Category: Publications

We are proud co-authors of an article published in Materials Today on “Genetic algorithm-guided deep learning of grain boundary diagrams”. Grain boundaries (GBs) often control the processing and properties of polycrystalline materials. In this work, we combine isobaric semi-grand canonical ensemble hybrid Monte Carlo and molecular dynamics (hybrid MC/MD) simulations with a genetic algorithm and deep neural networks (DNN) to predict complexion diagrams. The DNN prediction (work by Yunxing Zuo of MAVRL) is ~108 faster than atomistic simulations, enabling the construction of the property diagrams for millions of distinctly different GBs of five DOFs. Excellent prediction accuracies have been achieved for not only symmetric-tilt and twist GBs, but also asymmetric-tilt and mixed tilt-twist GBs. The data-driven prediction of GB properties as function of temperature, bulk composition, and five crystallographic DOFs (i.e., in a 7D space) opens a new paradigm.

Our article on “Rechargeable Alkali-Ion Battery Materials: Theory and Computation” has been published in Chemical Reviews! Since its development in the 1970s, the rechargeable alkali-ion battery has proven to be a truly transformative technology, providing portable energy storage for devices ranging from small portable electronics to sizable electric vehicles. Written in collaboration with Van der Ven group, we present a review of modern theoretical and computational approaches to the study and design of rechargeable alkali-ion battery materials, starting from fundamental thermodynamics and kinetics phenomenological equations to their relationships to key computable battery properties. We also critically the literature applying these techniques to yield crucial insights into battery operation and performance and provide perspectives on outstanding challenges and opportunities in the theory and computation of rechargeable alkali-ion battery materials. Check it out at this link.

Our critical review of the application of machine learning (ML) in Energy Materials led by Chi Chen is now out in Advanced Energy Materials. With its ability to solve complex tasks autonomously, ML is being exploited as a radically new way to help find material correlations, understand materials chemistry, and accelerate the discovery of materials. In this work, we provide a conceptual framework for ML in materials science, with a broad overview of different ML techniques as well as best practices. This is followed by a critical discussion of how ML is applied in energy materials, including rechargeable alkali-ion batteries, photovoltaics, catalysts, thermoelectrics, piezoelectrics, and superconductors. We conclude the work with our perspectives on major challenges and opportunities in this exciting field. Check out the work here.

Our work on “Performance and Cost Assessment of Machine Learning Interatomic Potentials (ML-IAPs)” has been published in the Journal of Physical Chemistry A! Co-authored with the developers of four leading ML-IAPs, this work provides a rigorous assessment of ML-IAPs across several metrics – accuracy in energies and forces, materials properties and training and computing cost. This assessment was carried out using a diverse data set – bcc (Li, Mo) and fcc (Cu, Ni) metals and diamond group IV semiconductors (Si, Ge) – generated using high-throughput density functional theory (DFT) calculations. To facilitate the reuse and reproduction of our results, the code, data and optimized ML models in this work are published open-source on our mlearn Github repo. The code includes high-level Python interfaces for ML-IAPs development as well as LAMMPS material properties calculators. Check out the publication at this link.

We are pleased to announce the release of the Grain Boundary Database (GBDB) together with the associated publication in Acta Materialia! The GBDB is the largest database of DFT-computed grain boundary properties to date, encompassing 327 GBs of 58 elemental metals. To construct the GBDB, we developed a novel scaled-structural template approach for GB calculations, which reduces the computational cost of converging GB structures by a factor of ~ 3–6. The grain boundary energies and work of separation have been rigorously validated against previous experimental and computational data. You can check out the GBDB at Crystalium@Materials Virtual Lab or the Materials Project.

Our collaborative paper with the group of Zhiguo Xia in South China University of Technology on “Engineering of K3YSi2O7 To Tune Photoluminescence with Selected Activators and Site Occupancy” has been published in Chemistry of Materials. We have discovered the Eu- and Ce-activated K3YSi2O7 phosphors, which exhibit orange-red and green emission, respectively. Using DFT calculations, we show that Eu2+ occupies both K1 and Y2 crystallographic sites, while Ce3+ and Eu3+ only occupy the Y2 site. Hence, the broad-band red emission of Eu2+ are attributed to a small DFT band gap (3.69 eV) of K3YSi2O7 host and a selective occupancy of Eu2+ in a highly distorted K1 site and a high crystal field splitting around Y2 sites.

The Materials Virtual Lab is pleased to announce two newly minted PhD graduates – Dr Zhuoying Zhu and Dr Chen Zheng! Congratulations on their successful PhD thesis defense! Both Zhuoying and Chen joined the group in 2014. They have taken radically different, but equally fruitful research paths over the course of their PhD career. Zhuoying’s thesis work is on superionic conductors solid electrolytes for all-solid-state rechargeable alkali-ion batteries. Using computational methods, she has successfully predicted completely novel lithium and sodium superionic conductors with superior ionic conductivity and electrochemical stability, several of which have already been realized experimentally. Her work showcases how first principles computations, combined with the application of thermodynamics and kinetics, can help accelerate materials optimization and discovery for technological applications. Chen’s thesis is focused on the relatively nascent field of machine learning (ML) in materials science. He has developed ML models that can interpret X-ray absorption spectra with accuracies exceeding that of humans, as well as applied graph-based deep learning techniques developed in our group to accelerate the exploration of vast compositional spaces for cathode materials.

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.

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 […]