no code implementations • 24 Nov 2016 • Xin Li, Alex Belianinov, Ondrej Dyck, Stephen Jesse, Chiwoo Park
We propose to formulate the identification of the lattice groups as a sparse group selection problem.
no code implementations • 14 Mar 2018 • Artem Maksov, Ondrej Dyck, Kai Wang, Kai Xiao, David B. Geohegan, Bobby G. Sumpter, Rama K. Vasudevan, Stephen Jesse, Sergei V. Kalinin, Maxim Ziatdinov
Understanding elementary mechanisms behind solid-state phase transformations and reactions is the key to optimizing desired functional properties of many technologically relevant materials.
Materials Science
1 code implementation • 13 May 2018 • Xin Li, Ondrej Dyck, Sergei V. Kalinin, Stephen Jesse
Scanning Transmission Electron Microscopy (STEM) has become the main stay for materials characterization on atomic level, with applications ranging from visualization of localized and extended defects to mapping order parameter fields.
1 code implementation • 18 Oct 2018 • Xin Li, Ondrej E. Dyck, Mark P. Oxley, Andrew R. Lupini, Leland McInnes, John Healy, Stephen Jesse, Sergei V. Kalinin
Four-dimensional scanning transmission electron microscopy (4D-STEM) of local atomic diffraction patterns is emerging as a powerful technique for probing intricate details of atomic structure and atomic electric fields.
1 code implementation • 22 Mar 2019 • Suhas Somnath, Chris R. Smith, Nouamane Laanait, Rama K. Vasudevan, Anton Ievlev, Alex Belianinov, Andrew R. Lupini, Mallikarjun Shankar, Sergei V. Kalinin, Stephen Jesse
The second is Pycroscopy, which provides algorithms for scientific analysis of nanoscale imaging and spectroscopy modalities and is built on top of pyUSID and USID.
Data Analysis, Statistics and Probability
2 code implementations • 26 Nov 2019 • Maxim Ziatdinov, Dohyung Kim, Sabine Neumayer, Rama K. Vasudevan, Liam Collins, Stephen Jesse, Mahshid Ahmadi, Sergei V. Kalinin
We investigate the ability to reconstruct and derive spatial structure from sparsely sampled 3D piezoresponse force microcopy data, captured using the band-excitation (BE) technique, via Gaussian Process (GP) methods.
Computational Physics Materials Science
1 code implementation • 10 Feb 2020 • Maxim Ziatdinov, Dohyung Kim, Sabine Neumayer, Liam Collins, Mahshid Ahmadi, Rama K. Vasudevan, Stephen Jesse, Myung Hyun Ann, Jong H. Kim, Sergei V. Kalinin
Imaging mechanisms in contact Kelvin Probe Force Microscopy (cKPFM) are explored via information theory-based methods.
Applied Physics Materials Science
1 code implementation • 25 Nov 2020 • Rama K. Vasudevan, Kyle Kelley, Hiroshi Funakubo, Stephen Jesse, Sergei V. Kalinin, Maxim Ziatdinov
Polarization dynamics in ferroelectric materials are explored via the automated experiment in Piezoresponse Force Spectroscopy.
Disordered Systems and Neural Networks Data Analysis, Statistics and Probability
no code implementations • 22 Mar 2021 • Sergei V. Kalinin, Maxim A. Ziatdinov, Jacob Hinkle, Stephen Jesse, Ayana Ghosh, Kyle P. Kelley, Andrew R. Lupini, Bobby G. Sumpter, Rama K. Vasudevan
Machine learning and artificial intelligence (ML/AI) are rapidly becoming an indispensable part of physics research, with domain applications ranging from theory and materials prediction to high-throughput data analysis.
1 code implementation • 5 Apr 2023 • Arpan Biswas, Yongtao Liu, Nicole Creange, Yu-Chen Liu, Stephen Jesse, Jan-Chi Yang, Sergei V. Kalinin, Maxim A. Ziatdinov, Rama K. Vasudevan
Optimization of experimental materials synthesis and characterization through active learning methods has been growing over the last decade, with examples ranging from measurements of diffraction on combinatorial alloys at synchrotrons, to searches through chemical space with automated synthesis robots for perovskites.