47 citations · 107 across the 11 of their papers we have counts for
9 papers · 1 filter
Lattice genome: representation and analysis of heterogeneous crystalline microstructures
Jiayang Wang, Mathieu Calvat, J. C. Stinville +1
Inspired by the concept of a generalized materials genome, we introduce the notions of lattice gene and lattice genome for crystalline materials. A lattice gene is a compact repres…
Neural electron backscatter diffraction
I-Tzu Huang, Marat I. Latypov
At the mesoscale, the state of a material is described by continuous fields. In polycrystals, crystallographic orientation and defect content vary continuously within grains, and g…
Thermodynamic assessment of the Ba-La-S and Ga-La-S systems
Jiayang Wang, Guangyu Hu, Pierre Lucas +1
This paper presents the first thermodynamic assessment of binary and pseudo-binary phase diagrams in the Ba--La--S and Ga--La--S systems by means of the CALPHAD method. Experimenta…
Eutectic and peritectic equilibria in coherent binary alloys
Samiah Hassan, Jiayang Wang, Teddy Meissner +2
This work extends the Cahn--Larché thermodynamic framework to binary alloys in which two coherent solid phases coexist with an incoherent liquid and investigates how coherency stra…
SR-CLD: Spatially Resolved Chord Length Distributions for Statistical Description and Visualization of Non-uniform Microstructures
Sheila E. Whitman, Marat I. Latypov
This study introduces the calculation of spatially-resolved chord length distribution (SR-CLD) as an efficient approach for quantifying and visualizing non-uniform microstructures…
FIP-GNN: Graph neural networks for scalable prediction of grain-level fatigue indicator parameters
Gyu-Jang Sim, Myoung-Gyu Lee, Marat I. Latypov
High-cycle fatigue is a critical performance metric of structural alloys for many applications. The high cost, time, and labor involved in experimental fatigue testing call for eff…
AnisoGNN: graph neural networks generalizing to anisotropic properties of polycrystals
Guangyu Hu, Marat I. Latypov
We present AnisoGNNs -- graph neural networks (GNNs) that generalize predictions of anisotropic properties of polycrystals in arbitrary testing directions without the need in exces…
Learning from 2D: machine learning of 3D effective properties of heterogeneous materials based on 2D microstructure sections
Guangyu Hu, Marat I. Latypov
Microstructure--property relationships are key to effective design of structural materials for advanced applications. Advances in computational methods enabled modeling microstruct…
Materials knowledge system for nonlinear composites
Marat I. Latypov, Laszlo S. Toth, Surya R. Kalidindi
In this contribution, we present a new Materials Knowledge System framework for microstructure-sensitive predictions of effective stress--strain responses in composite materials. T…