Physical Review Letters has found that in manganese oxide, a textbook antiferromagnetic material, the site of an implanted spin-polarized muon is not well identified, but can change due to a previously neglected effect: magnetostriction.
Upcoming Events
Publications
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Liu JC, Li C, Chahib O, Wang X, Rothenbühler S, Häner R, et al.
Spin excitations of high spin iron(II) in metal–organic chains on metal and superconductor
Advanced Science. 2024. https://doi.org/10.1002/advs.202412351
DORA PSI -
Bustamante CM, Sidler D, Ruggenthaler M, Rubio Á
The relevance of degenerate states in chiral polaritonics
Journal of Chemical Physics. 2024; 161(24): 244101 (9 pp.). https://doi.org/10.1063/5.0235935
DORA PSI -
Marrazzo A, Beck S, Margine ER, Marzari N, Mostofi AA, Qiao J, et al.
Wannier-function software ecosystem for materials simulations
Reviews of Modern Physics. 2024; 96(4): 045008 (54 pp.). https://doi.org/10.1103/RevModPhys.96.045008
DORA PSI -
Lani G, Marzari N
Potential energy surfaces from many-body functionals: Analytical benchmarks and conserving many-body approximations
Physical Review Research. 2024; 6(4): 043304 (16 pp.). https://doi.org/10.1103/PhysRevResearch.6.043304
DORA PSI -
Zadoks A, Marrazzo A, Marzari N
Spectral operator representations
npj Computational Materials. 2024; 10(1): 278 (12 pp.). https://doi.org/10.1038/s41524-024-01446-9
DORA PSI