To quantify chemical segregation at multiple length scales in APT in a semi-automatic way, we propose a multi-stage strategy. First, we collect composition statistics from APT datasets for 2x2x2 nm voxels. These voxel compositions are then clustered in compositional space using Gaussian mixture models to automatically identify key phases. Next, based on this compositional classification we...
Dual Phase (DP) steels are an important family of steel grades used widely in the automotive industry because of their beneficial properties such as high ultimate tensile strength (GPa range), low initial yield stress, and high early-stage strain hardening. The DP steel microstructure consists of soft ferritic grains, which are mainly responsible for ductility, and hard martensitic zones,...
In this poster, we present our research goals of a recently BiGmax funded project towards learning dynamics of scanning transmission electron microscopy (STEM) by incorporating physical consistency with phase-field models. The primary idea of this project is to develop machine learning (ML)-based modeling of an interpretable coarse-grained dynamic model utilizing in situ STEM video sequences...
The complexity of photoemission data is rapidly increasing,
as new technological breakthroughs have enabled multidimensional parallel acquisition of multiple observables. Most of the community is currently using heterogeneous data formats and workflows.
We propose a new data format based on NeXus, a hierarchically organized hdf5 structure. The aim is to immediately enable preprocessed data...
Due to their ability to recognize complex patterns, neural networks can drive a paradigm shift in the analysis of materials science data. Here, we introduce ARISE, a crystal-structure identification method based on Bayesian deep learning. As a major step forward, ARISE is robust to structural noise and can treat more than 100 crystal structures, a number that can be extended on demand. While...
I will present our latest progress in using machine learning for solving non-linear solid mechanics. The presentation will be based on our published work here: https://www.nature.com/articles/s41524-021-00571-z
