Lecturer
Dr Jack Yang
2010 Doctor of Philosophy, School of Material Science and Engineering, UNSW
2010 Graduate Certificate in Research Management and Commercialization, School of Biotechnology and Biomolecular Science, UNSW
2007 Bachelor of Science (Nanotechnology, Honor Class 1), School of Material Science and Engineering, UNSW
Science
School of Materials Science & Engineering
Jan 2019 – Present Lecturer, School of Material Science and Engineering & MMFI, UNSW
Jul 2017 – Dec 2018 Research Fellow, School of Material Science and Engineering & MMFI, UNSW
Jul 2017 – Dec 2019 Postdoctoral Research Fellow, Australian Nuclear Science and Technology Organization (ANSTO)
Oct 2013 – Jun 2017 Postdoctoral Research Fellow (with Prof. Graeme M. Day), School of Chemistry, University of Southampton, United Kingdom
May 2011 – Oct 2013 Postdoctoral Scientist (with Dr. Mark P. Waller), Organic Chemistry Institute, Westfälische Wilhelms-Universität Münster, Germany
E-mail
- Publications
- Media
- Grants
- Awards
- Research Activities
- Engagement
- Teaching and Supervision
2021 UNSW Science Faculty Research Grant, Solid Catalysts for Solarthermal Ammonia Synthesis – Exploring the Uncharted Territory of Quaternary Oxides ($4,000)
2020 UNSW MMFI Seed Funding, Solid Catalysts for Solarthermal Ammonia Synthesis – Exploring the Uncharted Territory of Ternary Oxides ($20,000)
2020 Postgraduate Council Research Supervisor Award (UNSW)
I am a computational chemist interested in the structural-property relationships across a diverse range of materials, including organic crystals and inorganic perovskites. He is particularly interested in the electron-phonon interactions in these systems and looking at how their electronic, thermal, as well as catalytic properties are affected by their structures. In his research group, they aim to shift the paradigm of serendipity-driven research to an informed approach. By applying high-throughput simulations on supercomputers to screening through thousands of materials, they are able to find out candidates with desirable physical/chemical properties before going to make and test them in the labs. To further discover intriguing information behind results obtained from high-throughput simulations, we develop and apply new machine-learning methods to explore the structural-property relationships in complex materials.
My Teaching
MATS3006 Computational Modelling