252 citations · 462 across the 3 of their papers we have counts for
3 papers
cond-mat.mtrl-sci2026
Extracting Atomic Environments for Machine Learning Interatomic Potentials
Jared C. Stimac, Fei Zhou, Kyle Bushick +4
In order to appropriately capture large-scale material features and emergent phenomena via atomistic simulations, such as Molecular Dynamics (MD), the system scale can range up to…
cs.CV2020★ 252 cited
Overview: Computer vision and machine learning for microstructural characterization and analysis
Elizabeth A. Holm, Ryan Cohn, Nan Gao +4
The characterization and analysis of microstructure is the foundation of microstructural science, connecting the materials structure to its composition, process history, and proper…
cs.CV2018★ 210 cited
High throughput quantitative metallography for complex microstructures using deep learning: A case study in ultrahigh carbon steel
Brian L. DeCost, Bo Lei, Toby Francis +1
We apply a deep convolutional neural network segmentation model to enable novel automated microstructure segmentation applications for complex microstructures typically evaluated m…