pycroscopy

TEM data quantification library through a model-based approach

Deep and Machine Learning for Microscopy

A single package for analyzing atomic resolution STEM, 4D-STEM and STEM-EELS datasets, along with basic STEM simulation functionality

Asynchronous central server for coordinating microscope hardware, enabling smarter experiments

AI-assisted intelligent realtime microscopy

Python utilities for storing, processing, and visualizing spectroscopic and imaging data

TEM analysis Gui

Virtual School – “Machine Learning and Automated Experiment in Scanning Probe Microscopy" at ORNL, October 4-7, 2021

A Gui framework for pycroscopy tools

Tools for extracting data and metadata from scientific data files

Materials for "AI for Atoms: How to Machine Learn STEM" Workshop

Scientific analysis of nanoscale materials imaging data

Digital Twin Microscope

A pure Python interface to reading and writing Gwyddion files.

Getting all relevant statistics from atomic resolution images

Framework for storing, visualizing, and processing Universal Spectroscopic and Imaging Data (USID)

Datasets for testing and demonstration purposes

Here, we will upload our deep/machine learning models and 'workflows' (such as AtomNet, DefectNet, SymmetryNet, etc) that aid in automated analysis of atomically resolved images

AReMS Tutorial 2025

Band Excitation and General Mode microscopy analytics software

Nomad for pycroscopy

Machine Learning in Materials Science: An Introduction through Python. Workshop as part of CNMS User Meeting 2019

Development of distributed framework for USID processing

Self-help guides for scalable data analytics

Utilities for facilitating the storage, access, and visualization of NSID data

This is a repository setup for the Machine Learning in Microscopy Virtual Summer School

Introduction to the Pycroscopy Ecosystem

CNMS User Meeting Workshop 2018: Machine learning in Materials Science with Python

FerroSchool Winter 2022 in Calgary

Notebooks for MRS2021 tutorial