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Found 5 papers, 2 papers with code
Prediction of the electron density of states for crystalline compounds with Atomistic Line Graph Neural Networks (ALIGNN)
Machine learning (ML) based models have greatly enhanced the traditional materials discovery and design pipeline.
Signal Processing Challenges and Examples for {\it in-situ} Transmission Electron Microscopy
Transmission Electron Microscopy (TEM) is a powerful tool for imaging material structure and characterizing material chemistry.
Recurrent Localization Networks applied to the Lippmann-Schwinger Equation
The bulk of computational approaches for modeling physical systems in materials science derive from either analytical (i. e. physics based) or data-driven (i. e. machine-learning based) origins.
Digital representation and quantification of discrete dislocation networks
Dislocation networks and their evolution are known to control the mechanical properties of metal samples.
Disordered Systems and Neural Networks Materials Science
Microstructure-sensitive mean-field model for nonlinear composite materials
In this contribution, we present a new mean-field model for microstructure-sensitive predictions of effective stress--strain responses in composite materials.
Materials Science
