Stanford Advisors


All Publications


  • Characterization of Two Fast-Turnaround Dry Dilution Refrigerators for Scanning Probe Microscopy JOURNAL OF LOW TEMPERATURE PHYSICS Barber, M. E., Li, Y., Gibson, J., Yu, J., Jiang, Z., Hu, Y., Ji, Z., Nandi, N., Hoke, J. C., Bishop-Van Horn, L., Arias, G. R., Van Harlingen, D. J., Moler, K. A., Shen, Z., Kou, A., Feldman, B. E. 2024
  • Capturing dynamical correlations using implicit neural representations. Nature communications Chitturi, S. R., Ji, Z., Petsch, A. N., Peng, C., Chen, Z., Plumley, R., Dunne, M., Mardanya, S., Chowdhury, S., Chen, H., Bansil, A., Feiguin, A., Kolesnikov, A. I., Prabhakaran, D., Hayden, S. M., Ratner, D., Jia, C., Nashed, Y., Turner, J. J. 2023; 14 (1): 5852

    Abstract

    Understanding the nature and origin of collective excitations in materials is of fundamental importance for unraveling the underlying physics of a many-body system. Excitation spectra are usually obtained by measuring the dynamical structure factor, S(Q, ω), using inelastic neutron or x-ray scattering techniques and are analyzed by comparing the experimental results against calculated predictions. We introduce a data-driven analysis tool which leverages 'neural implicit representations' that are specifically tailored for handling spectrographic measurements and are able to efficiently obtain unknown parameters from experimental data via automatic differentiation. In this work, we employ linear spin wave theory simulations to train a machine learning platform, enabling precise exchange parameter extraction from inelastic neutron scattering data on the square-lattice spin-1 antiferromagnet La2NiO4, showcasing a viable pathway towards automatic refinement of advanced models for ordered magnetic systems.

    View details for DOI 10.1038/s41467-023-41378-4

    View details for PubMedID 37730824

    View details for PubMedCentralID 8662964