Research | DECODE Materials Lab

The DECODE Materials Lab pursues three interrelated research thrusts centered on defect-informed computational design of advanced materials.

Thrust 1

Multiscale Modeling Methods

Our research spans quantum to continuum length scales, enabling a comprehensive mechanistic understanding of material behavior from individual atoms to engineering components. We integrate and extend these methods to tackle problems at the forefront of materials science.

We employ DFT + MD + PFDD to reveal how interstitial impurities (O, H, C) and chemical disorder govern temperature-dependent dislocation dynamics in refractory MPEAs:enabling design of alloys for aerospace, hypersonic, and nuclear energy applications.

DFT (VASP) Molecular Dynamics (LAMMPS) Phase-Field Dislocation Dynamics Kinetic Monte Carlo Dislocation Dynamics BCC Alloys

Applications: Aerospace · Hypersonic vehicles · Nuclear energy · Quantum technologies · Energy storage

DECODE multiscale modeling approach: DFT to MD to PFDD — Fig. 1: The DECODE multiscale modeling framework integrating quantum (DFT), atomistic (MC/MD), and mesoscale (PFDD) methods to study and design MPEAs for high-temperature structural applications.

Thrust 2

Multifunctional Multi-Principal Element Alloys

MPEAs offer exceptional combinations of thermal stability, mechanical performance, and functional properties inaccessible to conventional alloys. We develop a multi-fidelity Bayesian optimization framework combining large, low-cost MD datasets with high-accuracy DFT calculations to efficiently navigate the vast compositional landscape and predict new alloys with tailored properties.

AI / ML Machine Learning Bayesian Optimization DFT

Multi-fidelity predictive model workflow for accelerated MPEA discovery — Fig. 2: Workflow of a multi-fidelity predictive model combining low-cost MD and high-accuracy DFT datasets via Bayesian optimization to accelerate discovery of MPEAs with optimized properties.

Thrust 3

Nanocrystalline Alloys for Extreme Environments

Nanocrystalline metals offer superior strength but suffer from thermal instability driven by high-energy grain boundaries. We use hybrid Monte Carlo / MD simulations to investigate how multiple solutes (Zr, Ta, Ag, Nb) co-segregate to boundaries and induce amorphous grain boundary complexion transitions that simultaneously enhance strength, ductility, and chemical stability.

This work provides design rules for thermally stable nanocrystalline alloys capable of retaining exceptional properties under extreme mechanical and thermal conditions.

Grain Boundary Complexions Monte Carlo / MD Segregation Nanocrystalline Metals Extreme Environments

Enhanced strength and ductility via amorphous grain boundaries in nanocrystalline alloys — Fig. 3: Enhanced strength and ductility in multicomponent nanocrystalline alloys through solute segregation-driven amorphous grain boundary formation under extreme conditions.