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Interpretable, calibrated neural networks for analysis and understanding of inelastic neutron scattering data. | LitMetric

Interpretable, calibrated neural networks for analysis and understanding of inelastic neutron scattering data.

J Phys Condens Matter

ISIS Neutron and Muon Source, STFC Rutherford Appleton Laboratory, Harwell Campus, Didcot, OX11 0QX, United Kingdom.

Published: April 2021

AI Article Synopsis

  • Deep neural networks are powerful tools for learning complex relationships in data, but they face challenges in scientific contexts like inelastic neutron scattering due to limited labeled data, uncertainty quantification, and interpretability.
  • The study uses simulated data to train a deep neural network to differentiate between two magnetic exchange models in a half-doped manganite, showcasing the model's capabilities.
  • By applying uncertainty quantification methods and class activation maps, the research highlights the importance of realistic training data and identifies key features in the data that influence the network's classification outcomes.

Article Abstract

Deep neural networks (NNs) provide flexible frameworks for learning data representations and functions relating data to other properties and are often claimed to achieve 'super-human' performance in inferring relationships between input data and desired property. In the context of inelastic neutron scattering experiments, however, as in many other scientific scenarios, a number of issues arise: (i) scarcity of labelled experimental data, (ii) lack of uncertainty quantification on results, and (iii) lack of interpretability of the deep NNs. In this work we examine approaches to all three issues. We use simulated data to train a deep NN to distinguish between two possible magnetic exchange models of a half-doped manganite. We apply the recently developed deterministic uncertainty quantification method to provide error estimates for the classification, demonstrating in the process how important realistic representations of instrument resolution in the training data are for reliable estimates on experimental data. Finally we use class activation maps to determine which regions of the spectra are most important for the final classification result reached by the network.

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Source
http://dx.doi.org/10.1088/1361-648X/abea1cDOI Listing

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