Speaker
Description
The development of new materials and manufacturing processes is increasingly driven by advances in Artificial Intelligence (AI). In materials science, evaluating the properties of candidate designs often relies on computationally intensive simulations, which can become impractical when exploring vast design spaces. Machine learning offers a powerful alternative: surrogate models can approximate material properties with significantly reduced computational cost compared to traditional simulations. By leveraging mature machine learning workflows developed in computer science, materials researchers can accelerate discovery and optimization processes.
In this talk, I will present the application of Bayesian optimization across a diverse set of materials science collaborations, including alloy design, laser-induced graphene synthesis, and additive manufacturing of both polymers and metals. While each application domain expert supervision, they can be addressed with similar machine learning frameworks. I will also introduce a user-friendly, web-based interface that enables researchers without programming expertise to deploy and experiment with advanced machine learning models, democratizing access to these powerful tools.
Bio
Dr. Patrick Johnson recently joined the Materials Science and Engineering Department at Iowa State University after 17 years in the Chemical and Biomedical Engineering Department at the University of Wyoming. He has a research program that focuses on nanoscale materials, with a focus on advanced carbon species. Research topics include tuning these materials for applications in surface enhanced Raman spectroscopy (SERS) biosensors, additive manufacturing, and next generation batteries.
Patrick received his BS in Chemical Engineering from Lehigh University in 1992 and his Master’s in Biomedical Engineering from the University of Virginia in 1994. He then worked on projects for environmental remediation in Belarus followed by work on sensors for detection of biological and chemical weapons. He then received his PhD in Chemical Engineering from Columbia University in 2005.
