Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Integration of Machine Learning with Neutron Scattering: Hamiltonian
  Tuning in Spin Ice with Pressure   Quantum materials research requires co-design of theory with experiments and
involves demanding simulations and the analysis of vast quantities of data,
usually including pattern recognition and clustering. Artificial intelligence
is a natural route to optimise these processes and bring theory and experiments
together. Here we propose a scheme that integrates machine learning with
high-performance simulations and scattering measurements, covering the pipeline
of typical neutron experiments. Our approach uses nonlinear autoencoders
trained on realistic simulations along with a fast surrogate for the
calculation of scattering in the form of a generative model. We demonstrate
this approach in a highly frustrated magnet, Dy$_2$Ti$_2$O$_7$, using machine
learning predictions to guide the neutron scattering experiment under
hydrostatic pressure, extract material parameters and construct a phase
diagram. Our scheme provides a comprehensive set of capabilities that allows
direct integration of theory along with automated data processing and provides
on a rapid timescale direct insight into a challenging condensed matter system.

27
The categories of this paper are: comp-gas
2
The categories of this paper are: math
0
The categories of this paper are: hep-ph
25
The categories of this paper are: patt-sol
12
The categories of this paper are: nucl-th
6
The categories of this paper are: astro-ph
19
The categories of this paper are: eess
24
The categories of this paper are: dg-ga
17
The categories of this paper are: q-fin
20
The categories of this paper are: acc-phys
1
The categories of this paper are: cs
16
The categories of this paper are: stat
26
The categories of this paper are: bayes-an
22
The categories of this paper are: chao-dyn
33
The categories of this paper are: plasm-ph
37
The categories of this paper are: supr-con