Developing a neural network model for predictive modeling of many-body interactions will help simulate collective dynamics of cancer cells.
Houlong Zhuang is an assistant professor in aerospace and mechanical engineering in the School for Engineering of Matter, Transport and Energy at Arizona State University. His research experience has led to more than 70 peer-reviewed papers, with publications in journals such as Physical Review Materials, Physical Review Applied, Chemistry of Materials, and Advanced Science.
Total projects: 6
Utilizing Density Functional Theory to Compute the Efficacy of a Practical Material for Industrial Direct Air Capture via a Copper Metal Organic Framework
Computational modeling of a specific atomic structure that is efficient at capturing CO2 from the air can help fight climate change.
Studying patterns in 2D materials can lead to discovering new useful material properties for technologies such as batteries.
Using machine learning to explore phase selection rules will help discover novel metal alloys with useful properties.