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DECODE Materials Lab

Defect Engineering via Computational Design of Materials

University of Toledo

PI: Pulkit Garg

About the Lab

Defects behave very differently than perfect crystalline material and often control key material properties. The DECODE Materials Lab uses multiscale computational methods, including density functional theory (DFT), molecular dynamics (MD), and phase-field dislocation dynamics (PFDD), integrated with AI/ML-driven design, to understand and engineer defects across length scales. We use these tools to uncover new structure-property relationships in multi-principal element alloys, nanocrystalline metals, and superconducting materials, accelerating the development of next-generation materials for aerospace, hypersonic systems, energy, defense and national security, and quantum technologies.

Multiscale Modeling Methods

DECODE multiscale modeling approach: DFT to MD to PFDD

Our research spans quantum to continuum length scales, enabling a comprehensive mechanistic understanding of material behavior from individual atoms to engineering components.

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Multi-Principal Element Alloy Design

Temperature-dependent dislocation glide mechanisms in RMPEAs

MPEAs offer exceptional combinations of thermal stability, mechanical performance, and functional properties inaccessible to conventional alloys.

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Nanocrystalline Alloys for Extreme Environments

Enhanced strength and ductility via amorphous grain boundaries in nanocrystalline alloys

Nanocrystalline metals offer superior strength but suffer from thermal instability driven by high-energy grain boundaries.

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