1 code implementation • 30 Mar 2023 • Mani Valleti, Yongtao Liu, Sergei Kalinin
Electron, optical, and scanning probe microscopy methods are generating ever increasing volume of image data containing information on atomic and mesoscale structures and functionalities.
no code implementations • 25 Mar 2023 • Mani Valleti, Rama K. Vasudevan, Maxim A. Ziatdinov, Sergei V. Kalinin
The ability of deep learning methods to perform classification and regression tasks relies heavily on their capacity to uncover manifolds in high-dimensional data spaces and project them into low-dimensional representation spaces.
no code implementations • 2 Sep 2020 • Sergei V. Kalinin, Shuai Zhang, Mani Valleti, Harley Pyles, David Baker, James J. De Yoreo, Maxim Ziatdinov
The dynamic of complex ordering systems with active rotational degrees of freedom exemplified by protein self-assembly is explored using a machine learning workflow that combines deep learning-based semantic segmentation and rotationally invariant variational autoencoder-based analysis of orientation and shape evolution.
Soft Condensed Matter
1 code implementation • 9 Apr 2020 • Sergei V. Kalinin, Mani Valleti, Rama K. Vasudevan, Maxim Ziatdinov
Statistical physics models ranging from simple lattice to complex quantum Hamiltonians are one of the mainstays of modern physics, that have allowed both decades of scientific discovery and provided a universal framework to understand a broad range of phenomena from alloying to frustrated and phase-separated materials to quantum systems.
Materials Science Computational Physics