SOFT-AI-lab Researches (SOFTing)

SOFTing is now placed on the intersection of physics-driven simulations, data-driven machine learning, and disordered (meta)solids, and, ultimately, aims to build an advanced AI-computing platform for (meta)solids modeling and inverse (meta)solids design.

MATERIALS

Glassy Materials, Porous Materials, and Mechanical Metamaterials

RESEARCHES

Direction 1: High-Throughput Materials Modeling Tools

Fusion of physics- and data-driven modeling toward high-throughput predictive tools

Direction 2: Materials’ Inverse Design

Devising inverse design strategies based on optimization and generative models

Direction 3: Materials Database

Big data organization and analysis built upon physics laws

Direction 4: Interpretable Physics of Materials

Unveiling the power of modeling tools to interrogate complex physics laws