Machine Learning assisted optimization and parameter space exploration dataset of spin ice Hamiltonian

Samarakoon, Anjana Malinge; Tennant, Alan; Batista, Cristian D; Barros, Kipton; Li, Ying Wai; Eisenbach, Markus; A, Santiago

doi:10.13139/ORNLNCCS/1797498

Title: Machine Learning assisted optimization and parameter space exploration dataset of spin ice Hamiltonian

Dataset
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Abstract

This repository contains both simulated and experimental structure factor data for the data challenge involving the inverse scattering problem. The simulated data were generated during a machine-learning-assisted optimization routine described in ref[1]. The experimental structure factor was measured on a rare-earth oxide, Dy2Ti2O7 using diffuse neutron scattering from time-of-flight techniques on the CORELLI instrument at the Spallation Neutron Source, Oak Ridge National Laboratory. A Metropolis Monte Carlo code implemented to run in a High-performance computing setting was used to calculated simulated structure factors for the spin-ice Hamiltonian at 680 mK, which is the same temperature as for the experimental data. The total size of all the files in this repository is 5.12 GB. A detailed description of the files is given below. ExperimentalData_630mK.dat â A linearized version of 3-dimensional experimental data of size 61Ã81Ã21. This data was processed to remove an estimation of non-magnetic background, including nuclear scattering signal and instrumentation background. Parameters.dat â 6700 samples were evaluated over the 4-dimensional parameter space (J_1, J_2, J_3 and J_(3^' )). There is an additional parameter, D in the spin Hamiltonian to mimic the dipolar interaction between magnetic ions. However, this parameter, D was fixed to a value determined by prior work.more »« less

Authors:

Samarakoon, Anjana Malinge; Tennant, Alan; Batista, Cristian D; Barros, Kipton; Li, Ying Wai; Eisenbach, Markus; A, Santiago

ORNL-OLCF

Publication Date:: Fri Apr 29 00:00:00 EDT 2022

DOE Contract Number:: AC05-00OR22725

Research Org.:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Org.:: Office of Science (SC)

Subject:: 36 MATERIALS SCIENCE; 75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND SUPERFLUIDITY; 97 MATHEMATICS AND COMPUTING

OSTI Identifier:: 1797498

DOI:: https://doi.org/10.13139/ORNLNCCS/1797498

Citation Formats


                    Samarakoon, Anjana Malinge, Tennant, Alan, Batista, Cristian D, Barros, Kipton, Li, Ying Wai, Eisenbach, Markus, and A, Santiago. Machine Learning assisted optimization and parameter space exploration dataset of spin ice Hamiltonian.  United States: N. p., 2022. 
        Web.  doi:10.13139/ORNLNCCS/1797498.

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                    Samarakoon, Anjana Malinge, Tennant, Alan, Batista, Cristian D, Barros, Kipton, Li, Ying Wai, Eisenbach, Markus, & A, Santiago. Machine Learning assisted optimization and parameter space exploration dataset of spin ice Hamiltonian.  United States.  doi:https://doi.org/10.13139/ORNLNCCS/1797498

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                    Samarakoon, Anjana Malinge, Tennant, Alan, Batista, Cristian D, Barros, Kipton, Li, Ying Wai, Eisenbach, Markus, and A, Santiago. 2022.  
"Machine Learning assisted optimization and parameter space exploration dataset of spin ice Hamiltonian".  United States.  doi:https://doi.org/10.13139/ORNLNCCS/1797498.  https://www.osti.gov/servlets/purl/1797498. Pub date:Fri Apr 29 00:00:00 EDT 2022

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@article{osti_1797498,

  title        = {Machine Learning assisted optimization and parameter space exploration dataset of spin ice Hamiltonian},

  author       = {Samarakoon, Anjana Malinge and Tennant, Alan and Batista, Cristian D and Barros, Kipton and Li, Ying Wai and Eisenbach, Markus and A, Santiago},

  abstractNote = {This repository contains both simulated and experimental structure factor data for the data challenge involving the inverse scattering problem. The simulated data were generated during a machine-learning-assisted optimization routine described in ref[1]. The experimental structure factor was measured on a rare-earth oxide, Dy2Ti2O7 using diffuse neutron scattering from time-of-flight techniques on the CORELLI instrument at the Spallation Neutron Source, Oak Ridge National Laboratory. A Metropolis Monte Carlo code implemented to run in a High-performance computing setting was used to calculated simulated structure factors for the spin-ice Hamiltonian at 680 mK, which is the same temperature as for the experimental data. The total size of all the files in this repository is 5.12 GB. A detailed description of the files is given below. ExperimentalData_630mK.dat â A linearized version of 3-dimensional experimental data of size 61Ã81Ã21. This data was processed to remove an estimation of non-magnetic background, including nuclear scattering signal and instrumentation background. Parameters.dat â 6700 samples were evaluated over the 4-dimensional parameter space (J_1, J_2, J_3 and J_(3^' )). There is an additional parameter, D in the spin Hamiltonian to mimic the dipolar interaction between magnetic ions. However, this parameter, D was fixed to a value determined by prior work. This file contains five columns for the parameters J_1, J_2, J_3, J_(3^' ) and D respectively. 3D_Simulation_Data.dat â The simulated structure factor, S(Q) data are included in this file. Each raw contains a linearized array of 3D volumes of S(Q) calculated for the parameter set given in the corresponding row of the file "Parameters.dat". The size of the volume data was matched to the experimental data. Qx(h,-h,0).dat, Qy(k,k,-2k).dat, Qz(l,l,l).dat â These files contain the h, k, and l values along with the reciprocal vectors [h,-h,0], [k,k,-2k] and [l,l,l] respectively.},

  doi          = {10.13139/ORNLNCCS/1797498},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Fri Apr 29 00:00:00 EDT 2022},

  month        = {Fri Apr 29 00:00:00 EDT 2022}

}

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DOI: https://doi.org/10.13139/ORNLNCCS/1797498

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