Electrochemically induced amorphous-to-rock-salt phase transformation in niobium oxide electrode for Li-ion batteries.

Intercalation-type metal oxides are promising negative electrode materials for safe rechargeable lithium-ion batteries due to the reduced risk of Li plating at low voltages. Nevertheless, their lower energy and power density along with cycling instability remain bottlenecks for their implementation, especially for fast-charging applications. Here, we report a nanostructured rock-salt Nb2O5 electrode formed through an amorphous-to-crystalline transformation during repeated electrochemical cycling with Li+. This electrode can reversibly cycle three lithiums per Nb2O5, corresponding to a capacity of 269 mAh g-1 at 20 mA g-1, and retains a capacity of 191 mAh g-1 at a high rate of 1 A g-1. It exhibits superb cycling stability with a capacity of 225 mAh g-1 at 200 mA g-1 for 400 cycles, and a Coulombic efficiency of 99.93%. We attribute the enhanced performance to the cubic rock-salt framework, which promotes low-energy migration paths. Our work suggests that inducing crystallization of amorphous nanomaterials through electrochemical cycling is a promising avenue for creating unconventional high-performance metal oxide electrode materials.

Performance and Cost Assessment of Machine Learning Interatomic Potentials.

Machine learning of the quantitative relationship between local environment descriptors and the potential energy surface of a system of atoms has emerged as a new frontier in the development of interatomic potentials (IAPs). Here, we present a comprehensive evaluation of machine learning IAPs (ML-IAPs) based on four local environment descriptors-atom-centered symmetry functions (ACSF), smooth overlap of atomic positions (SOAP), the spectral neighbor analysis potential (SNAP) bispectrum components, and moment tensors-using a diverse data set generated using high-throughput density functional theory (DFT) calculations. The data set comprising bcc (Li, Mo) and fcc (Cu, Ni) metals and diamond group IV semiconductors (Si, Ge) is chosen to span a range of crystal structures and bonding. All descriptors studied show excellent performance in predicting energies and forces far surpassing that of classical IAPs, as well as predicting properties such as elastic constants and phonon dispersion curves. We observe a general trade-off between accuracy and the degrees of freedom of each model and, consequently, computational cost. We will discuss these trade-offs in the context of model selection for molecular dynamics and other applications.

Atomic-scale origin of the low grain-boundary resistance in perovskite solid electrolyte Li0.375Sr0.4375Ta0.75Zr0.25O3.

Oxide solid electrolytes (OSEs) have the potential to achieve improved safety and energy density for lithium-ion batteries, but their high grain-boundary (GB) resistance generally is a bottleneck. In the well-studied perovskite oxide solid electrolyte, Li3xLa2/3-xTiO3 (LLTO), the ionic conductivity of grain boundaries is about three orders of magnitude lower than that of the bulk. In contrast, the related Li0.375Sr0.4375Ta0.75Zr0.25O3 (LSTZ0.75) perovskite exhibits low grain boundary resistance for reasons yet unknown. Here, we use aberration-corrected scanning transmission electron microscopy and spectroscopy, along with an active learning moment tensor potential, to reveal the atomic scale structure and composition of LSTZ0.75 grain boundaries. Vibrational electron energy loss spectroscopy is applied for the first time to reveal atomically resolved vibrations at grain boundaries of LSTZ0.75 and to characterize the otherwise unmeasurable Li distribution therein. We find that Li depletion, which is a major reason for the low grain boundary ionic conductivity of LLTO, is absent for the grain boundaries of LSTZ0.75. Instead, the low grain boundary resistivity of LSTZ0.75 is attributed to the formation of a nanoscale defective cubic perovskite interfacial structure that contained abundant vacancies. Our study provides new insights into the atomic scale mechanisms of low grain boundary resistivity.

Learning properties of ordered and disordered materials from multi-fidelity data.

Predicting the properties of a material from the arrangement of its atoms is a fundamental goal in materials science. While machine learning has emerged in recent years as a new paradigm to provide rapid predictions of materials properties, their practical utility is limited by the scarcity of high-fidelity data. Here, we develop multi-fidelity graph networks as a universal approach to achieve accurate predictions of materials properties with small data sizes. As a proof of concept, we show that the inclusion of low-fidelity Perdew-Burke-Ernzerhof band gaps greatly enhances the resolution of latent structural features in materials graphs, leading to a 22-45% decrease in the mean absolute errors of experimental band gap predictions. We further demonstrate that learned elemental embeddings in materials graph networks provide a natural approach to model disorder in materials, addressing a fundamental gap in the computational prediction of materials properties.

Atomistic simulations of dislocation mobility in refractory high-entropy alloys and the effect of chemical short-range order.

Refractory high-entropy alloys (RHEAs) are designed for high elevated-temperature strength, with both edge and screw dislocations playing an important role for plastic deformation. However, they can also display a significant energetic driving force for chemical short-range ordering (SRO). Here, we investigate mechanisms underlying the mobilities of screw and edge dislocations in the body-centered cubic MoNbTaW RHEA over a wide temperature range using extensive molecular dynamics simulations based on a highly-accurate machine-learning interatomic potential. Further, we specifically evaluate how these mechanisms are affected by the presence of SRO. The mobility of edge dislocations is found to be enhanced by the presence of SRO, whereas the rate of double-kink nucleation in the motion of screw dislocations is reduced, although this influence of SRO appears to be attenuated at increasing temperature. Independent of the presence of SRO, a cross-slip locking mechanism is observed for the motion of screws, which provides for extra strengthening for refractory high-entropy alloy system.

Conductive Binder for Si Anode with Boosted Charge Transfer Capability via n-Type Doping.

Employing conductive binders in silicon (Si) anode has been considered as a fundamental solution to the pulverization of Si particles. Therefore, it is still a great challenge to improve the charge transfer capability of the conductive binder. Herein, a copolymer (PFPQ-COONa) is synthesized, characterized, and electrochemically tested as conductive binder for Si anode. It is found that PFPQ-COONa exhibits not only excellent cycling stability, but also satisfactory rate performance with relatively high areal loading, which outperforms currently reported single-component conductive binders. The superior electrochemical performance can be attributed to the molecular-level contact between binder and Si particles and to the enhanced intrinsic conductivity of PFPQ-COONa at reductive potential. This method provides a fresh perspective to design and develop conductive binder for high-capacity battery anode.

How to Optimize the Interface between Photosensitizers and TiO2 Nanocrystals with Molecular Engineering to Enhance Performances of Dye-Sensitized Solar Cells?

In this work, the interfacial properties of a series of metal-free organic naphthodithienothiophene (NDTT)-based photosensitizers adsorbed on TiO2 surfaces were investigated by a combination of ab initio calculations and experimental measurements. The calculations and experiments reveal that because of the efficient charge transfer from the adsorbed dyes to TiO2 nanocrystal surface there is an upward shift for the energy levels of dyes and a downward shift for the conduction band of surface TiO2 and that the band gaps for both of them are also reduced. Such electronic level alignments at the interface would lead to increased light absorption range by adsorbed dyes and increased driving force for charge injection but reduced open-circuit potential (V(oc)). More interestingly, we found that molecule engineering of the donor group and introducing additional electron-withdrawing unit have little effect on the electronic level alignments at the interface (because band gaps of the dyes adsorbed on TiO2 surfaces become approximately identical when compared with those of the dyes measured in solution) but that they can affect the steric effect and the charge separation at the interface to tune V(oc) and the short-circuit current density (J(sc)) effectively. All these findings suggest that optimizing the interfacial properties of dyes adsorbed on TiO2 surfaces by synchronously modifying steric effects of dye molecules anchored on TiO2 and charge-transfer and separation properties at the interfaces is important to construct efficient dye-sensitized solar cells.