ABSTRACT Nickel‐Zinc (Ni‐Zn) batteries offer an interesting alternative for the expanding
electrochemical energy storage industry due to their high‐power density, low cost, and …

The development of highly accurate constitutive models for materials that undergo path-
dependent processes continues to be a complex challenge in computational solid …

We present graph neural networks (GNNs) as an efficient and accurate machine learning
approach to predict mechanical properties of polycrystalline materials. Here, a GNN was …

A graph convolutional network (GCN) is employed in the deep energy method (DEM) model
to solve the momentum balance equation in three‐dimensional space for the deformation of …

Over the past decade, fundamental changes in artificial intelligence (AI)—from foundational
to applied—have delivered dramatic insights across a wide breadth of US Department of …

We propose a neural network framework to preclude the need to define or observe
incompletely or inaccurately defined states of a material in order to describe its response …

Fracture is one of the main causes of failure in engineering structures. Phase field methods
coupled with adaptive mesh refinement (AMR) techniques have been widely used to model …

Many material response functions depend strongly on microstructure, such as
inhomogeneities in phase or orientation. Homogenization presents the task of predicting the …

Control volume analysis models physics via the exchange of generalized fluxes between
subdomains. We introduce a scientific machine learning framework adopting a partition of …

Simulating the mechanical response of advanced materials can be done more accurately
using concurrent multiscale models than with single-scale simulations. However, the …