Metal-Insulator Transition in V2O3 with Intrinsic Defects


Richard’s paper on “Metal-Insulator Transition in V2O3 with Intrinsic Defects” has just been published in Physical Review B! V2O3 is a material of potential interest for neuromorphic computing, i.e., computers that mimic biological brains and have the potential to be far more efficient than traditional von Neumann architectures. A potential implementation utilizes metal insulator transitions (MITs) to implement “leaky, integrate, and fire” to emulate short-term memory. V2O3, which undergo a metal-insulator transition (MIT) at 165K, can be used to implement al for such devices as they exhibit a sudden collapse of insulating behavior under an external stimuli, and they can gradually recover their insulating state over time in the absence of the stimuli. This behavior is known as volatile resistive switching. Here, we show that the PBE + U functional provides the best compromise between accuracy and efficiency in calculating the properties related to the MIT between low-temperature and high-temperature V2O3. We use this functional to explore the various influences that intrinsic point defects will have on the MIT in V2O3. This work is a collaboration with the Schuller group at UCSD as part of the Quantum Materials for Energy Efficient Neuromorphic Computing (QMEEN-C) center, an Energy Frontier Research Center […]

Stable Cathode-Na3-xY1-xZrxCl6 Composite for High Voltage All-Solid-State Na-ion Batteries

Our collaborative work with the Meng (UCSD) and Clement (UCSB) groups on the discovery of the Na3-xY1-xZrxCl6 (NYZC) ion conductor has just been published in Nature Communications. While rechargeable solid-state sodium-ion batteries (SSSBs) promise to bring about safer and more energy-dense energy storage, the poor interfacial stability between existing solid electrolytes and typical oxide cathodes has limited their long-term cycling performance and practicality. Using DFT calculations and MD simulations with a machine learning interatomic potential, Swastika Banerjee and Ji Qi from the Materials Virtual Lab identified NYZC as a promising new ion conductor that is both electrochemically stable up to 3.8 V vs. Na/Na+ and chemically compatible with oxide cathodes. NYZC’s ionic conductivity of 6.6 × 10−5 S/cm at ambient temperature, several orders of magnitude higher than oxide coatings, is due to abundant Na vacancies and cooperative MCl6 rotation. A SSSB comprising a NaCrO2 + NYZC composite cathode, Na3PS4 electrolyte, and Na-Sn anode exhibits an exceptional first-cycle Coulombic efficiency of 97.1% at room temperature and can cycle over 1000 cycles with 89.3% capacity retention at 40 °C. Check out our article at this link.

“Liquid-like” Li sublattice in Mixed-Halide Argyrodites Li6-xPS5-xClBrx

Our collaborative work with Prof Hu’s group at Florida State University on “Tunable Lithium-Ion Transport in Mixed-Halide Argyrodites Li6-xPS5-xClBrx: An Unusual Compositional Space” has been published in Chemistry of Materials. In this work, we report a new compositional space of argyrodite superionic conductors, Li6−xPS5−xClBrx [0 ≤ x ≤ 0.8]. In particular, Li5.3PS4.3ClBr0.7 has a remarkably high ionic conductivity of 24 mS/cm at 25 °C and an extremely low lithium migration barrier of 0.155 eV that makes it highly promising for low-temperature operation. Using NMR and DFT calculations (performed by Swastika Banerjee from the Materials Virtual Lab), we show that bromination leads to co-occupancy of Cl-, Br- , and S2- at 4a/4d sites eventually resulting in a “liquid-like” Li-sublattice with a flattened energy landscape when x approaches 0.7.

Chi’s Talk on Constructing Accurate Quantitative Structure-Property Relationships via Materials Graph Networks

Chi Chen gave a talk at nanoHub’s Hands-on Data Science and Machine Learning Training Series on how to develop MatErials Graph Network (MEGNet) models for predicting various materials properties from crystal structure. He also demonstrates how the MEGNet framework can be adapted to work with multi-fidelity data sources to improve predictions on high-value small datasets (e.g., experimental data). Extensive examples are shown using Jupyter notebooks. The video is available on the Materials Virtual Lab Youtube Channel. The megnet package used extensively in these tutorials can be found on Github.