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, Phys.org, etc.

Pythia

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.

Scialog: Advanced Energy Storage Team Award

Prof Ong’s team is one of six teams selected for the Scialog Advanced Energy Storage Team Awards by the Research Corporation (Rescorp) for Science Advancement. This project is a collaboration with Prof Scott Warren of University of North Carolina at Chapel Hill and Prof Zhenxing Feng of Oregon State University to develop high-voltage dual-ion batteries. More information can be found in the Rescorp press release.

Zr strengthening of MoSi2

Hui Zheng’s first paper on “Role of Zr in strengthening MoSi2 from density functional theory calculations” has just been published in Acta Materialia. MoSi2 is an important intermetallic with excellent oxidation resistance at high temperatures. However, “pesting” by oxygen limits its application at intermediate temperatures. Using DFT calculations, we show that Zr nanoparticles act as a getter for oxygen, and in the process, significantly strengthens MoSi2 interfaces and grain boundaries. We also use the Materials Project to efficiently screen for other potential getter elements using simple thermodynamic descriptors, a general approach that can be extended to other alloy systems of interest.

Probing Interfacial Reactions in All-solid-state Na-ion Batteries

Hanmei’s first paper on “Probing Solid-Solid Interfacial Reactions in All-Solid-State Sodium-ion Batteries with First Principles Calculations” has just been published in Chemistry of Materials. In this comprehensive work, we show how explicit AIMD models can lead to different predictions of interfacial reaction products from simple thermodynamic approximations. Specifically, SO4 formation is predicted to be favored over PO4 formation at the interface between NaCoO2 and Na3PS4. We also identified several promising new candidates for buffer materials that potentially show lower reactivity with common electrodes and solid electrolytes.

Halide migration in Hybrid Organic-Inorganic Perovskites

Zhuoying Zhu is a proud co-author on a recent publication titled “Direct Observation of Halide Migration and its Effect on the Photoluminescence of Methylammonium Lead Bromide Perovskite Single Crystals” published in Advanced Materials. In this collaborative effort with the Fenning group@UCSD, halide ion migration and its corresponding effect on photoluminescence are observed in relation to changes in the applied electric field in  single crystals of methylammonium lead bromide (CH3NH3PbBr3). Higher local Br concentrations is shown to be correlated with superior optoelectronic performance. Nudged elastic band calculations (Zhuoying’s contribution) show that lower barriers for vacancy migration in directions that contains a component along the C-N alignment axis, in good agreement with the experimental observations.

Accurate Machine-learned Potential for Molybdenum

Chi’s paper on “Accurate force field for molybdenum by machine learning large materials data” has just been published in Physical Review Materials. This work addresses a crucial gap in the available force field for Mo. We will show that by fitting to the energies, forces, and stress tensors of a large DFT dataset on a diverse set of Mo structures, a Mo Spectral Neighbor Analysis Potential (SNAP) model can be developed that achieves close to DFT accuracy in the prediction of a broad range of properties, including elastic constants, melting point, phonon spectra, surface energies, grain boundary energies, etc. Examples and parameters of the new potential can be obtained at our Github page.