Predicting Crystal Volumes

Iek-Heng Chu’s paper on “Predicting the Volumes of Crystals” has been published in Computational Materials Science. In this collaborative work with the Hacking Materials group, we developed two schemes for predicting crystal volumes. Accurate crystal volume estimates are immensely useful for further experimental analysis, or to generate initial guesses for electronic structure optimizations. The volume prediction algorithms are implemented in the open-source pymatgen software.

First-order Interfacial Transformations in GB

Hui Zheng is a co-author on a recently published article in Physical Review Letters on “First-Order Interfacial Transformations with a Critical Point: Breaking the Symmetry at a Symmetric Tilt Grain Boundary”. A collaboration with the Luo group, this work examines symmetry breaking in the ∑5 (210) GB using a modified genetic algorithm with Monte Carlo and molecular dynamics simulations. Read more about this work here.

Sr2LiAlO4 – A novel earth-abundant phosphor with excellent color quality

Our paper on “Mining Unexplored Chemistries for Phosphors for High-Color-Quality White-Light-Emitting Diodes” has been published in Joule. Using supercomputers and data mining, we identified Sr2LiAlO4, the first known Sr-Li-Al-O quaternary crystal, as a highly promising phosphor material in low-cost, high-color-quality white LEDs. Eu2+ and Ce3+-activated Sr2LiAlO4 phosphors exhibit broad green-yellow and blue emissions, respectively, with excellent thermal quenching resistance of > 88% intensity at 150oC. A prototype phosphor-converted white LED utilizing Sr2LiAlO4-based phosphors yields an excellent color rendering index exceeding 90. This work is a collaboration between the Materials Virtual Lab (UCSD), McKittrick group (UCSD) and Im group (Chonnam University). The lead authors are Zhenbin Wang, Jungmin Ha and Yoon Hwa Kim. More information about this work can be found in the Jacobs School of Engineering News as well as Science Daily,, etc.


Meet the newest member of our group, Pythia@Mavrl. Named after the famed oracle of antiquity, Pythia is a GPU-based deep learning machine from Lambda Labs. Our lab will be utilizing Pythia to develop cutting edge models for materials property prediction and discovery.