Extraction of interaction parameters for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>α</mml:mi><mml:mtext>−</mml:mtext><mml:msub><mml:mi>RuCl</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:mrow></mml:math> from neutron data using machine learning
Anjana Samarakoon, Pontus Laurell, Christian Balz, Arnab Banerjee, Paula Lampen-Kelley, David Mandrus, S. E. Nagler, Satoshi Okamoto, D. M. Tennant
Abstract
This article introduces a machine learning assisted workflow in solving inverse scattering problems and presents an implementation in inelastic neutron scattering data on $\ensuremath{\alpha}$-RuCl${}_{3}$.
Topics & Concepts
AlgorithmComputer scienceParameter spaceInelastic neutron scatteringAutoencoderStatistical physicsPhysicsArtificial intelligenceScatteringArtificial neural networkMachine learningNeutron scatteringQuantum mechanicsMathematicsGeometryAdvanced Condensed Matter PhysicsMachine Learning in Materials ScienceMagnetic and transport properties of perovskites and related materials