Wenhao Sun

Assistant Professor

whsun@umich.edu

2006 H.H. Dow

T: (734) 763-2296

Bio



EDUCATION:

BS—Materials Science and Engineering—Northwestern University (2010)
BS—Engineering Sciences and Applied Mathematics—Northwestern University (2010)
PhD—Materials Science and Engineering—Massachusetts Institute of Technology (2016)

CURRENTLY TEACHING:


RESEARCH INTERESTS:

We are interested in resolving outstanding fundamental scientific problems that impede the computational materials design process. Our group uses high-throughput density functional theory, applied thermodynamics, and materials informatics to deepen our fundamental understanding of synthesis-structure-property relationships, while exploring new chemical spaces for functional technological materials. These research interests are driven by the practical goal of the U.S. Materials Genome Initiative to accelerate materials discovery, but whose resolution requires basic fundamental research in synthesis science, inorganic chemistry, and materials thermodynamics. 
Predictive materials synthesis
Our primary research focus is on developing new quantitative and predictive theories of inorganic materials synthesis. This effort was born out of a realization that the computational materials discovery pipeline is no longer bottlenecked by the identification of promising new materials, but rather, by the difficulty of synthesizing predicted compounds in the laboratory. We derive theoretical frameworks to predict non-equilibrium crystallization pathways, helping solid-state chemists navigate through the thermodynamic and kinetic energy landscape towards the synthesis of novel target materials. 
Mapping structural and chemical relationships across broad materials spaces
In the modern age of data science, there is more catalogued and query-able materials data than ever before. We employ high-throughput computational materials discovery techniques to survey uncharted chemical spaces for novel synthesizable materials, constructing large stability maps to help guide exploratory synthesis. Using data-mining and machine-learning algorithms, we aim to explain the complex interplay between chemistry, composition, and electronic structure in governing large-scale stability trends across broad materials spaces.

We have multiple open positions available for graduate students and postdocs. Please email  for more details. 


PRIOR EXPERIENCE:

Postdoc—Materials Science Division—Lawrence Berkeley National Laboratory (2016-2018)
Staff Scientist—Materials Science Division—Lawrence Berkeley National Laboratory (2019)