Marcelo Kuroda
Auburn University, AL·Materials Science
How matter behaves depends on the scale at which it is observed. The motion of individual atoms ultimately shapes how a bulk material performs.
- Fields
Materials Science
Physics
Engineering
- Topics
undergraduate research
multiscale modeling
computational materials science
- Grants
- 1 known grant, through Mar 2029
What they work on
- Engage ten undergraduates per summer in ten-week computational projects that model materials and molecules across length and time scales, from electronic-structure theory to continuum modeling.
- Have student teams address shared questions at different scales, comparing atomistic simulations, continuum models, and machine learning to assess each method's accuracy and limits.
- Train participants in scientific programming, high-performance computing workflows, and validating simulation results, along with communication and professional skills.
More about the research
Technologies from energy systems to additive manufacturing (3D printing) and medicine depend on connecting these scales. Computer simulation is a key way to do that.
Why it matters
Designing better materials for energy, manufacturing, and medicine increasingly depends on accurate computer modeling across atomic to bulk scales. This program trains undergraduates in these modeling and data science methods. It especially reaches students from schools without research computing resources, helping build a skilled computational science workforce.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Michael Falk
Johns Hopkins University, MD·Materials Science
Use machine learning to derive structural order parameters of amorphous metals from electron nanodiffraction and atomistic simulation data, linking measurements made at different scales.
Gül Zerze
University of Houston, TX·Engineering·Early-career
Identify the role of mesoscale clusters, small intermediate molecular groupings, in nonclassical nucleation of liquid-like condensates, using free energy methods to map their stability and find metastable states.
Qi An
Iowa State University, IA·Materials Science
Build a closed-loop AI platform that compares Bayesian optimization (choosing the most informative next test) with deep reinforcement learning (trial-and-reward learning) for designing the high-entropy carbide (Ti-Zr-Hf-Nb-Ta)C, using machine-learning force-field atomistic simulations.
The school
Auburn University: 11 labs on PI Pages, $394M a year on research, 327 research doctorates a year.
Reach out
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