Multiscale Modeling of Materials LabSan Diego State UniversityResearch
Current projects connect atomistic simulation, microstructure characterization, field-assisted processing, and data-driven materials design.
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Data-driven models can help connect processing conditions, microstructure descriptors, and material response.
The lab uses machine-learning ideas to accelerate multiscale modeling and identify interpretable links among simulated microstructures, processing routes, and properties.
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Nanosecond electrical pulses may offer a route to manipulate grain boundaries and design stronger materials.
This project studies whether short, intense electrical pulses can alter grain-boundary structures and open new processing pathways for advanced materials. The work combines multiphysics modeling, nanoscale mechanism hypotheses, and microstructure evidence.
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Field-assisted ceramic processing can accelerate densification and reveal new coupling among current, heat, defects, and microstructure.
The lab studies 8 mol% yttria-stabilized zirconia and related ceramic systems to understand how electric fields, temperature, and microstructure interact during rapid sintering.
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Microscopy, diffraction, and three-dimensional reconstruction provide evidence for processing-structure-property relationships.
Experimental characterization grounds the lab's modeling work, helping the group compare mechanisms against microstructure, deformation, and processing evidence.
Computation connects electronic, atomic, microstructural, and component scales for materials design.
The M3 Lab develops and applies modeling workflows across length scales, using simulation to interpret mechanisms and connect materials structure to performance.