A shift-invariant variational autoencoder (shift-VAE) is developed as an unsupervised method for the analysis of spectral data in the presence of shifts along the parameter axis, disentangling the physically-relevant shifts from other latent variables.
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.
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
Deep neural networks ("deep learning") have emerged as a technology of choice to tackle problems in natural language processing, computer vision, speech recognition and gameplay, and in just a few years has led to superhuman level performance and ushered in a new wave of "AI."
Here we suggest and implement a Gaussian Process based methods that allow to effectively sample the degenerate parameter space of a complex non-local model to output regions of parameter space which yield desired functionalities.
Disordered Systems and Neural Networks Materials Science Computational Physics
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
Imaging mechanisms in contact Kelvin Probe Force Microscopy (cKPFM) are explored via information theory-based methods.
Applied Physics Materials Science
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 • 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
Understanding elementary mechanisms behind solid-state phase transformations and reactions is the key to optimizing desired functional properties of many technologically relevant materials.