Data Science and Machine Learning for polymer films and beyond

As a powerful example of how machine learning (ML) algorithms can streamline discovery from experimental data, scientists at the LBNL Advanced Light Source have employed Convolutional Neural Networks (CNN) [1, 2] to enable lattice structure classification using diffraction patterns, and Gaussian process regression to construct surrogate models and error functions based on the limited experimental data. Diffraction patterns have come from Grazing Incidence Small Angle X-ray Scattering (GISAXS), a surface sensitive technique with increasingly usage growth in probing complex morphologies, such as conductive polymers. GISAXS allows electron density correlation analyses at surfaces by combining features from small-angle X-ray scattering and diffuse X-ray reflectivity. Resulting scattering patterns work as signatures, which depend on the size, shape, and arrangement of the nano-structured components. We will illustrate some of the advantages of using ML methods over traditional ways of searching for configurations in large materials databases. We will also discuss scaling analysis to high-throughput data to enable quick selection of materials, and benefits of autonomous experiments [3], faster experimental sessions and accelerated scientific discovery from materials science samples.
[1] Ushizima, Bale, Bethel, Ercius, Helms, Krishnam, Grinberg, Haranczyk, Macdowell, Odziomek, Parkinson, Ritchie, and Yang. IDEAL: Images across Domains, Experiments, Algorithms and Learning, Journal of Minerals, Metals and Materials, 68(11), 2963-2972, 2016.
[2] Liu, Melton, Venkatakrishnam, Pandolfi, Freychet, Kumar, Tang, Hexemer, Ushizima, Convolutional Neural Networks for Grazing Incidence X-ray Scattering Patterns: Thin Film Structure Identification, Materials Research Society , pp.1-7, 2019.
[3] Noack, Yager, Fukuto, Doerk, Li, Sethian, A Kriging-Based Approach to Autonomous Experimentation with Applications to X-Ray Scattering, Nature Scientific Reports 9: 11809, 2019.