In a collaboration with the Xie group from Xiamen, our group is pleased to announce yet another novel phosphor discovered via first-principles computations. Sr2AlSi2O6N:Eu2+ has a superbroad emission with a bandwidth of 230 nm, the broadest emission bandwidth ever reported, and has excellent thermal quenching resistance (88% intensity at 150°C). A prototype white LED utilizing only this full-visible-spectrum phosphor exhibits superior color quality (Ra = 97, R9 = 91), outperforming commercial tricolor phosphor-converted LEDs. This work is published in Chemistry of Materials and is co-authored by Shuxing Li of the Xie group with Materials Virtual Lab alumnus Zhenbin Wang.
Xingyu’s first paper titled “Water Contributes to Higher Energy Density and Cycling Stability of Prussian Blue Analogue Cathodes for Aqueous Sodium-Ion Batteries” is now published in Chemistry of Materials! In this work, we show that dry Prussian blue analogues (PBAs), one of the most promising cathode materials for aqueous sodium-ion batteries for large-scale energy-storage systems, generally undergo a phase transition from a rhombohedral Na2PR(CN)6 to a tetragonal/cubic PR(CN)6 during Na extraction. However, the presence of water fundamentally alters this phsae behavior, increasing an increase in the average voltage and a reduction in volume change during electrochemical cycling, resulting in both higher energy density and better cycling stability. We also identiﬁed four new promising PBA compositions, Na2CoMn(CN)6, Na2NiMn(CN)6, Na2CuMn(CN)6 and Na2ZnMn(CN)6 for further exploration.
Our group alumnus Paul Lin is part of a collaboration that won the DOE’s Energy Frontier Research Center’s “Ten at Ten” award! This award is in recognition of the NorthEast Center for Chemical Energy Storage highly successful efforts at enabling a fully rechargeable multi-electron battery cathode (see our joint publications on VOPO4). Our collaborators in this work are Carrie Siu (Whittingham group), Ieuan Seymour (Grey group) and Jatin Rana (Piper group).
Zhi Deng is the lead author in our recently published work in npj Computational Materials on a machine-learned (ML) electrostatic Spectral Neighbor Analysis Potential (eSNAP) for Li3N, a prototypical superionic conductor. By incorporating long-ranged electrostatics, we developed a highly accurate eSNAP model for Li3N that far outperforms traditional potentials in the prediction of energies, forces and properties such as lattice constants, elastic constants, and phonon dispersion curves. Most importantly, we demonstrate that the eSNAP enables long-time, large-scale Li diffusion studies in Li3N, computing the Haven ratio and simulating GB diffusion in Li3N for the first time to excellent agreement with experimental values.
Our group members are also co-authors in several recently published works.
- Group alumnus Zhenbin Wang co-authored “Color Tunable Single-Phase Eu2+ and Ce3+ Co-Activated Sr2LiAlO4 Phosphors” published in Journal of Materials Chemistry C, a work that builds on the Sr2LiAlO4 phosphor previously discovered by our group using data mining and DFT computations to show that co-doping of Eu2+ and Ce3+ can be used to tune the color of the Sr2LiAlO4 phosphor.
- Zhuoying co-authored a work on “Elucidating the Limit of Li Insertion into the Spinel Li4Ti5O12” published in ACS Materials Letters. Our contribution is using DFT computations to identify the structures and voltage profile of LixTi5O12 when lithiated to Li8Ti5O12.
- Yiming and Hanmei are coauthors on a paper on “2DMatPedia, an Open Computational Database of Two-Dimensional Materials from Top-down and Bottom-up Approaches” published in Scientific Data.
Congratulations to Mahdi and Manas for passing their Master Thesis Defense!
We are pleased to announce that Richard’s follow-up work on the anisotropic work functions of the elements has been published in Surface Science. The work function is a fundamental electronic property of a solid that varies with the facets of a crystalline surface. It is a crucial parameter in spectroscopy as well as materials design, especially for technologies such as thermionic electron guns and Schottky barriers. In this work, we present the largest database of calculated work functions for elemental crystals to date. This well-validated database contains the anisotropic work functions of more than 100 polymorphs of about 72 elements. One significant advance is the development of an improved model for the work function of metals from atomic parameters such as the electronegativity and metallic radius based on Gauss’ law.
The work function database can be accessed at the Crystalium website together with other surface properties.
The Materials Virtual Lab is proud to announce the launch of crystals.ai, a website of curated models, software and datasets for AI in materials science. Here, you will find web applications implementing on-the-fly prediction of properties using our MEGNet and other models, open-source software frameworks for building your own AI models, as well as curated datasets for reproducible materials AI research.
Our paper on MatErials Graph Networks (MEGNet) for machine learning in crystals and molecules have been published in Chemistry of Materials. The article is available here. Key advances include the incorporation of state variables such as temperature, pressure and entropy, transfer learning from models with large data (e.g., formation energies) to models with smaller data (e.g., elastic constants) and extraction of chemical trends from learned elemental embeddings. These advances address key limitations in ML in materials science, such as data size limitations and physical interpretability.
We have also released all our codes and data in our open Github repo at https://github.com/materialsvirtuallab/megnet to enable others to reproduce and improve on our models.
Prof Ong is the Feature Editor in Mar 2019’s MRS Energy Quarterly article on “Artificial intelligence is aiding the search for energy materials”. In this article written by Prachi Patel, we interview various leaders in the field on their perspectives on how AI is being applied in energy materials design, from discovering entirely novel materials to enabling large-scale complex simulations to providing insights into how to synthesize materials. Check it out at https://doi.org/10.1557/mrs.2019.51.
Paul’s collaborative paper on “Rational Synthesis and Electrochemical Performance of LiVOPO4 Polymorphs” as part of the NorthEast Center for Chemical Energy Storage has just been published in Journal of Materials Chemistry A. Here, we have carried out a comprehensive experimental and DFT study of the α-I, β and ε polymorphs of LiVOPO4 and demonstrated how selectivity for each polymorph can be tuned through manipulating the precursor and oxidation environment. Further, we discuss how synthesis conditions may be used to improve the rate performance of β-LiVOPO4.