What can machine learning help with microstructure‑informed materials modeling and design?

Boosting Coercivity of 3D Printed Hard Magnets through Nano-Modification of the Powder Feedstock

Unraveling impacts of polycrystalline microstructures on ionic conductivity of ceramic electrolytes by computational homogenization and machine learning

Phase-field ductile fracture simulations of thermal cracking in additive manufacturing

Elasto-plastic residual stress analysis of selective sintered porous materials based on 3D-multilayer thermo-structural phase-field simulations

A robust finite strain isogeometric solid-beam element

Thermal Conductivity Analysis of Polymer-Derived Nanocomposite via Image-Based Structure Reconstruction, Computational Homogenization, and Machine Learning

Cohesive phase-field chemo-mechanical simulations of inter- and trans- granular fractures in polycrystalline NMC cathodes via image-based 3D reconstruction

Towards enhancing ODS composites in laser powder bed fusion: Investigating the incorporation of laser-generated zirconia nanoparticles in a model iron–chromium alloy

Data-driven inverse design of composite triangular lattice structures