In the CM³ Lab, we advance physics-based and artificial-intelligence-based approaches from subatomic to macro scale and apply them to understanding processing-structure-property relations in chemically and structurally complex materials.

For the processing-structure relation, we use the discrete element method for powder dynamics (using FLOW-3D AM), thermal-fluid model for melt pool (using FLOW-3D AM), and CALPHAD-informed phase field model for solidification (using Thermo-Calc and MOOSE).

For the structure-property relation, we mainly work with two multiscale modeling frameworks. The first framework involves concurrent multiscale modeling based on a coarse-grained atomistic method (using PyCAC). The second framework concerns sequential multiscale modeling, which links density functional theory (using VASP), atomistic simulation method (using LAMMPS), phase-field model (using PFDD), and crystal plasticity model (using ρ-CP).

We establish processing-structure-property relationships in materials using machine learning (ML), with a focus on neural networks and Gaussian processes.

We develop ML-based interatomic potentials for chemically complex materials.

We also fine-tune generative large language models for domain-specific applications in STEM.

We utilize multi-objective optimization approaches for forward and inverse design of materials.