Rapid Mining of Fast Ion Conductors via Subgraph Isomorphism Matching.

The rapidly evolving field of inorganic solid-state electrolytes (ISSEs) has been driven in recent years by advances in data-mining techniques, which facilitates the high-throughput computational screening for candidate materials in the databases. The key to the mining process is the selection of critical features that underline the similarity of a material to an existing ISSE. Unfortunately, this selection is generally subjective and frequently under debate. Here we propose a subgraph isomorphism matching method that allows an objective evaluation of the similarity between two compounds according to the topology of the local atomic environment. The matching algorithm has been applied to discover four structure types that are highly analogous to the LiTi2(PO4)3 NASICON prototype. We demonstrate that the local atomic environments similar to LiTi2(PO4)3 endow these four structures with favorable Li diffusion tunnels and ionic conductivity on par with those of the prototype. By further taking into account the electronic structure and electrochemical stability window, 13 compounds are identified to be potential ISSEs. Our findings not only offer a promising approach toward rapid mining of fast ion conductors without limitation in the compositional range but also reveal insights into the design of ISSEs according to the topology of their framework structures.

Graph-based discovery and analysis of atomic-scale one-dimensional materials.

Recent decades have witnessed an exponential growth in the discovery of low-dimensional materials (LDMs), benefiting from our unprecedented capabilities in characterizing their structure and chemistry with the aid of advanced computational techniques. Recently, the success of two-dimensional compounds has encouraged extensive research into one-dimensional (1D) atomic chains. Here, we present a methodology for topological classification of structural blocks in bulk crystals based on graph theory, leading to the identification of exfoliable 1D atomic chains and their categorization into a variety of chemical families. A subtle interplay is revealed between the prototypical 1D structural motifs and their chemical space. Leveraging the structure graphs, we elucidate the self-passivation mechanism of 1D compounds imparted by lone electron pairs, and reveal the dependence of the electronic band gap on the cationic percolation network formed by connections between structure units. This graph-theory-based formalism could serve as a source of stimuli for the future design of LDMs.

Co13O8-metalloxocubes: a new class of perovskite-like neutral clusters with cubic aromaticity.

Exploring stable clusters to understand structural evolution from atoms to macroscopic matter and to construct new materials is interesting yet challenging in chemistry. Utilizing our newly developed deep-ultraviolet laser ionization mass spectrometry technique, here we observe the reactions of neutral cobalt clusters with oxygen and find a very stable cluster species of Co13O8 that dominates the mass distribution in the presence of a large flow rate of oxygen gas. The results of global-minimum structural search reveal a unique cubic structure and distinctive stability of the neutral Co13O8 cluster that forms a new class of metal oxides that we named as 'metalloxocubes'. Thermodynamics and kinetics calculations illustrate the structural evolution from icosahedral Co13 to the metalloxocube Co13O8 with decreased energy, enhanced stability and aromaticity. This class of neutral oxygen-passivated metal clusters may be an ideal candidate for genetic materials because of the cubic nature of the building blocks and the stability due to cubic aromaticity.

Direct Visualization of Large-Scale Intrinsic Atomic Lattice Structure and Its Collective Anisotropy in Air-Sensitive Monolayer 1T'- WTe2.

Probing large-scale intrinsic structure of air-sensitive 2D materials with atomic resolution is so far challenging due to their rapid oxidization and contamination. Here, by keeping the whole experiment including growth, transfer, and characterizations in an interconnected atmosphere-control environment, the large-scale intact lattice structure of air-sensitive monolayer 1T'-WTe2 is directly visualized by atom-resolved scanning transmission electron microscopy. Benefit from the large-scale atomic mapping, collective lattice distortions are further unveiled due to the presence of anisotropic rippling, which propagates perpendicular to only one of the preferential lattice planes in the same WTe2 monolayer. Such anisotropic lattice rippling modulates the intrinsic point defect (Te vacancy) distribution, in which they aggregate at the constrictive inner side of the undulating structure, presumably due to the ripple-induced asymmetric strain as elaborated by density functional theory. The results pave the way for atomic characterizations and defect engineering of air-sensitive 2D layered materials.

Degeneration of Key Structural Components Resulting in Ageing of Supercapacitors and the Related Chemical Ageing Mechanism.

The research on supercapacitors (SCs) is one of the hot topics in the field of energy storage, and the intrinsic ageing mechanism of SCs is significant from both the economic and the scientific point of view. In this paper, the negative effects of decay of the key structural components on ageing of SCs were investigated by factorial design and analysis of variance (ANOVA). The ANOVA results showed that the degree of the negative influence on ageing of SCs could be ranked in descending order as anode > separator > cathode. The ageing would be accelerated due to the interaction between the electrode and separator, especially at a high charge-discharge current density. Further, the intrinsic chemical ageing mechanism of SCs was revealed by the morphology, microstructure, and chemical composition analyses of the fresh and aged key components (the electrode carbon materials, current collectors, and separators) with scanning electron microscopy (SEM), Brunauer-Emmett-Teller (BET), X-ray photoelectron spectra (XPS), time-of-flight secondary ion mass spectrometry (TOF-SIMS), wide-angle X-ray diffraction (WAXD), differential scanning calorimetry (DSC), Fourier transform infrared (FTIR), etc. Moreover, the minimum pore width of electrode carbon materials suitable for electrolyte ion diffusion was obtained by density functional theory (DFT) calculations, which corroborated the assumption that the pore structure deterioration was one of the direct causes of capacitance loss for aged SCs. Generally, the ageing mechanism of key components of SCs could be a reference to develop advanced electrode materials and separators for SCs.

Structural origin of the high-voltage instability of lithium cobalt oxide.

Layered lithium cobalt oxide (LiCoO2, LCO) is the most successful commercial cathode material in lithium-ion batteries. However, its notable structural instability at potentials higher than 4.35 V (versus Li/Li+) constitutes the major barrier to accessing its theoretical capacity of 274 mAh g-1. Although a few high-voltage LCO (H-LCO) materials have been discovered and commercialized, the structural origin of their stability has remained difficult to identify. Here, using a three-dimensional continuous rotation electron diffraction method assisted by auxiliary high-resolution transmission electron microscopy, we investigate the structural differences at the atomistic level between two commercial LCO materials: a normal LCO (N-LCO) and a H-LCO. These powerful tools reveal that the curvature of the cobalt oxide layers occurring near the surface dictates the structural stability of the material at high potentials and, in turn, the electrochemical performances. Backed up by theoretical calculations, this atomistic understanding of the structure-performance relationship for layered LCO materials provides useful guidelines for future design of new cathode materials with superior structural stability at high voltages.

Strong influence of strain gradient on lithium diffusion: flexo-diffusion effect.

Lithium ion batteries (LIBs) work under a sophisticated external force field and the electrochemical properties could be modulated by strain. Owing to electro-mechanical coupling, the change of micro local structures can greatly affect the lithium (Li) diffusion rate in solid state electrolytes and the electrode materials of LIBs. In this study, we found, through first-principles calculations, that the strain gradient in bilayer graphene (BLG) significantly affects the Li diffusion barrier, which is termed as the flexo-diffusion effect. The Li diffusion barrier substantially decreases/increases under a positive/negative strain gradient, leading to a change of Li diffusion coefficient of several orders of magnitude at 300 K. Interestingly, the regulation effect of strain gradient is much more significant than that of a uniform strain field, which can have a remarkable effect on the rate performance of batteries, with a considerable increase in the ionic conductivity and a slight change of the original material structure. Moreover, our ab initio molecular dynamics simulations (AIMD) show that the asymmetric distorted lattice structure provides a driving force for Li diffusion, resulting in oriented diffusion along the positive strain gradient direction. We predict the new phenomenon of a flexo-diffusion effect from a theoretical calculation aspect, these findings could extend present LIB technologies by introducing a novel strain gradient engineering.

Topology-Based Machine Learning Strategy for Cluster Structure Prediction.

In cluster physics, the determination of the ground-state structure of medium-sized and large-sized clusters is a challenge due to the number of local minimal values on the potential energy surface growing exponentially with cluster size. Although machine learning approaches have had much success in materials sciences, their applications in clusters are often hindered by the geometric complexity clusters. Persistent homology provides a new topological strategy to simplify geometric complexity while retaining important chemical and physical information without having to "downgrade" the original data. We further propose persistent pairwise independence (PPI) to enhance the predictive power of persistent homology. We construct topology-based machine learning models to reveal hidden structure-energy relationships in lithium (Li) clusters. We integrate the topology-based machine learning models, a particle swarm optimization algorithm, and density functional theory calculations to accelerate the search of the globally stable structure of clusters.

Li-Ion Cooperative Migration and Oxy-Sulfide Synergistic Effect in Li14 P2 Ge2 S16-6 x Ox Solid-State-Electrolyte Enables Extraordinary Conductivity and High Stability.

Critical to the development of all-solid-state lithium-ion batteries technology are novel solid-state electrolytes with high ionic conductivity and robust stability under inorganic solid-electrolyte operating conditions. Herein, by using density functional theory and molecular dynamics, a mixed oxygen-sulfur-based Li-superionic conductor is screened out from the local chemical structure of ß-Li3 PS4 to discover novel Li14 P2 Ge2 S8 O8 (LPGSO) with high ionic conductivity and high stability under thermal, moist, and electrochemical conditions, which causes oxygenation at specific sites to improve the stability and selective sulfuration to provide an O-S mixed path by Li-S/O structure units with coordination number between 3 and 4 for fast Li-cooperative conduction. Furthermore, LPGSO exhibits a quasi-isotropic 3D Li-ion cooperative diffusion with a lesser migration barrier (≈0.19 eV) compared to its sulfide-analog Li14 P2 Ge2 S16 . The theoretical ionic conductivity of this conductor at room temperature is as high as ≈30.0 mS cm-1 , which is among the best in current solid-state electrolytes. Such an oxy-sulfide synergistic effect and Li-ion cooperative migration mechanism would enable the engineering of next-generation electrolyte materials with desirable safety and high ionic conductivity, for possible application in the near future.

Neural Network Force Fields for Metal Growth Based on Energy Decompositions.

A method using machine learning (ML) is proposed to describe metal growth for simulations, which retains the accuracy of ab initio density functional theory (DFT) and results in a thousands-fold reduction in the computational time. This method is based on atomic energy decomposition from DFT calculations. Compared with other ML methods, our energy decomposition approach can yield much more information with the same DFT calculations. This approach is employed for the amorphous sodium system, where only 1000 DFT molecular dynamics images are enough for training an accurate model. The DFT and neural network potential (NNP) are compared for the dynamics to show that similar structural properties are generated. Finally, metal growth experiments from liquid to solid in a small and larger system are carried out to demonstrate the ability of using NNP to simulate the real growth process.

Discovery of space aromaticity in transition-metal monoxide crystal Nb3O3 enabled by octahedral Nb6 structural units.

An octahedral Nb6 structural unit with space aromaticity is identified for the first time in a transition-metal monoxide crystal Nb3O3 by ab initio calculations. The strong Nb-Nb metallic bonding facilitates the formation of stable octahedral Nb6 structural units and the release of delocalization energy. Moreover, the Nb atoms in continuously connected Nb6 structural units share their electrons with each other in a continuous space of framework, so that the electrons are uniformly distributed. The newly discovered aromaticity in the octahedral Nb6 structural units extends the range of aromatic compounds and broadens our vision in structural chemistry.

High-throughput HSE study on the doping effect in anatase TiO2.

Titania is a widely used semiconductor due to its excellent optoelectronics and catalytic properties. Doping with other cations or anions by substitution of Ti or O is a common way to adjust the electronic structure of pristine TiO2. Here, using ab initio calculations at the Heyd-Scuseria-Ernzerhof (HSE06) level, the substitution energy, formation energy and electronic structures of anatase TiO2 doped with 40 kinds of elements including transition metals, alkali metals, alkaline earth metals, p-block metals, and nonmetals have been studied systematically. It is found that doping with most of these elements can narrow down the band gap of TiO2, while in some doped systems, a recombination center induced by intermediate bands is also observed. Besides, for transition metal-doped TiO2 systems, the electron spin state analysis of dopants and the doping level investigation reveal that a relatively high spin structure tends to be formed in Cr, Mn, Fe, Zn, Mo, Tc, Ru and Cd-doped TiO2, and the doping levels of 4d-orbital transition metals are generally higher than those of 3d-orbital transition metals.

Unravelling H+ /Zn2+ Synergistic Intercalation in a Novel Phase of Manganese Oxide for High-Performance Aqueous Rechargeable Battery.

Aqueous Zn-MnO2 batteries using mild electrolyte show great potential in large-scale energy storage (LSES) application, due to high safety and low cost. However, structure collapse of manganese oxides upon cycling caused by the conversion mechanism (e.g., from tunnel to layer structures for α-, ß-, and γ-phases) is one of the most urgent issues plaguing its practical applications. Herein, to avoid the phase conversion issue and enhance battery performance, a structurally robust novel phase of manganese oxide MnO2 H0.16 (H2 O)0.27 (MON) nanosheet with thickness of ≈2.5 nm is designed and synthesized as a promising cathode material, in which a nanosheet structure combined with a novel H+ /Zn2+ synergistic intercalation mechanism is demonstrated and evidenced. Accordingly, a high-performance Zn/MON cell is achieved, showing a high energy density of ≈228.5 Wh kg-1 , impressive cyclability with capacity retention of 96% at 0.5 C after 300 cycles, as well as exhibiting rate performance of 115.1 mAh g-1 at current rate of 10 C. To the best current knowledge, this H+ /Zn2+ synergistic intercalation mechanism is first reported in an aqueous battery system, which opens a new opportunity for development of high-performance aqueous Zn ion batteries for LSES.

An Ordered Ni6 -Ring Superstructure Enables a Highly Stable Sodium Oxide Cathode.

Sodium-based layered oxides are among the leading cathode candidates for sodium-ion batteries, toward potential grid energy storage, having large specific capacity, good ionic conductivity, and feasible synthesis. Despite their excellent prospects, the performance of layered intercalation materials is affected by both a phase transition induced by the gliding of the transition metal slabs and air-exposure degradation within the Na layers. Here, this problem is significantly mitigated by selecting two ions with very different MO bond energies to construct a highly ordered Ni6 -ring superstructure within the transition metal layers in a model compound (NaNi2/3 Sb1/3 O2 ). By virtue of substitution of 1/3 nickel with antimony in NaNiO2 , the existence of these ordered Ni6 -rings with super-exchange interaction to form a symmetric atomic configuration and degenerate electronic orbital in layered oxides can not only largely enhance their air stability and thermal stability, but also increase the redox potential and simplify the phase-transition process during battery cycling. The findings reveal that the ordered Ni6 -ring superstructure is beneficial for constructing highly stable layered cathodes and calls for new paradigms for better design of layered materials.

Lithium ion diffusion mechanism in covalent organic framework based solid state electrolyte.

Solid state electrolytes (SSEs) based on two dimensional covalent organic frameworks (2D-COFs) with Li salts and solvents impregnated in their large pores have emerged as novel candidate materials for solid state lithium batteries. Here, using ab initio molecular dynamics simulation, we track the atomic-scale structural evolution during Li+ ion diffusion in a 2D-COF SSE composed of COF-5, LiClO4 and tetrahydrofuran (THF). Our simulation results show the transient dynamics of the Li+ diffusion events, the free rotation of ClO4- ions and the essential role of THFs in partitioning between the ions and the solid framework. We find clear evidence that Li+ ion diffusion adopts a one-dimensional (1D) liquid-like behavior with the coordination evolution driven by facile rotation and short-range diffusion of ClO4- ions and THFs. The fast Li+ diffusion pathway in the 1D tunnels of COFs may shed light on future design of high-performance COF based SSEs.

Tuning polaronic redox behavior in olivine phosphate.

In order to understand and improve the conductivity of LiFePO4, lots of attempts have been made both experimentally and theoretically. Here we performed hybrid density functional theory calculations to systematically investigate the electronic structures with polaronic redox behavior of polyanionic intercalation compounds similar to LiFePO4, such as in XMPO4 (X = Li, Na; M = Mn, Fe, Co, Ni). It is proved that the replacement of Li ions does not eliminate the polaronic redox behavior of Fe ions during delithiation and hence does not lead to a significant improvement in electronic conductivity. By contrast, replacing Fe with Mn, Co or Ni can tune the polaronic redox behavior during delithiation by varying degrees. For Ni, the polaronic redox behavior has almost disappeared, and band gaps disappear during delithiation, indicating a better electronic conductivity. For Mn or Co, the polaronic redox behavior is still obvious with little improvement in the electronic conductivity. This study provides important clues to improve the electronic conductivity of LiFePO4-like cathode materials.

Discovering unusual structures from exception using big data and machine learning techniques.

Recently, machine learning (ML) has become a widely used technique in materials science study. Most work focuses on predicting the rule and overall trend by building a machine learning model. However, new insights are often learnt from exceptions against the overall trend. In this work, we demonstrate that how unusual structures are discovered from exceptions when machine learning is used to get the relationship between atomic and electronic structures based on big data from high-throughput calculation database. For example, after training an ML model for the relationship between atomic and electronic structures of crystals, we find AgO2F, an unusual structure with both Ag3+ and O22-, from structures whose band gap deviates much from the prediction made by our model. A further investigation on this structure might shed light into the research on anionic redox in transition metal oxides of Li-ion batteries.

Wannier Koopmans Method Calculations of 2D Material Band Gaps.

A major drawback of the widely successful density functional theory is its underestimation of the material band gap. Various methods have been proposed to correct its band gap predictions. Wannier Koopmans method (WKM) is recently developed for this purpose to predict the band gap of extended 3D bulk systems. While the WKM has also been shown to be successful for isolated molecules, it is still a question whether it will work for 2D materials that are in between the 0D molecules and 3D bulk systems. We apply the WKM to 16 commonly known well studied 2D materials and find that the WKM predicted band gaps are on par with their GW calculated results.

Electrical Contacts in Monolayer Arsenene Devices.

Arsenene, arsenic analogue of graphene, as an emerging member of two-dimensional semiconductors (2DSCs), is quite promising in next-generation electronic and optoelectronic applications. The metal electrical contacts play a vital role in the charge transport and photoresponse processes of nanoscale 2DSC devices and even can mask the intrinsic properties of 2DSCs. Here, we present a first comprehensive study of the electrical contact properties of monolayer (ML) arsenene with different electrodes by using ab initio electronic calculations and quantum transport simulations. Schottky barrier is always formed with bulk metal contacts owing to the Fermi level pinning (pinning factor S = 0.33), with electron Schottky barrier height (SBH) of 0.12, 0.21, 0.25, 0.35, and 0.50 eV for Sc, Ti, Ag, Cu, and Au contacts and hole SBH of 0.75 and 0.78 eV for Pd and Pt contacts, respectively. However, by contact with 2D graphene, the Fermi level pinning effect can be reduced due to the suppression of metal-induced gap states. Remarkably, a barrier free hole injection is realized in ML arsenene device with graphene-Pt hybrid electrode, suggestive of a high device performance in such a ML arsenene device. Our study provides a theoretical foundation for the selection of favorable electrodes in future ML arsenene devices.

Fast Diffusion of O2 on Nitrogen-Doped Graphene to Enhance Oxygen Reduction and Its Application for High-Rate Zn-Air Batteries.

N-doped graphene (NDG) was investigated for oxygen reduction reaction (ORR) and used as air-electrode catalyst for Zn-air batteries. Electrochemical results revealed a slightly lower kinetic activity but a much larger rate capability for the NDG than commercial 20% Pt/C catalyst. The maximum power density for a Zn-air cell with NDG air cathode reached up to 218 mW cm-2, which is nearly 1.5 times that of its counterpart with the Pt/C (155 mW cm-2). The equivalent diffusion coefficient (DE) of oxygen from electrolyte solution to the reactive sites of NDG was evaluated as about 1.5 times the liquid-phase diffusion coefficient (DL) of oxygen within bulk electrolyte solution. Combined with experiments and ab initio calculations, this seems counterintuitive reverse ORR of NDG versus Pt/C can be rationalized by a spontaneous adsorption and fast solid-state diffusion of O2 on ultralarge graphene surface of NDG to enhance effective ORR on N-doped-catalytic-centers and to achieve high-rate performance for Zn-air batteries.