Abstract Machine learning plays an important role in accelerating the discovery and design
process for novel electrochemical energy storage materials. This review aims to provide the …

With an emphasis on the development of machine learning-based constitutive modeling
approaches, the state of constitutive modeling techniques and applications for metals and …

Abstract Machine learning (ML) methods are commonly used for pattern recognition in
almost any field one could imagine. ML techniques can also offer a substantial improvement …

Recently, the design of grid composites with superior mechanical properties has gained
significant attention as a testbed for deep neural network (DNN)-based optimization …

Crystal plasticity theory is often employed to predict the mesoscopic states of polycrystalline
metals, and is well-known to be costly to simulate. Using a neural network with convolutional …

Crystal plasticity finite element models (CPFEM) have shown tremendous potential for
simulating the microstructure evolution paths in polycrystalline aggregates subjected to …

Reduced-order models of process-structure evolution linkages play a central role in the
discovery and development of new/improved materials and their deployment in advanced …

This prospective offers a new Bayesian framework that could guide the systematic
application of the emerging toolsets of machine learning in the efforts to address two of the …

In this work, an uncertainty propagation study is performed based on simulated ensembles
of statistical volume elements (SVE) used to inform a reduced order internal state variable …

Crystal plasticity finite element simulations provide physics-based predictions of the plastic
response in polycrystalline metals subjected to large plastic strains. Despite their …