Machine Learning for Materials Discovery
Our materials informatics research applies data-driven ML approaches to accelerate the discovery and design of polymers and lattices. By integrating molecular simulation with specialized databases, we develop predictive tools that identify promising material candidates for targeted applications, especially for thermal applications, significantly reducing the time and cost of traditional trial-and-error experimentation. Students will be required to collect, clean and process the data with graduate students and perform ML tasks based on the data.
Joining the Tengfei Luo Lab offers a unique opportunity to work at the cutting edge of materials informatics and machine learning. Students will gain hands-on experience with state-of-the-art computational tools and real research datasets, collaborating closely with graduate students and faculty. This experience is ideal for undergraduates interested in data science, materials science, or engineering who want to build practical skills that are highly valued in both academia and industry.