Elucidating proximity magnetism through polarized neutron reflectometry and machine learning

Andrejevic, Nina; Chen, Zhantao; Nguyễn, Thanh Bình; Fan, Leon; Henry, Heiberger,; Zhou, Ling-Jie; Zhao, Yifan; Chang, Cui-Zu; Grutter, Alexander J.; Li, Mingda

doi:10.48550/arxiv.2109.08005

Cited by 2 publications

(4 citation statements)

References 78 publications

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“…Other groups have tried to employ autoencoder architectures for the analysis of reflectivity data. For example, Andrejevic et al (2021) trained a variational autoencoder to compress reflectivity curves from polarized neutron reflectometry into an information-dense latent space. They deliberately designed the architecture in such a way that the sample parameters can be retrieved from the latent-space variables.…”

Section: X-ray and Neutron Reflectivitymentioning

confidence: 99%

Machine learning for scattering data: strategies, perspectives and applications to surface scattering

Hinderhofer¹,

Greco²,

Starostin³

et al. 2023

J Appl Cryst

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Machine learning (ML) has received enormous attention in science and beyond. Discussed here are the status, opportunities, challenges and limitations of ML as applied to X-ray and neutron scattering techniques, with an emphasis on surface scattering. Typical strategies are outlined, as well as possible pitfalls. Applications to reflectometry and grazing-incidence scattering are critically discussed. Comment is also given on the availability of training and test data for ML applications, such as neural networks, and a large reflectivity data set is provided as reference data for the community.

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Section: X-ray and Neutron Reflectivitymentioning

confidence: 99%

Machine learning for scattering data: strategies, perspectives and applications to surface scattering

Hinderhofer¹,

Greco²,

Starostin³

et al. 2023

J Appl Cryst

View full text Add to dashboard Cite

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“…Such learned representations are proposed as efficient prediction targets for supervised learning from relevant structural or chemical attributes, or as convenient parameter spaces for optimization of physical models. In Chapter 4, we implement a semi-supervised learning approach to improve upon conventional methods of parameter retrieval from polarized neutron reflectometry measurements [61]. In particular, we focus on elucidating the subtle interaction mechanisms in proximitycoupled heterostructures and show that the trained model learns an interpretable latent representation of the relevant parameters.…”

Section: Thesis Objectivesmentioning

confidence: 99%

“…The work described in this chapter is available as a pre-print in Ref. [61] and is currently under review.…”

Section: Chaptermentioning

confidence: 99%

“…In this chapter, we develop an alternate, data-driven framework to retrieve the sample parameters of candidate proximity-coupled systems from their PNR profiles with minimal user intervention [61]. Using a variational autoencoder (VAE), we map reflectometry profiles simulated from a broad range of candidate physical parameters to a low-dimensional latent space from which the true sample parameters can be readily obtained.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning-Augmented Spectroscopies for Intelligent Materials Design

Andrejevic

2022

Springer Theses

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Neutron and photon scattering and spectroscopy represent two fundamental categories of characterization techniques used to interrogate materials' structural and dynamical properties at atomic to mesoscopic length scales. As advances at scientific user facilities enable the collection of ever larger data volumes in higher-dimensional parameter spaces, the design, analysis, and interpretation of such experiments becomes both increasingly valuable and complex. At the same time, interest in novel functional and quantum materials for next-generation technologies, including dissipationless electronics, energy harvesting, and quantum computing, demands an understanding of unconventional or emergent properties beyond the scope of many approximate models. Machine learning methods are designed to leverage large, highdimensional datasets in order to detect underlying patterns and make informed predictions on related tasks. Thus, integration of these data-driven methods with neutron and photon spectroscopies has the potential to improve experimental design, accelerate and enhance data analysis, and uncover insights beyond traditional models.This thesis work demonstrates the proposed integration of machine learning to augment experimental design and analysis in the context of four different scattering and spectroscopic techniques. First, we use Euclidean neural networks to predict materials' vibrational properties directly from the atomic masses and positions of their constituent atoms, highlighting the importance of using effective materials data representations to strengthen model performance and interpretability. We then consider how machine learning methods can be applied to develop effective, low-dimensional representations of materials' spectral signatures, which we exemplify through unsupervised representation learning of Raman spectra. Such learned representations are proposed as efficient prediction targets for supervised learning from relevant structural or chemical attributes, or as convenient parameter spaces for optimization of physical models. We illustrate the latter by training a variational autoencoder to retrieve the sample parameters of proximity-coupled heterostructures from their polarized neutron reflectometry profiles with high resolution. Finally, we study the capacity of machine learning models to extract "hidden" insights from spectral data by develop-

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Elucidating proximity magnetism through polarized neutron reflectometry and machine learning

Cited by 2 publications

References 78 publications

Machine learning for scattering data: strategies, perspectives and applications to surface scattering

Machine learning for scattering data: strategies, perspectives and applications to surface scattering

Machine Learning-Augmented Spectroscopies for Intelligent Materials Design

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