Phase-field method (PFM) has become a mainstream computational method for predicting
the evolution of nano and mesoscopic microstructures and properties during materials …

The chemical space for designing materials is practically infinite. This makes disruptive
progress by traditional physics-based modeling alone challenging. Yet, training data for …

Phase-field (PF) modeling is a versatile physics-based computational method that has been
used to simulate the evolution of microstructures. The PF method can produce accurate …

Dielectric capacitors, characterized by ultra-high power densities, have been widely used in
Internet of Everything terminals and vigorously developed to improve their energy storage …

High-entropy alloys (HEAs) represent a promising class of materials with exceptional
structural and functional properties. However, their design and optimization pose challenges …

Prediction of microstructure evolution during material processing is essential to control the
material properties. Simulation tools for microstructure evolution prediction based on …

Phase-field modeling offers a powerful tool for investigating the electrical control of the
domain structure in ferroelectrics. However, its broad application is constrained by …

In this article, a thermodynamics-based data-driven approach utilizing machine learning is
proposed to accelerate multiscale phase-field simulations. To obtain training data, the …

The response of materials to shock loading is important to planetary science, aerospace
engineering, and energetic materials. Thermally activated processes, including chemical …

Computational modeling of heterogeneous materials is increasingly relying on multiscale
simulations which typically leverage the homogenization theory for scale coupling. Such …