Extracting Microscopic Information from Neutron Scattering Data with Machine Learning Assistance

1. Extract microscopic information of materials from Neutron Scattering data using a Machine Learning assisted approach ANJANA SAMARAKOON Post Doctoral Research Associate, Direct Geometry Team, Oak Ridge National Laboratory

2. High performance codes and ML to address crucial bottlenecks in interpretation and analysis RuCl3: [4D Data] Dy2Ti2O7 : [3D Data]

3. Diffuse Scattering Example Diffuse Scattering Experiment Model System: Dy2Ti2O7 , A Classical Spin Ice 3 3' 2 1 Model System: Experiment at CORELLI at SNS (BL 09 / Elastic Diffuse Scattering Spectrometer)

4. Neutron Structure Factor for differnet parameter sets, By Fixing and , Three-Demensional parameter space Cost Function: We used High Performance Computing Resources, ORNL

5. Dy2Ti2O7 (Spin Ice) : Fitting Neutron Scattering data • Tried many Different Approches and Algorithems • Gradient based optimization algorithems • Gradient Decend • Conjugate Gradient Method • Powell's conjugate direction method • Conjugate Gradient • Simplex Method Optimization • Nelder-Mead • Bio inspired Optimization • Partical Swarm Cost Function:

6. Dy2Ti2O7 (Spin Ice) : Fitting Neutron Scattering data Cost Function: Total Noise = Experimental Noise + Simulation Noise Higher degree of Noise  Rugged Landscape  Hard to narrow down potential region

7. How to make use of Machine Learning appraches? Application of ML Accelerate the search of potential solution for a given dataset. Extract useful behaviours in simulation data and assist in experimental data Fitting. Understand higher dimensional order parameters and phase diagrams (Phases and phase transitions). Identify interesting regions in higher dimensional paramter-space and predict possible future experiments.

8. Learning the topography of the landscape 2D Example 1D Example

9. Learning the topography of the landscape

10. Methodology Here, we used Autoencoder type Neural Network archetecture, Regularized Data

11. Extracted Features Corresponding activation function

12. Machine Learning Assisted Optimization Region from a simple cost function Modified Cost Function: Train an autoencoder and compress S(Q) information to lower dimention (Feature Space) S(L), Advantage: Can reduce the noice level since we compare features instead of raw data  Narrow down the region of interest Region from improved cost function

13. Solution to Diffuse Scattering Data Neutron Scattering Heat Capacity Combined Note: Here we have only used 680 mK zero field diffuse scattering data and heat capcity data

14. Heat Capacity Experiment Best Model

15. Validation of the solution

16. Biproducts of Machine Learning Can Generate of Phase Map Can learn hidden correlation functions and their order parameters

18. Inelastic Neutron Scattering Example Inelastic Single crystal data Model System: -RuCl3 , A proximate Kitaev liquid Model: Experiment at SEQUOIA at SNS (BL 17 / Fine-Resolution Fermi Chopper Spectrometer) Reference: Banerjee, A., Lampen-Kelley, P., Knolle, J., Balz, C., Aczel, A. A., Winn, B., ... & Savici, A. T. (2018). Excitations in the field-induced quantum spin liquid state of α-RuCl 3. npj Quantum Materials, 3(1), 8.

19. Modeling and Understanding Z.Z. 3D FM Z.Z. 2D

20. Powder Inelastic Example Powder Inelastic data from SNS Model System: BaNd2ZnO5 , The Shastry-Sutherland lattice structure Two experiments: at 1.55 meV and 3.32 meV Symmetry allowed Hamiltonian, 10 Dimensional parameter space

21. Modeling and Understanding

22. Solution manifold and Principal Componant Analysis Other relations between parameters extracted from PCA,

23. Conclusion and Future directions Future directions Conclusion • Addressed the challenge of experimental artifacts, denoising and background, and problem of working with 3D and 4D datasets. • We have introduced a new machine learning assisted approach to handle large volumes of multimodal data automatically. • By means of HPC, we can categorize and map high dimensional parameters spaces and model complex behavior in materials. • Pretrained neural nets can be used during experiment for analysis and to plan and guide the experiment. • Directly extract physics from simulations AI challenge • Use to identify new materials and models • Train nets for wide range of cases • Deep learning on doping, temperature, quantum corrections etc • New protocol to identify quantum coherence and quantum vs classical • Thermal and spin transport combined with neutrons

24. HPC and MLTeam Project Colaborators Alan Tennant (MSTD, ORNL) Cristian Batista (NSD / UTK) • Dy2Ti2O7 project • Qiang Zhang (Experiment) • Feng Ye (Experiment) • Haidong Zhou (Crystal growth) • Santiago A. Gringera (Discussion) • -RuCl3 project • Arnab Banerjee (Experiment) • Christian Balz (Experiment) • Steve Nagler (Experiment) • BaNd2ZnO5 project • Andrew D. Christianson (Experiment) • Gabriele Sala (Experiment) • Hao Zhang (Theory) Kipton Barros (CNLS, LANL) Ying Wai Li (NCCS) Markus Eisenbach (NCCS)

25. Thank you !!! Any Questions?