59 citations · 60 across the 6 of their papers we have counts for
6 papers
Interactive Visual Study of Multiple Attributes Learning Model of X-Ray Scattering Images
Xinyi Huang, Suphanut Jamonnak, Ye Zhao +4
Existing interactive visualization tools for deep learning are mostly applied to the training, debugging, and refinement of neural network models working on natural images. However…
Autonomous Materials Discovery Driven by Gaussian Process Regression with Inhomogeneous Measurement Noise and Anisotropic Kernels
Marcus M. Noack, Gregory S. Doerk, Ruipeng Li +4
A majority of experimental disciplines face the challenge of exploring large and high-dimensional parameter spaces in search of new scientific discoveries. Materials science is no…
Visual Understanding of Multiple Attributes Learning Model of X-Ray Scattering Images
Xinyi Huang, Suphanut Jamonnak, Ye Zhao +4
This extended abstract presents a visualization system, which is designed for domain scientists to visually understand their deep learning model of extracting multiple attributes i…
The Implications of Grain Size Variation in Magnetic Field Alignment of Block Copolymer Blends
Yekaterina Rokhlenko, Paweł W. Majewski, Steven R. Larson +3
Recent experiments have highlighted the intrinsic magnetic anisotropy in coil-coil diblock copolymers, specifically in poly(styrene-b-4-vinylpyridine) (PS-b-P4VP), that enables mag…
X-ray Scattering Image Classification Using Deep Learning
Boyu Wang, Kevin Yager, Dantong Yu +1
Visual inspection of x-ray scattering images is a powerful technique for probing the physical structure of materials at the molecular scale. In this paper, we explore the use of de…
Magnetic alignment of block copolymer microdomains by intrinsic chain anisotropy
Yekaterina Rokhlenko, Kai Zhang, Manesh Gopinadhan +6
We examine the role of intrinsic chain susceptibility anisotropy in magnetic field directed self-assembly of a block copolymer using \textit{in situ} X-ray scattering. Alignment of…