canSAS 2023

Session

Machine learning based SAS data analysis: Chair: Sylvain Prévost + Narayanan Theyencheri

16 Oct 2023, 14:00

31. The Autonomous Formulation Lab: Industrial Formulation Optimization Combining SAS & ML

Tyler Martin (NIST)

16/10/2023, 14:00

Talk

Societal need and regulations are driving reformulation of materials and products so that they reduce the pace of climate change and cause less harm to humanity and the environment. While scattering methods (SAXS, SANS, WAXS) are workhorse techniques for characterizing formulations, they are challenged to keep up with the resultant rapid pace of redesign. Consumer and industrial formulations...

32. Machine Learning for SAS: A Lay of the Land – Pitfalls, Beartraps and All

Tim SNOW (Diamond Light Source)

16/10/2023, 14:30

Talk

At the 2017 canSAS workshop I was first introduced to the concept of applying Machine Learning (ML) algorithms to analysing scientific datasets. Since then I have moved towards adopting an increasing number of ML based techniques for data analysis with a view towards developing truly adaptive experimentation.

Lofty words.

Wondrous ideals.

I would like to share with the community what...

21. Inferring Lyotropic Phase Topology through Scattering using Deep Learning

Wei-Ren Chen (Oak Ridge National Laboratory)

16/10/2023, 15:00

Talk

Lyotropic phases, which encompass structures like lamellar or sponge formations, constitute a significant category within the realm of soft matter. The characterization of these lyotropic phases has often relied on the technique of small angle scattering. The impact of curvatures on the diverse lyotropic mesomorphism has been widely acknowledged. However, conventional regression analysis based...

26. Machine-learning-assisted Analysis of Small Angle X-ray Scattering

Shun Yu (RISE Research Institute of Sweden)

16/10/2023, 15:30

Talk

Small-angle X-ray scattering (SAXS) is a powerful characterization technique for nanoscale structures in materials. The analysis of SAXS is a modeling-heavy process to find a plausible structure model that corresponds to the measured scattering intensity due to the inherent “phase problem” of the X-ray detection. Despite various scientific computing tools to assist the model selection, the...