Xingyu’s second paper on “Design Principles for Aqueous Na-Ion Battery Cathodes” has just been published in Chemistry of Materials! In this work, we develop design rules for aqueous sodium-ion battery cathodes through a comprehensive DFT study of known cathode materials. We identified five promising aqueous sodium-ion battery cathode materials – NASICON-Na3Fe2(PO4)3, Na2FePO4F, Na3FeCO3PO4, alluadite-Na2Fe3(PO4)3, and Na3MnCO3PO4 – with high voltage, good capacity, high stability in aqueous environments, and facile Na-ion migration. Check out this work at this link!
Mahdi’s paper on “Predicting Thermal Quenching in Inorganic Phosphors” has just been published in Chemistry of Materials! Phosphors are used in energy efficient light emitting diodes (LEDs). A key performance metric of a phosphor is its thermal quenching (TQ), which is the percentage loss of emission at elevated temperatures during operation. In this work, we develop a new approach to predicting TQ in phosphors using ab initio molecular dynamics (AIMD) simulations and the band gap of the phosphor host. We demonstrate for the ﬁrst time that TQ under the crossover mechanism is related to the local environment stability of the activator. Further, by accounting for the eﬀect of the crystal ﬁeld on the thermal ionization barrier, we show that a uniﬁed model can predict the experimental TQ in 29 known phosphors to within a root-mean-square error of ∼3.1−7.6%. Check out the work at this link.
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.