Zhenbin Wang
Assistant Professor at City University of Hong Kong
Kowloon, Hong Kong, China
Summary
🤩
Rockstar🎓
Top SchoolZhenbin Wang is an Assistant Professor and computational materials scientist with 11 years of experience at the intersection of physics, chemistry, and data science, focused on accelerating discovery of technologically relevant materials. He holds a PhD in Materials Science from UC San Diego and has transitioned predictions to practice—several of his data-driven predicted materials have been synthesized and shown excellent device performance. His work spans academia and open-source: he contributed backend enhancements to the widely used pymatgen library, improving VASP parsing for elastic tensors and optical properties to strengthen materials analysis workflows. Trained at top institutions in China and Denmark as well as the US, he combines rigorous computational modeling with experimental and software tooling expertise to bridge theory and validation.
11 years of coding experience
4 years of employment as a software developer
University of California San DiegoMaster of Engineering - MEng, Materials Science, Master of Engineering - MEng, Materials Science at University of Science and Technology of ChinaBachelor of Engineering - BE, Electronic Packaging Technology, Bachelor of Engineering - BE, Electronic Packaging Technology at Harbin Institute of Technology
English, Chinese
Github Skills (7)
science10
python10
materials-informatics10
numpy9
unit-testing8
testing8
unit-test8
Programming languages (3)
Github contributions (5)
materialsproject/pymatgen
Feb 2015 - Dec 2017
Python Materials Genomics (pymatgen) is a robust materials analysis code that defines classes for structures and molecules with support for many electronic structure codes. It powers the Materials Project.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:16 commits, 7 PRs, 6 pushes in 2 years 10 months
Contributions summary:Zhenbin primarily contributed to the `pymatgen` library by adding and modifying features related to the VASP output reader. Their work focused on parsing and incorporating elastic tensor data from VASP calculations, enhancing the library's ability to analyze material properties. They also improved the codebase by adding checks, implementing a tag to evaluate the existence of a tensor matrix, and added unit tests to validate the new functionality. Furthermore, the user addressed a bug and added optical absorption coefficient method.
moleculespythonscienceelectronic-structurepowers
zbwang/nano266
Apr 2015 - Mar 2023
Contributions:4 pushes in 8 years
quantum-computingmechanicalnanoquantummodelling
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