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.
Un raccourci possible
L’apprentissage machine et l’intelligence artificielle font désormais partie des outils de la plupart des scientifiques du PSI. Ces méthodes modifient parfois en profondeur la 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.
Publications
-
Vogler M, Steensen SK, Ramírez FF, Merker L, Busk J, Carlsson JM, et al.
Autonomous battery optimization by deploying distributed experiments and simulations
Advanced Energy Materials. 2024: 2403263 (13 pp.). https://doi.org/10.1002/aenm.202403263
DORA PSI -
Yen Y, Krieger JA, Yao M, Robredo I, Manna K, Yang Q, et al.
Controllable orbital angular momentum monopoles in chiral topological semimetals
Nature Physics. 2024. https://doi.org/10.1038/s41567-024-02655-1
DORA PSI -
Pazhedath A, Bastonero L, Marzari N, Simoncelli M
First-principles characterization of thermal conductivity in LaPO4 -based alloys
Physical Review Applied. 2024; 22(2): 024064 (21 pp.). https://doi.org/10.1103/PhysRevApplied.22.024064
DORA PSI -
Du D, Baird TJ, Eimre K, Bonella S, Pizzi G
Jupyter widgets and extensions for education and research in computational physics and chemistry
Computer Physics Communications. 2024; 305: 109353 (11 pp.). https://doi.org/10.1016/j.cpc.2024.109353
DORA PSI -
Liu X, Erbas B, Conde-Rubio A, Rivano N, Wang Z, Jiang J, et al.
Deterministic grayscale nanotopography to engineer mobilities in strained MoS2 FETs
Nature Communications. 2024; 15(1): 6934 (12 pp.). https://doi.org/10.1038/s41467-024-51165-4
DORA PSI