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Found 13 papers, 9 papers with code
Bayesian Co-navigation: Dynamic Designing of the Materials Digital Twins via Active Learning
Hence, the integration of theory within automated and autonomous experimental setups, or theory in the loop automated experiment, is emerging as a crucial objective for accelerating scientific research.
A dynamic Bayesian optimized active recommender system for curiosity-driven Human-in-the-loop automated experiments
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.
Deep Kernel Methods Learn Better: From Cards to Process Optimization
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.
Decoding the shift-invariant data: applications for band-excitation scanning probe microscopy
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.
Automated and Autonomous Experiment in Electron and Scanning Probe Microscopy
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.
Autonomous Experiments in Scanning Probe Microscopy and Spectroscopy: Choosing Where to Explore Polarization Dynamics in Ferroelectrics
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
Off-the-shelf deep learning is not enough: parsimony, Bayes and causality
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."
Guided search for desired functional responses via Bayesian optimization of generative model: hysteresis loop shape engineering in ferroelectrics
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
Exploration of lattice Hamiltonians for functional and structural discovery via Gaussian Process-based Exploration-Exploitation
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
Super-resolution and signal separation in contact Kelvin Probe Force Microscopy of Electrochemically Active Ferroelectric Materials
Imaging mechanisms in contact Kelvin Probe Force Microscopy (cKPFM) are explored via information theory-based methods.
Applied Physics Materials Science
Imaging Mechanism for Hyperspectral Scanning Probe Microscopy via Gaussian Process Modelling
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
USID and Pycroscopy -- Open frameworks for storing and analyzing spectroscopic and imaging data
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
Deep Learning Analysis of Defect and Phase Evolution During Electron Beam Induced Transformations in WS2
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
