The UNSW Materials and Manufacturing Futures Institute (MMFI) is organising a series of free online lectures/workshops for postgraduate students and research associates who are affected by the limited access to laboratories and research facilities. These free online lectures/workshops include “Machine Learning” (ML) and “Density Functional Theory” (DFT) and will provide individualised support to assist postgraduate students and research associates with their research and assist with the implementation of new computational approaches. It will also give them the opportunity to expand their research capability and provide them with additional research skills that will enhance their research outputs.
Halide perovskites have received a surge in research interests as promising photovoltaic materials with up to ~30 % of photocurrent conversion efficiency. However, these materials also possess low structural stabilities, which can be attributed to the presence of anharmonic phonon vibrational modes. Modifying the chemical bonding in halide perovskites via compositional engineering is often employed to further stabilise these materials, but it can also enhance the soft-mode vibrations. This type of vibration can strongly couple to the excited electron-hole pairs in the material, causing light-induced ion segregations (photo-instabilities), counter-acting the enhancement in chemical stabilities. In this talk, Dr Jack Yang will discuss the suite of computational tools developed in his research group, that allows us to correlate the electronic dynamics with the chemical and structural stabilities of mixed halide perovskites. This understanding in material property is critical for guiding the collaborative research efforts in MMFI for improving the performance of perovskite photovoltaic materials.
Dr Jianliang(Jack) Yang Profile
Dr Jianliang(Jack) Yang is 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.
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