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

Area of Science:

• Materials Science
• Physics
• Data Science

Background:

• Machine learning (ML) is increasingly utilized across scientific disciplines.
• X-ray and neutron scattering are powerful techniques for materials characterization.
• Surface scattering, in particular, benefits from advanced data analysis methods.

Purpose of the Study:

• To review the current status, opportunities, and challenges of applying ML to X-ray and neutron scattering.
• To emphasize ML applications in surface scattering techniques like reflectometry and grazing-incidence scattering.
• To discuss data availability for ML models and provide a reference dataset.

Main Methods:

• Critical discussion of ML strategies and potential pitfalls in scattering data analysis.
• Review of ML applications in reflectometry and grazing-incidence scattering.
• Assessment of data requirements and provision of a reference reflectivity dataset.

Main Results:

• ML presents significant opportunities for advancing X-ray and neutron scattering analysis.
• Challenges include data quality, model interpretability, and generalizability.
• Availability of comprehensive datasets is crucial for successful ML implementation.

Conclusions:

• ML holds great potential to enhance the interpretation of complex scattering data.
• Addressing challenges in data and methodology will accelerate ML adoption in scattering science.
• Providing community resources, like reference datasets, is vital for progress.