CdS/V3O5 for Neuromorphic Computing

Congratulations to Jasleen on her first co-author paper on “An Optoelectronic Heterostructure for Neuromorphic Computing: CdS/V3O5” in Applied Physics Letters! Nonvolatile resistive switching is one of the key phenomena for emerging applications in optoelectronics and neuromorphic computing. However, the stochastic nature of the ion migration can be an impediment for the device robustness and controllability, with uncontrolled variations of high and low resistance states or threshold voltages. In this work, we report an optically induced resistive switching based on a CdS/V3O5 heterostructure. V3O5 is known to have a second order insulator to metal transition around 415 K, with an electrically induced threshold switching at room temperature. Upon illumination, the direct transfer of the photoinduced carriers from the CdS into V3O5 produces a nonvolatile resistive switching at room temperature. Jasleen’s contribution is in using DFT calculations to understand the defects present in V3O5 and the effects of electron doping. We show that electrons (generated by CdS under illumination) injected in V3O5 are trapped in a deep state, slowing the “low” temperature relaxation rate. For the LT phase (T < 340 K), the photoexcited electrons trapped into the oxygen vacancy are unable to overcome the barrier, and therefore, no relaxation is observed. […]

Universal ML model for GB energies

Weike’s swansong project in our group on “A Universal Machine Learning Model for Elemental Grain Boundary Energies” has just been published in Scripta Materialia! The grain boundary (GB) energy has a profound influence on the grain growth and properties of polycrystalline metals. Here, we show that the energy of a GB, normalized by the bulk cohesive energy, can be described purely by four geometric features to within a mean absolute error of 0.13 Jm-2 . More importantly, this universal GB energy model can be extrapolated to the energies of high Σ GBs without loss in accuracy. These results highlight the importance of capturing fundamental scaling physics and domain knowledge in the design of interpretable, extrapolatable machine learning models for ma­terials science. Check out this work here.

Cover art for Chemistry of Materials

Mahdi’s cover art for his recent paper “MxLa1-xSiO2-yNz (M = Ca/Sr/Ba): Elucidating and Tuning the Structure and Eu2+ Local Environments to Develop Full-Visible Spectrum Phosphors” has been selected for the Front Cover of Chemistry of Materials!

Efficient near-infrared phosphors

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.

DRX Li3Nb2O5 Electrode

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.

MxLa1-xSiO2-yNz Full-Visible Spectrum Phosphors

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.

AtomSets – using graph networks as an encoder


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.

Using ML to Discover New Materials


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.

Dislocation mobility in refractory high-entropy alloys

Our collaborative paper with the Ritchie and Asta groups on “Atomistic simulations of dislocation mobility in refractory high-entropy alloys (RHEAs) and the effect of chemical short-range order” has been published in Nature Communications! RHEAs are designed for high elevated-temperature strength, with both edge and screw dislocations playing an important role in plastic deformation. Using the highly accurate machine learning interatomic potential developed by MAVRL alum Dr Yunxing Zuo, we investigate mechanisms underlying the mobilities of screw and edge dislocations in the bcc MoNbTaW RHEA over a wide temperature range using MD simulations, and how these mechanisms are affected by the presence of short range order. We show that the mobility of edge dislocations is enhanced by SRO, while the rate of double-kink nucleation in the motion of screw dislocations is reduced. We also found a cross-slip locking mechanism for the motion of screws, which provides for extra strengthening for bcc RHEAs. Check out this work at this link.

AIMD prediction errors in Lithium Superionic Conductors

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 […]