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.

VOPO4 polymorphs as Li/Na-ion battery cathodes

Paul’s paper titled “Comparison of the polymorphs of VOPO4 as multi-electron cathodes for rechargeable alkali-ion batteries” has just been published in Journal of Materials Chemistry A. In collaboration with the Whittingham group, we performed a systematic first principles investigation, supported by careful electrochemical characterization and published experimental data, of the relative thermodynamic stability, voltage, band gap, and diffusion kinetics for alkali intercalation into the β, ε and αI polymorphs of VOPO4, a highly promising family of multi-electron cathodes.  We identify the β polymorph as the most promising for Li insertion, and the αI polymorph as  the most promising for Na insertion. We show that differences in the voltage, kinetics and rate capability of these different polymorphs for Li and Na insertion can be traced back to their fundamentally different VO6/VO5–PO4 frameworks.

Atomate

Hanmei Tang and Iek-Heng Chu are co-authors on “Atomate: A High-Level Interface to Generate, Execute, and Analyze Computational Materials Science Workflows” just published in Computational Materials Science. This paper describes atomate, an open-source Python framework for computational materials science simulation, analysis, and design with an emphasis on automation and extensibility, that is built on top of pymatgen, FireWorks, and custodian. The Materials Virtual Lab are proud contributors to this great open science initiative! Check out the atomate package here.

Effect of transition metal mixing in Layered P2 Oxides

Our work on “Effects of Transition-Metal Mixing on Na Ordering and Kinetics in Layered P2 Oxides” has just been published in Physical Review Applied. In this work by Chen Zheng and other co-authors, we systematically investigate the effects of transition-metal (TM) mixing on Na ordering and kinetics in the NaxCo1−yMnyO2 model system using DFT calculations. We show that the TM composition at the Na(1) (face-sharing) site has a strong influence on the Na site energies, which in turn impacts the kinetics of Na diffusion towards the end of the charge. By employing a site-percolation model, we establish theoretical upper and lower bounds for TM concentrations based on their effect on Na(1) site energies, providing a framework to rationally tune mixed-TM compositions for optimal Na diffusion.