Category: Publications

Efficient near-infrared (NIR) LEDs are used in many applications, including medical diagnostics, food detection, security monitoring, and machine vision. In this collaborative work with the group of Prof Rong-jun Xie at Xiamen University published in Matter, Mahdi Amachraa developed descriptors of the Eu(II)-host interactions to predict the 5d-to-4f energy gap with a RMSE of 7.0 nm. By incorporating this predictor into a high-throughput screening of 223 nitride materials in the Inorganic Crystal Structure Database, we identified and experimentally validated (Sr,Ba)3Li4Si2N6:Eu(II) with NIR emissions of 800- 830 nm and high quantum efficiencies (QEs) of 30%-40%. This NIR emitter has 3x more power than prevailing NIR emitters. We demonstrate that the ultralong emission wavelength and high QE stem from a coordinated energy transfer and an optimized electronic delocalization around Eu(II). Check out this work here.

Our collaborative work with the group of Prof Claire Xiong on “Electrochemically induced amorphous-to-rock-salt phase transformation in niobium oxide electrode for Li-ion batteries” has been published in Nature Materials! In this work, we report a nanostructured rock-salt Nb2O5 electrode formed through an amorphous-to-crystalline transformation during repeated electrochemical cycling with Li+. This electrode can reversibly cycle three lithiums per Nb2O5, corresponding to a capacity of 269 mAh/g at 20 mA/g, and retains a capacity of 191 mAh/g at a high rate of 1 A/g. The main contribution from Yunxing Zuo of the Materials Virtual Lab is using DFT computations to show that the cubic rock-salt framework promotes the percolation of low-energy migration paths. We also develop a computable metric to identify other transition metal oxides with a likelihood of rock-salt formation. Our work suggests that inducing crystallization of amorphous nanomaterials through electrochemical cycling is a promising avenue for creating unconventional high-performance metal oxide electrode materials. Check out the publication here.

Mahdi’s collaborative work with the group of Prof Rong-jun Xie on “MxLa1−xSiO2−yNz (M = Ca/Sr/Ba): Elucidating and Tuning the Structure and Eu2+ Local Environments to Develop Full-Visible Spectrum Phosphors” has just been published in Chemistry of Materials! The local environments of rare-earth activators have profound effects on the luminescent properties of phosphors. Here, we elucidate the crystal structure of the LaSiO2N phosphor host using a combination of density functional theory calculations and synchrotron Xray diffraction. We determine that LaSiO2N crystallizes in the monoclinic C2/c instead of the hexagonal P6̅c2 space group. To improve the luminescence performance, divalent cations M (M = Ca/Sr/Ba) were introduced into LaSiO2N to eliminate Eu3+. A family of apatite M1+xLa4−xSi3O13−x/2:Eu2+ (x ∼ 1.5, M = Ca/Sr/Ba) phosphors was further developed with unprecedented ultra-broadband (290 nm) emission spectra and excellent thermal stability. Detailed local environment investigations reveal that the formation of oxygen vacancies within and beyond the first shell environment of Eu2+ is responsible for the redshift and broadening of the emission spectra via geometrical alteration of the Eu2+ local environment. This work provides new insights for understanding and optimizing the luminescence of rare-earth phosphors. Check out this work here.

Graph networks are an extremely powerful deep learning tool for predicting materials properties. However, a critical weakness is their reliance on large quantities of training data. In this work published in npj Computational Materials, Dr Chi Chen shows that pre-trained MEGNet formation energy models can be effectively used as “encoders” for crystals in what we call the AtomSets framework. The compositional and structural descriptors extracted from graph network deep learning models, combined with standard artificial neural network models, can achieve lower errors than the graph network models at small data limits and other non-deep-learning models at large data limits. AtomSets also transfer better in a simulated materials discovery process where the targeted materials have property values out of the training data limits, require minimal domain knowledge inputs and are free from feature engineering. Check out this work here.

Our paper on “Accelerating materials discovery with Bayesian optimization and graph deep learning” has just been published in Materials Today! In our group, we are firm advocates of ML models that utilize structure-based features, because only such models can reliably predict property differences between chemically similar but structurally different materials (e.g., diamond vs graphite). However, a bottleneck remains in that obtaining an input structure today still depends on expensive DFT calculations. Here, we show that Bayesian optimization with an accurate MEGNet energy model can be used to obtain sufficiently good input structures for ML model predictions. We demonstrated the power of this approach by screening 400,000 materials for ultra-incompressibility. Two completely novel materials are realized experimentally by Mingde Qin in Prof Jian Luo’s group at UCSD. This work paves the way to ML-accelerated discovery of new materials with exceptional properties. Check out this work here.

Congratulations to Ji Qi on his paper on “Bridging the gap between simulated and experimental ionic conductivities in lithium superionic conductors” published in Materials Today Physics. Part of a “Special Issue on Solid state batteries: materials, characterizations, and understandings”, this is Ji’s first first-author paper. Lithium superionic conductors (LSCs) are of major importance as solid electrolytes for next-generation all-solid-state lithium-ion batteries. However, ab initio molecular dynamics (AIMD) often make wrong predictions of their ionic conductivities due to the short time scales and small cell sizes used. Here, we present a strategy to bridge this gap using machine learning interatomic potentials based on the moment tensor potential (MTP) formalism. We show that the DFT functional used to train the MTPs plays a critical role in the accuracy of the predictions. In particular, the van der Waals optB88 functional yield much more accurate lattice parameters, which in turn leads to accurate prediction of ionic conductivities and activation energies for Li0.33La0.56TiO3, Li3YCl6 and Li7P3S11. Nanosecond NPT MD simulations also reveal that all three lithium superionic conductors undergo a transition between two quasi-linear Arrhenius regimes at relatively low temperatures. This transition can be traced to an increase in the number and diversity of diffusion […]

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.

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.

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 […]