Publications

L-edge XANES database

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Yiming’s paper on “Database of ab initio L-edge X-ray absorption near edge structure” has just been published in Nature Scientific Data! This work is a collaboration between the Materials Virtual Lab, the Materials Project, Alan Dozier and the groups of Prof John Rehr at the University of Washington and Prof Jordi Cabana at the University of Illinois Chicago. It is a follow-up to our earlier work on a K-edge XANES database, the L-edge XANES database provides instant access to more than 140,000 L-edge spectra for more than 22,000 structures generated using a high-throughput FEFF9 workflow. The L-edge XANES is widely used in the characterization of transition metal compounds. The data is available through the Materials Project XAS app and addresses a critical need for L-edge XANES spectra among the research community. The journal article is available at this link.

Publications

Halide Migration in Lead Halide Perovskites

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Manas just published his first paper on “Correlated Octahedral Rotation and Organic Cation Reorientation Assist Halide Ion Migration in Lead Halide Perovskites” in Chemistry of Materials! Halide ion migration is one of the main contributors to instability and hysteresis in lead halide perovskite (LHP) solar cells. In this collaborative work with the Fenning group, we elucidate the effect of octahedral rotation and organic cation rotation on halide ion migration in APbBr3 (A = Cs or methylammonium/MA) LHPs. While both effects lower halide migration barriers, organic cation rotation plays a much bigger role in hybrid organic-inorganic LHPs, which can be linked to changes in H bonding during the halide migration process. We suggest that “locking” the organic cation via chemical and processing means can help mitigate halide migration-induced instability and reduced hysteresis in LHP solar cells.

Check out the work at this link.

Presentations

Bridging computational and experimental predictions in materials machine learning models

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Prof Ong gave a talk on bridging computational and experimental predictions in materials machine learning models at the CNLS Virtual Workshop Machine Learning in Chemical and Materials Sciences held on May 12 2021. This talk discusses the sources of ML prediction errors, namely model errors and data errors, and demonstrate how these errors can be mitigated using appropriate techniques. For example, multi-fidelity/multi-task models can help small data models learn from larger, less accurate data models, while choosing an appropriate DFT functional for computing energies and forces for ML interatomic potentials can significantly improve the agreement with experimental measurements. You can jump to the relevant chapter of interest below!

Presentations

GTC2021

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Prof Ong gave a talk on “Discovering New Materials in a Fraction of the Time with Graph Networks” at the NVIDIA GTC 2021 conference. This talk discusses our recent work on using GPU-trained multi-fidelity graph networks, together with Bayseian optimization techniques, to discover novel materials.

Publications

Metal-Insulator Transition in V2O3 with Intrinsic Defects

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V2O3 MIT

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 funded by the Department of Energy.

Publications

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

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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.

Publications

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

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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.

Presentations

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

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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.

Presentations

Yunxing’s Workshop Talk on Machine Learning Interatomic Potential Development with MAML

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Yunxing gave a talk at NanoHUB’s Hands-on Data Science and Machine Learning Training Series today on how to conveniently develop machine learning interatomic potentials (ML-IAPs) using the Materials Machine Learning (maml) library. ML-IAPs describe the potential energy surface using local environment descriptors and has been demonstrated to be able to achieve near-DFT accuracy with linear scaling with respect to the number of atoms. The recording of this talk is now available on the Materials Virtual Lab’s Youtube channel.

To find out more about the maml package, check out our Github repository. You can also read Yunxing’s excellent paper benchmarking the performance and cost of various ML-IAPs to learn more.