(734) 615-8086

Relevant Projects:

NSF, DOE


Liang Qi

Associate Professor

Department of Materials Science and Engineering

My research fields are investigations of the mechanical and chemical properties of materials by applying theoretical and computational tools, including first-principles calculations, atomistic simulations, multiscale modeling, and how to apply machine learning to overcome the size/time scale limitations that widely exist in these computational studies. As shown in the following picture, a recent example of my research is to construct the physics-informed machine learning surrogate model to quickly predict material parameters related to their strength and ductility, otherwise, these parameters can only obtain from costly first-principles calculations and/or atomistic simulations.

Surrogate models based on physical descriptors to predict the planar fault energies of multicomponent alloys in order to quickly estimate the strength and ductility of these alloys in a large compositional space (Acta Materialia, 210 (2021) 116800).