We are proud co-authors of an article in Nature Energy on “Ultrafast ion transport at a cathode–electrolyte interface and its strong dependence on salt solvation”. In this work, Bohua Wen from Prof Yet-Ming Chiang’s group at MIT performed electrodynamic measurements on single electrode particles to show that Li intercalation into NMC333 cathodes is primarily impeded by interfacial kinetics when using a conventional LiPF6 salt. Electrolytes containing LiTFSI salt, with or without LiPF6, exhibit about 100-fold higher exchange current density. Zhi Deng from the Materials Virtual Lab carried out MD simulations to show that TFSI preferentially solvates Li+ compared to PF6-, while still yielding a lower Li+ binding energy, explaining the observed ultrafast interfacial kinetics. Check out this work at this link.
Xiangguo’s excellent paper on “Complex strengthening mechanisms in the NbMoTaW multi-principal element alloy” has just been published on npj Computational Materials. Refractory multi-principal element alloys (MPEAs) have exceptional mechanical properties, including high strength-to-weight ratio and fracture toughness, at high temperatures. Here we elucidate the complex interplay between segregation, short-range order, and strengthening in the NbMoTaW MPEA using a machine learning interatomic potential. We show that the single crystal MPEA exhibit greatly reduced anisotropy in the critically resolved shear stress between screw and edge dislocations compared to the elemental metals. In the polycrystalline MPEA, we demonstrate that thermodynamically driven Nb segregation to the grain boundaries (GBs) and W enrichment within the grains intensiﬁes the observed short-range order (SRO). The increased GB stability due to Nb enrichment reduces the von Mises strain, resulting in higher strength than a random solid solution MPEA. These results highlight the need to simultaneously tune GB composition and bulk SRO to tailor the mechanical properties of MPEAs. Check out this work at this link.
Chen Zheng’s and Chi Chen’s paper on “Random Forest Models for Accurate Identiﬁcation of Coordination Environments from X-Ray Absorption Near-Edge Structure” has just been published in the second issue of the Cell Press journal Patterns! Analyzing coordination environments using X-ray absorption spectroscopy has broad applications in solid-state physics and material chemistry. Here, we show that random forest models trained on 190,000 K-edge XANES can directly identify the main atomic coordination environment with a high accuracy of 85.4%. More importantly, we show that the random forest models can be used to predict coordination environments from experimental K-edge XANES with minimal loss in accuracy. Check out the work here.
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.