Integrating Machine Learning with Neutron Scattering

S. A. GRIGERA

Puerto Varas

Congreso; Quantum Optics and Solid State VIII, Puerto Varas, Chile, December 3--7, 2022.; 2022

Quantum materials research requires co-design of theory with experiments and involvesdemanding simulations and the analysis of vast quantities of data, usually including patternrecognition and clustering. Artificial intelligence is a natural route to optimise these processesand bring theory and experiments together. This talk will discuss a scheme that integratesmachine learning (ML) with high-performance simulations and scattering measurements [1].This approach uses nonlinear autoencoders trained on realistic simulations along with a fastsurrogate for the calculation of scattering in the form of a generative model. As an example ofimplementation of these techniques and of the approach, I will discuss how ML can be used toextract an effective Hamiltonian for the highly frustrated magnet Dy 2 Ti 2 O 7 and how thescheme was used to guide neutron scattering experiment under hydrostatic pressure, extractmaterial parameters and construct a phase diagram [2].