Category: Publications

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.

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 […]

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.

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.

Our collaborative work with the Hu group @ Florida State University on functional defects in the Cl-doped Na3PS4 solid electrolyte has just been published in Advanced Functional Materials! Through tuning of Cl and Na vacancies in Cl-doped Na3PS4, we show that conductivities as high as 1.96 mS/cm can be achieved, with excellent full-cell performance in Na/Na3.0PS3.8Cl0.2/Na3V2(PO4)3 with a reversible capacity of 100 mAh/g at room temperature.

Chi has recently developed MatErials Graph Networks (MEGNet) based on DeepMind’s graph networks approach.We show that MEGNet models are a universal approach to machine learning for both crystals and molecules, outperforming prior ML models on a broad array of properties. We also demonstrate the incorporation of state (e.g., temperature, pressure) into MEGNet models, and how transfer learning can be used to accelerate and improve the accuracy of models trained on smaller data sets. A preprint of our paper is published on arXiv and the models are available at https://github.com/materialsvirtuallab/megnet. We have also posted a useful data set of 69,000 crystals from the Materials Project on figshare for ML purposes.

Our work (Wang, Z.; Ha, J.; Kim, Y. H.; Im, W. Bin; McKittrick, J.; Ong, S. P. Mining Unexplored Chemistries for Phosphors for High-Color-Quality White-Light-Emitting Diodes. Joule 2018, 2 (5), 914–926 DOI: 10.1016/j.joule.2018.01.015) on discovery of phosphors using DFT calculations has been featured in ACS Chemical & Engineering News! Follow this link to read about it.