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.

Congratulations to Swastika!

Congratulations to group alumni, Swastika Banerjee, on starting her assistant professorship at Indian Institute of Technology – Roorkee! We wish her all the best in her scientific career!

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.

Associate Editor of ACS Materials Letters

ACS Materials Letters

Prof Ong has been appointed Associate Editor of ACS Materials Letters with effect from Feb 1 2022. ACS Materials Letters publishes high quality and urgent papers on the forefront of fundamental and applied research, at the interface between materials and other disciplines, such as chemistry, engineering and biology. Papers that showcase multidisciplinary and innovative materials research addressing global challenges are especially welcome.

Highly Cited Researcher 2021

Prof Ong has been identified by Clarivate as a Highly Cited Researcher in 2021! Each year, Clarivate identifies the world’s most influential researchers ─ the select few who have been most frequently cited by their peers over the last decade. In 2021, fewer than 6,700, or about 0.1%, of the world’s researchers, in 21 research fields and across multiple fields, have earned this exclusive distinction.

AtomSets – using graph networks as an encoder

AtomSets

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

BOWSR

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.

Electronic Structure 21 Workshop

Prof Ong gave a talk on “Addressing Errors in Ab Initio Molecular Dynamics Predictions through Machine Learning” at the Electronic Structure 21 Workshop held on Jul 14 2021. In this talk, we discuss the errors that AIMD simulations make in predicting the properties of lithium superionic conductors, which can be traced to the small simulation cell sizes, high temperatures and short simulation times. We then demonstrate how machine learning can be used to construct highly accurate interatomic potentials (ML-IAPs) to enable long time scale, large system simulations of complex materials. In particular, a standardized approach to construct ML-IAPs for lithium superionic conductors such as Li7P3S11, Li3YCl6 and Li0.33La0.56TiO3 (LLTO) is shown. All three superionic conductor solid electrolytes exhibit a transition between Arrhenius regimes are relatively low temperatures due to a change in diffusion dimensionality.