Extraction of interaction parameters for from neutron data using machine learning
Abstract
Single-crystal inelastic neutron-scattering (INS) data contain rich information about the structure and dynamics of a material. Yet the challenge of matching sophisticated theoretical models with large data volumes is compounded by computational complexity and the ill-posed nature of the inverse scattering problem. Here we utilize a novel machine-learning (ML)-assisted framework featuring multiple neural network architectures to address this via high-dimensional modeling and numerical methods. A comprehensive data set of diffraction and INS measured on the Kitaev material α-RuCl3 is processed to extract its Hamiltonian. Semiclassical Landau-Lifshitz dynamics and Monte-Carlo simulations were employed to explore the parameter space of an extended Kitaev-Heisenberg Hamiltonian. A ML-assisted iterative algorithm was developed to map the uncertainty manifold to match experimental data, a nonlinear autoencoder was used to undertake information compression, and radial basis networks were utilized as fast surrogates for diffraction and dynamics simulations to predict potential spin Hamiltonians with uncertainty. Exact diagonalization calculations were employed to assess the impact of quantum fluctuations on the selected parameters around the best prediction.
- Authors:
- Publication Date:
- Research Org.:
- Argonne National Laboratory (ANL), Argonne, IL (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Spallation Neutron Source (SNS) and Oak Ridge Leadership Computing Facility (OLCF); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division; USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities Division; USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR). Scientific Discovery through Advanced Computing (SciDAC)
- OSTI Identifier:
- 1873436
- Alternate Identifier(s):
- OSTI ID: 1873526; OSTI ID: 1883884
- Grant/Contract Number:
- AC05-00OR22725; AC02-06CH11357
- Resource Type:
- Published Article
- Journal Name:
- Physical Review Research
- Additional Journal Information:
- Journal Name: Physical Review Research Journal Volume: 4 Journal Issue: 2; Journal ID: ISSN 2643-1564
- Publisher:
- American Physical Society
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND SUPERFLUIDITY; quantum spin liquid; strongly correlated systems; data analysis; deep learning; exact diagonalization; hybrid Monte Carlo algorithm; inelastic neutron scattering; Kitaev-Heisenberg model; Landau-Lifshitz model; machine learning; neutron diffraction; neutron scattering; simulated annealing
Citation Formats
Samarakoon, Anjana M., Laurell, Pontus, Balz, Christian, Banerjee, Arnab, Lampen-Kelley, Paula, Mandrus, David, Nagler, Stephen E., Okamoto, Satoshi, and Tennant, D. Alan. Extraction of interaction parameters for α − RuCl 3 from neutron data using machine learning. United States: N. p., 2022.
Web. doi:10.1103/PhysRevResearch.4.L022061.
Samarakoon, Anjana M., Laurell, Pontus, Balz, Christian, Banerjee, Arnab, Lampen-Kelley, Paula, Mandrus, David, Nagler, Stephen E., Okamoto, Satoshi, & Tennant, D. Alan. Extraction of interaction parameters for α − RuCl 3 from neutron data using machine learning. United States. https://doi.org/10.1103/PhysRevResearch.4.L022061
Samarakoon, Anjana M., Laurell, Pontus, Balz, Christian, Banerjee, Arnab, Lampen-Kelley, Paula, Mandrus, David, Nagler, Stephen E., Okamoto, Satoshi, and Tennant, D. Alan. Tue .
"Extraction of interaction parameters for α − RuCl 3 from neutron data using machine learning". United States. https://doi.org/10.1103/PhysRevResearch.4.L022061.
@article{osti_1873436,
title = {Extraction of interaction parameters for α − RuCl 3 from neutron data using machine learning},
author = {Samarakoon, Anjana M. and Laurell, Pontus and Balz, Christian and Banerjee, Arnab and Lampen-Kelley, Paula and Mandrus, David and Nagler, Stephen E. and Okamoto, Satoshi and Tennant, D. Alan},
abstractNote = {Single-crystal inelastic neutron-scattering (INS) data contain rich information about the structure and dynamics of a material. Yet the challenge of matching sophisticated theoretical models with large data volumes is compounded by computational complexity and the ill-posed nature of the inverse scattering problem. Here we utilize a novel machine-learning (ML)-assisted framework featuring multiple neural network architectures to address this via high-dimensional modeling and numerical methods. A comprehensive data set of diffraction and INS measured on the Kitaev material α-RuCl3 is processed to extract its Hamiltonian. Semiclassical Landau-Lifshitz dynamics and Monte-Carlo simulations were employed to explore the parameter space of an extended Kitaev-Heisenberg Hamiltonian. A ML-assisted iterative algorithm was developed to map the uncertainty manifold to match experimental data, a nonlinear autoencoder was used to undertake information compression, and radial basis networks were utilized as fast surrogates for diffraction and dynamics simulations to predict potential spin Hamiltonians with uncertainty. Exact diagonalization calculations were employed to assess the impact of quantum fluctuations on the selected parameters around the best prediction.},
doi = {10.1103/PhysRevResearch.4.L022061},
journal = {Physical Review Research},
number = 2,
volume = 4,
place = {United States},
year = {Tue Jun 21 00:00:00 EDT 2022},
month = {Tue Jun 21 00:00:00 EDT 2022}
}
https://doi.org/10.1103/PhysRevResearch.4.L022061
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