81 research outputs found
Combining Variational Autoencoders and Physical Bias for Improved Microscopy Data Analysis
Electron and scanning probe microscopy produce vast amounts of data in the
form of images or hyperspectral data, such as EELS or 4D STEM, that contain
information on a wide range of structural, physical, and chemical properties of
materials. To extract valuable insights from these data, it is crucial to
identify physically separate regions in the data, such as phases, ferroic
variants, and boundaries between them. In order to derive an easily
interpretable feature analysis, combining with well-defined boundaries in a
principled and unsupervised manner, here we present a physics augmented machine
learning method which combines the capability of Variational Autoencoders to
disentangle factors of variability within the data and the physics driven loss
function that seeks to minimize the total length of the discontinuities in
images corresponding to latent representations. Our method is applied to
various materials, including NiO-LSMO, BiFeO3, and graphene. The results
demonstrate the effectiveness of our approach in extracting meaningful
information from large volumes of imaging data. The fully notebook containing
implementation of the code and analysis workflow is available at
https://github.com/arpanbiswas52/PaperNotebooksComment: 20 pages, 7 figures in main text, 4 figures in Supp Ma
Physics discovery in nanoplasmonic systems via autonomous experiments in Scanning Transmission Electron Microscopy
Physics-driven discovery in an autonomous experiment has emerged as a dream
application of machine learning in physical sciences. Here we develop and
experimentally implement a deep kernel learning workflow combining the
correlative prediction of the target functional response and its uncertainty
from the structure, and physics-based selection of acquisition function, which
autonomously guides the navigation of the image space. Compared to classical
Bayesian optimization methods, this approach allows to capture the complex
spatial features present in the images of realistic materials, and dynamically
learn structure-property relationships. In combination with the flexible
scalarizer function that allows to ascribe the degree of physical interest to
predicted spectra, this enables physical discovery in automated experiment.
Here, this approach is illustrated for nanoplasmonic studies of nanoparticles
and experimentally implemented in a truly autonomous fashion for bulk- and edge
plasmon discovery in MnPS3, a lesser-known beam-sensitive layered 2D material.
This approach is universal, can be directly used as-is with any specimen, and
is expected to be applicable to any probe-based microscopic techniques
including other STEM modalities, Scanning Probe Microscopies, chemical, and
optical imaging
Describing condensed matter from atomically resolved imaging data: from structure to generative and causal models
The development of high-resolution imaging methods such as electron and
scanning probe microscopy and atomic probe tomography have provided a wealth of
information on structure and functionalities of solids. The availability of
this data in turn necessitates development of approaches to derive quantitative
physical information, much like the development of scattering methods in the
early XX century which have given one of the most powerful tools in condensed
matter physics arsenal. Here, we argue that this transition requires adapting
classical macroscopic definitions, that can in turn enable fundamentally new
opportunities in understanding physics and chemistry. For example, many
macroscopic definitions such as symmetry can be introduced locally only in a
Bayesian sense, balancing the prior knowledge of materials' physics and
experimental data to yield posterior probability distributions. At the same
time, a wealth of local data allows fundamentally new approaches for the
description of solids based on construction of statistical and physical
generative models, akin to Ginzburg-Landau thermodynamic models. Finally, we
note that availability of observational data opens pathways towards exploring
causal mechanisms underpinning solid structure and functionality
AtomAI: A Deep Learning Framework for Analysis of Image and Spectroscopy Data in (Scanning) Transmission Electron Microscopy and Beyond
AtomAI is an open-source software package bridging instrument-specific Python
libraries, deep learning, and simulation tools into a single ecosystem. AtomAI
allows direct applications of the deep convolutional neural networks for atomic
and mesoscopic image segmentation converting image and spectroscopy data into
class-based local descriptors for downstream tasks such as statistical and
graph analysis. For atomically-resolved imaging data, the output is types and
positions of atomic species, with an option for subsequent refinement. AtomAI
further allows the implementation of a broad range of image and spectrum
analysis functions, including invariant variational autoencoders (VAEs). The
latter consists of VAEs with rotational and (optionally) translational
invariance for unsupervised and class-conditioned disentanglement of
categorical and continuous data representations. In addition, AtomAI provides
utilities for mapping structure-property relationships via im2spec and spec2im
type of encoder-decoder models. Finally, AtomAI allows seamless connection to
the first principles modeling with a Python interface, including molecular
dynamics and density functional theory calculations on the inferred atomic
position. While the majority of applications to date were based on atomically
resolved electron microscopy, the flexibility of AtomAI allows straightforward
extension towards the analysis of mesoscopic imaging data once the labels and
feature identification workflows are established/available. The source code and
example notebooks are available at https://github.com/pycroscopy/atomai
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