Fundamental Understanding of Hydrogen Evolution Reaction on Zinc Anode Surface: A First-Principles Study.

Hydrogen evolution reaction (HER) has become a key factor affecting the cycling stability of aqueous Zn-ion batteries, while the corresponding fundamental issues involving HER are still unclear. Herein, the reaction mechanisms of HER on various crystalline surfaces have been investigated by first-principle calculations based on density functional theory. It is found that the Volmer step is the rate-limiting step of HER on the Zn (002) and (100) surfaces, while, the reaction rates of HER on the Zn (101), (102) and (103) surfaces are determined by the Tafel step. Moreover, the correlation between HER activity and the generalized coordination number ([Formula: see text]) of Zn at the surfaces has been revealed. The relatively weaker HER activity on Zn (002) surface can be attributed to the higher [Formula: see text] of surface Zn atom. The atomically uneven Zn (002) surface shows significantly higher HER activity than the flat Zn (002) surface as the [Formula: see text] of the surface Zn atom is lowered. The [Formula: see text] of surface Zn atom is proposed as a key descriptor of HER activity. Tuning the [Formula: see text] of surface Zn atom would be a vital strategy to inhibit HER on the Zn anode surface based on the presented theoretical studies. Furthermore, this work provides a theoretical basis for the in-depth understanding of HER on the Zn surface.

FFMDFPA: A FAIRification Framework for Materials Data with No-Code Flexible Semi-Structured Parser and Application Programming Interfaces.

The FAIR Data Principles are guidelines to ensure Findability, Accessibility, Interoperability, and Reusability of digital resources, which are essential to accelerate data-driven materials science. Despite the development and growing adoption of the FAIR principles, appropriate implementation solutions and software to make data FAIR are still sparse, particularly in standardization of heterogeneous data and subsequent data access. Here, we introduce a FAIRification Framework for Materials Data with No-Code Flexible Semi-Structured Parser and API (FFMDFPA) (API, application programming interface) for raw data processing. Using a template-based parser, FFMDFPA can extract and transform semistructured data in various text formats, providing the flexibility to extend data manipulation without coding. Additionally, FFMDFPA provides a standardized API with efficient query syntax that facilitates seamless data sharing. Taking various text files generated by computational software as examples, we demonstrate the potential utility of FFMDFPA. This work offers important insights toward efficient utilization and reuse of materials data, and the data semantic manipulation implemented in the parser and API can be extended to textual data, which has implications for future data FAIRification.

Spatial-temporal dynamics of land surface phenology over Africa for the period of 1982-2015.

Knowledge of the dynamics of vegetation phenology is essential for the understanding of vegetation-climate interactions. Although the interest in phenology study is growing, vegetation phenology in Africa received far less attention compared to the Northern Hemisphere. Africa straddles the northern and southern hemispheres, and the climate has a clear latitudinal gradient, which facilitates the study of the interaction between phenology and climate. In this study, the latitudinal and longitudinal gradients and temporal trends of start of growing season (SOS), peak of growing season (POS), and end of growing season (EOS) were examined using long-term satellite dataset during 1982-2015. The latitudinal variations in these phenology metrics were larger in the northern than those in the southern Africa, especially from 6°N northwards to 16°N. The latitudinal variations in southern Africa had no clear patterns due to the more complex climate systems. For the longitudinal variation, the temporal trends in POS and EOS exhibited a gradient-decreasing rate in northern Africa. Over the period from 1982 to 2015, the overall trends of the phenology in Africa were 'later SOS', 'later POS', and 'later EOS'. The faster rate of delay in EOS than in SOS resulted in a prolonged length of growing season (LOS) with 0.50 days/year on average in northern Africa, while a slower rate of delay in EOS than in SOS resulted in a shorter LOS with -0.12 days/year in southern Africa. The prolonged LOS in northern Africa contributes to the increase in the yearly-averaged Normalized Difference Vegetation Index (NDVI) from 1982 to 2000. Nevertheless, the NDVI appeared to have reached saturation around the 2000s, although the LOS was still extending after 2000s. Overall, the findings of this study provide an overall view of the spatial and temporal patterns of land surface phenology in the African continent, and a necessary component for future studies on the response of phenology to climate.

Data quantity governance for machine learning in materials science.

Data-driven machine learning (ML) is widely employed in the analysis of materials structure-activity relationships, performance optimization and materials design due to its superior ability to reveal latent data patterns and make accurate prediction. However, because of the laborious process of materials data acquisition, ML models encounter the issue of the mismatch between a high dimension of feature space and a small sample size (for traditional ML models) or the mismatch between model parameters and sample size (for deep-learning models), usually resulting in terrible performance. Here, we review the efforts for tackling this issue via feature reduction, sample augmentation and specific ML approaches, and show that the balance between the number of samples and features or model parameters should attract great attention during data quantity governance. Following this, we propose a synergistic data quantity governance flow with the incorporation of materials domain knowledge. After summarizing the approaches to incorporating materials domain knowledge into the process of ML, we provide examples of incorporating domain knowledge into governance schemes to demonstrate the advantages of the approach and applications. The work paves the way for obtaining the required high-quality data to accelerate materials design and discovery based on ML.

Auto-MatRegressor: liberating machine learning alchemists.

Machine learning (ML) is widely used to uncover structure-property relationships of materials due to its ability to quickly find potential data patterns and make accurate predictions. However, like alchemists, materials scientists are plagued by time-consuming and labor-intensive experiments to build high-accuracy ML models. Here, we propose an automatic modeling method based on meta-learning for materials property prediction named Auto-MatRegressor, which automates algorithm selection and hyperparameter optimization by learning from previous modeling experience, i.e., meta-data on historical datasets. The meta-data used in this work consists of 27 meta-features that characterize the datasets and the prediction performances of 18 algorithms commonly used in materials science. To recommend optimal algorithms, a collaborative meta-learning method embedded with domain knowledge quantified by a materials categories tree is designed. Experiments on 60 datasets show that compared with the traditional modeling method from scratch, Auto-MatRegressor automatically selects appropriate algorithms at lower computational cost, which accelerates constructing ML models with good prediction accuracy. Auto-MatRegressor supports dynamic expansion of meta-data with the increase of the number of materials datasets and other required algorithms and can be applied to any ML materials discovery and design task.

Liquid-Like Li-Ion Conduction in Oxides Enabling Anomalously Stable Charge Transport across the Li/Electrolyte Interface in All-Solid-State Batteries.

The softness of sulfur sublattice and rotational PS4 tetrahedra in thiophosphates result in liquid-like ionic conduction, leading to enhanced ionic conductivities and stable electrode/thiophosphate interfacial ionic transport. However, the existence of liquid-like ionic conduction in rigid oxides remains unclear, and modifications are deemed necessary to achieve stable Li/oxide solid electrolyte interfacial charge transport. In this study, by combining the neutron diffraction survey, geometrical analysis, bond valence site energy analysis, and ab initio molecular dynamics simulation, 1D liquid-like Li-ion conduction is discovered in LiTa2 PO8 and its derivatives, wherein Li-ion migration channels are connected by four- or five-fold oxygen-coordinated interstitial sites. This conduction features a low activation energy (0.2 eV) and short mean residence time (<1 ps) of Li ions on the interstitial sites, originating from the Li-O polyhedral distortion and Li-ion correlation, which are controlled by doping strategies. The liquid-like conduction enables a high ionic conductivity (1.2 mS cm-1 at 30 °C), and a 700 h anomalously stable cycling under 0.2 mA cm-2 for Li/LiTa2 PO8 /Li cells without interfacial modifications. These findings provide principles for the future discovery and design of improved solid electrolytes that do not require modifications to the Li/solid electrolyte interface to achieve stable ionic transport.

Enabling the Operation of Highly Compatible LiI-3-Hydroxypropionitrile Small-Molecule Solid-State Electrolytes in Lithium Metal Batteries via Stepped-Amorphization Strategy.

Integrating the advantages of both inorganic ceramic and organic polymer solid-state electrolytes, small-molecule solid-state electrolytes represented by LiI-3-hydroxypropionitrile (LiI-HPN) inorganic-organic hybrid systems possess good interfacial compatibility and high modulus. However, their lack of intrinsic Li+ conduction ability hinders potential application in lithium metal batteries until now, despite containing LiI phase composition. Herein, inspired by evolution tendency of ionic conduction behaviors together with first-principles molecular dynamics simulations, we propose a stepped-amorphization strategy to break the Li+ conduction bottleneck of LiI-HPN. It involves three progressive steps of composition (LiI-content increasing), time (long-time standing), and temperature (high-temperature melting) regulations, to essentially construct a small-molecule-based composite solid-state electrolyte with intensified amorphous degree, which realizes efficient conversion from an I- to Li+ conductor and improved conductivity. As a proof, the stepped-optimized LiI-HPN is successfully operated in lithium metal batteries cooperated with Li4 Ti5 O12 cathode to deliver considerable compatibility and stability over 250 cycles. This work not only clarifies the ionic conduction mechanisms of LiI-HPN inorganic-organic hybrid systems, but also provides a reasonable strategy to broaden the application scenarios of highly compatible small-molecule solid-state electrolytes.

A customized strategy to design intercalation-type Li-free cathodes for all-solid-state batteries.

Pairing Li-free transition-metal-based cathodes (MX) with Li-metal anodes is an emerging trend to overcome the energy-density limitation of current rechargeable Li-ion technology. However, the development of practical Li-free MX cathodes is plagued by the existing notion of low voltage due to the long-term overlooked voltage-tuning/phase-stability competition. Here, we propose a p-type alloying strategy involving three voltage/phase-evolution stages, of which each of the varying trends are quantitated by two improved ligand-field descriptors to balance the above contradiction. Following this, an intercalation-type 2H-V1.75Cr0.25S4 cathode tuned from layered MX2 family is successfully designed, which possesses an energy density of 554.3 Wh kg-1 at the electrode level accompanied by interfacial compatibility with sulfide solid-state electrolyte. The proposal of this class of materials is expected to break free from scarce or high-cost transition-metal (e.g. Co and Ni) reliance in current commercial cathodes. Our experiments further confirm the voltage and energy-density gains of 2H-V1.75Cr0.25S4. This strategy is not limited to specific Li-free cathodes and offers a solution to achieve high voltage and phase stability simultaneously.

The Origin of Solvent Deprotonation in LiI-added Aprotic Electrolytes for Li-O2 Batteries.

LiI and LiBr have been employed as soluble redox mediators (RMs) in electrolytes to address the sluggish oxygen evolution reaction kinetics during charging in aprotic Li-O2 batteries. Compared to LiBr, LiI exhibits a redox potential closer to the theoretical one of discharge products, indicating a higher energy efficiency. However, the reason for the occurrence of solvent deprotonation in LiI-added electrolytes remains unclear. Here, by combining ab initio calculations and experimental validation, we find that it is the nucleophile I O 3 - ${{{\rm I}{\rm O}}_{3}^{-}}$ that triggers the solvent deprotonation and LiOH formation via nucleophilic attack, rather than the increased solvent acidity or the elongated C-H bond as previously suggested. As a comparison, the formation of B r O 3 - ${{{\rm B}{\rm r}{\rm O}}_{3}^{-}}$ in LiBr-added electrolytes is found to be thermodynamically unfavorable, explaining the absence of LiOH formation. These findings provide important insight into the solvent deprotonation and pave the way for the practical application of LiI RM in aprotic Li-O2 batteries.

Breaking the Electronic Conductivity Bottleneck of Manganese Oxide Family for High-Power Fluorinated Graphite Composite Cathode by Ligand-Field High-Dimensional Constraining Strategy.

Primary lithium fluorinated graphite (Li/CFx ) batteries with superior energy density are an indispensable energy supply for multiple fields but suffer from sluggish reaction kinetics of the CFx cathode. Designing composite cathodes emerges as a solution to this problem. Despite the optimal composite component for CFx , the manganese oxide family represented by MnO2 is still faced with an intrinsic electronic conductivity bottleneck, which severely limits the power density of the composite cathode. Here, a cation-induced high-dimensional constraining strategy from the perspective of ligand-field stacking structure topological design, which breaks the molecular orbital hybridization of pristine semiconductive oxides to transform them into the high-conductivity metallic state while competitively maintaining structural stability, is proposed. Through first-principles phase diagram calculations, mixed-valent Mn5 O8 ( Mn 2 2 + Mn 3 4 + O 8 ${\rm{Mn}}_2^{2 + }{\rm{Mn}}_3^{4 + }{{\rm{O}}_8}$ ) is explored as an ideal high-dimensional constraining material with satisfied conductivity and large-scale production feasibility. Experiments demonstrate that the as-proposed CFx  @ Mn5 O8 composite cathode achieves 2.36 times the power density (11399 W kg-1 ) of pristine CFx and a higher CFx conversion ratio (86%). Such a high-dimensional field-constraining strategy is rooted in the established four-quadrant electronic structure tuning framework, which fundamentally changes the orbital symmetry under the ligand field to overcome the common conductivity challenge of wide transition metal oxide materials.

Insights into LiMXO4F (M-X = Al-P and Mg-S) as Cathode Coatings for High-Performance Lithium-Ion Batteries.

Cathode coatings have received extensive attention due to their ability to delay electrochemical performance degradation in lithium-ion batteries. However, the development of cathode coatings possessing high ionic conductivity and good interfacial stability with cathode materials has proven to be a challenge. Here, we performed first-principles computational studies on the phase stability, thermodynamic stability, and ionic transport properties of LiMXO4F (M-X = Al-P and Mg-S) used as cathode coatings. We find that the candidate coatings are thermodynamically metastable and can be synthesized experimentally. The coating materials possess high oxidative stability, with the materials predicted to decompose above 4.2 V, suggesting that they have good electrochemical stability under a high-voltage cathode. In addition, the candidate coatings exhibit significant chemical stability when in contact with oxide cathodes. Finally, we have studied the Li-ion transport paths and migration barriers of LiMXO4F (M-X = Al-P and Mg-S) and calculated the low migration barriers to be 0.19 and 0.09 eV, respectively. Our findings indicate that LiMXO4F (M-X = Al-P and Mg-S) are promising cathode coatings, among which LiAlPO4F has been experimentally confirmed. The theoretical cathode coating computational methods presented here can be extended to the solid-state battery system.

Software for Evaluating Long-Range Electrostatic Interactions Based on the Ewald Summation and Its Application to Electrochemical Energy Storage Materials.

Electrochemical characteristics such as open-circuit voltage and ionic conductivity of electrochemical energy storage materials are easily affected, typically negatively, by mobile ion/vacancy ordering. Ordered phases can be identified based on the lattice gas model and electrostatic energy screening. However, the evaluation of long-range electrostatic energy is not straightforward because of the conditional convergence. The Ewald method decomposes the electrostatic energy into a real space part and a reciprocal space part, achieving a fast convergence in each. Due to its high computational efficiency, Ewald-based techniques are widely used in analyzing characteristics of electrochemical energy storage materials. In this work, we present software not only integrating Ewald techniques for two-dimensional and three-dimensional periodic systems but also combining the Ewald method with the lattice matching algorithm and bond valence. It is aimed to become a useful tool for screening stable structures and interfaces and identifying the ionic transport channels of cation conductors.

Mg2SiO4/Si-Coated Disproportionated SiO Composite Anodes with High Initial Coulombic Efficiency for Lithium Ion Batteries.

Silicon monoxide (SiO) is considered as one of the most promising anode material candidates for next-generation high-energy-density lithium ion batteries (LIBs) due to its high specific capacity and relatively lower volume expansion than that of Si. However, a large number of irreversible products are formed during the first charging and discharging process, resulting in a low initial Coulombic efficiency (ICE) of SiO. Herein, we report an economical and convenient method to increase the ICE of SiO without sacrificing its specific capacity by a solid reaction between magnesium silicide (Mg2Si) and micron-sized SiO. The reaction product (named MSO) exhibits a unique core-shell structure with uniformly distributed Mg2SiO4 and Si as the shell and disproportionated SiO as the core. MSO exhibits a superior ICE and a high reversible capacity of 81.7% and 1306.1 mAh g-1, respectively, which can be further increased to 88.7% and 1446.4 mAh g-1 after carbon coating, and improved cyclic stability compared to bare SiO. This work provides a simple yet effective strategy to address the low ICE issue of SiO anode materials to promote the practical application of SiO.

Broadband stealth devices based on encoded metamaterials.

Based on the generalized Snell's law, the relationship between the phase gradient of the metasurface and the incident frequency is demonstrated, and the principle of the achromatic metasurface is developed. By adjusting the phase gradient and linear dispersion simultaneously, the function of achromatic aberration is realized, and the influence of chromatic aberration on the metasurface is reduced. We propose a metasurface stealth device with achromatic multilayer frame metasurfaces with beam deflection, steering, and collection functions so that the incident electromagnetic beam is transmitted around the stealth object without scattering. In the range of 0.45-0.9 THz, the stealth function can be achieved. We have shown that the achromatic principle, design method, and stealth structure provide a guide for achieving transmissive cloaking.

Metallic VS2/graphene heterostructure as an ultra-high rate and high-specific capacity anode material for Li/Na-ion batteries.

Graphene has been used as a conductive substrate to improve the electrochemical performance of layered VS2 as an anode material for lithium-ion batteries. However, there is still a lack of in-depth understanding of the synergistic effect between the layered VS2 and graphene, which contributes to the enhanced performance of Li/Na-ion batteries. In this work, using first-principles calculations, we have systematically studied the VS2/graphene heterostructure as an anode material for Li/Na-ion batteries. Our results show that the VS2/graphene heterostructure is a promising anode material with good structural stability, high adsorption strength, high stiffness, intrinsic metallic characteristic after Li/Na adsorption, high theoretical specific capacity, shallow averaged open-circuit voltage and ultra-low ion diffusion barriers. The diffusion barriers are found to be 0.03 eV (Li) and 0.08 eV (Na), superior to that of the widely studied heterostructure materials, which guarantees an extremely fast Li/Na diffusion rate during charge/discharge cycling. The anode overall open-circuit voltages for the Li/Na-ion batteries are calculated to be as low as 0.65 and 0.46 V, and the maximum theoretical storage capacity is 771 and 578 mA h g-1, respectively. The above results provide valuable insights into the experimental set-up of the VS2/graphene nanocomposite anode material for ultra-high rate and high-specific capacity Li/Na-ion batteries.

Perfluorinated organics regulating Li2O2 formation and improving stability for Li-oxygen batteries.

Lithium-oxygen batteries have a high theoretical capacity, but they are still far from meeting the capacity required for practical applications. In this study, we systematically investigate the synergistic effect of perfluorotributylamine (PFTBA) as an additive in a TEGDME-based electrolyte to optimize the electrochemical performance of Li-O2 batteries. PFTBA promotes cyclic Li2O2 growth, and the discharge capacity is increased to 9548.7 mA h g-1. Moreover, the hydrophobicity of PFTBA can aid in the protection of the lithium anode against corrosion as it remains stable during cycling. The Li-O2 battery exhibited enhanced cycling stability (200 cycles) as a consequence. This study reveals that PFTBA increases the capacity and provides more possibilities for the application of perfluorinated chemicals in Li-O2 batteries.

Machine learning prediction of activation energy in cubic Li-argyrodites with hierarchically encoding crystal structure-based (HECS) descriptors.

Rational design of solid-state electrolytes (SSEs) with high ionic conductivity and low activation energy (Ea) is vital for all solid-state batteries. Machine learning (ML) techniques have recently been successful in predicting Li+ conduction property in SSEs with various descriptors and accelerating the development of SSEs. In this work, we extend the previous efforts and introduce a framework of ML prediction for Ea in SSEs with hierarchically encoding crystal structure-based (HECS) descriptors. Taking cubic Li-argyrodites as an example, an Ea prediction model is developed to the coefficient of determination (R2) and root-mean-square error (RMSE) values of 0.887 and 0.02 eV for training dataset, and 0.820 and 0.02 eV for test dataset, respectively by partial least squares (PLS) analysis, proving the prediction power of HECS-descriptors. The variable importance in projection (VIP) scores demonstrate the combined effects of the global and local Li+ conduction environments, especially the anion size and the resultant structural changes associated with anion site disorder. The developed Ea prediction model directs us to optimize and design new Li-argyrodites with lower Ea, such as Li6-xPS5-xCl1+x (<0.322 eV), Li6+xPS5+xBr1-x (<0.273 eV), Li6+xPS5+xBr0.25I0.75-x (<0.352 eV), Li6+(5-n)yP1-yNyS5I (<0.420 eV), Li6+(5-n)yAs1-yNyS5I (<0.371 eV), Li6+(5-n)yAs1-yNySe5I (<0.450 eV), by broadening bottleneck size, invoking site disorder and activating concerted Li+ conduction. This analysis shows great potential in promoting rational design of advanced SSEs and the same approach can be applied to other types of materials.

High Quality Pyrazinoquinoxaline-Based Graphdiyne for Efficient Gradient Storage of Lithium Ions.

N-doping of graphdiyne with atomic precision is very important for the study of heteroatom doping effect and the structure-properties relationships of graphdiyne. Here we report the bottom-up synthesis and characterizations of high-quality pyrazinoquinoxaline-based graphdiyne (PQ-GDY) film. First-principle studies of the layered structure were performed to examine the stacking mode, lithium binding affinity, and bulk lithium storage capacity. Three-stage insertion of 14 lithium atoms with binding affinities in the order of pyrazine nitrogen > diyne carbon > central aromatic ring were confirmed by both lithium-ion half-cell measurements and DFT calculations. More than half of the lithium atoms preferentially bind to pyrazine nitrogen, and a reversible capacity of 570.0 mA h g-1 at a current density of 200 mA g-1 after 800 cycles was achieved. Such a high capacity utilization rate of 97.2% provides a good case study of N-doped GDY with atomic precision.

Europium-Doped Ceria Nanowires as Anode for Solid Oxide Fuel Cells.

CeO2-based materials have been studied intensively as anodes for intermediate temperature solid oxide fuel cells (IT-SOFCs). In this work, pristine and europium (Eu)-doped CeO2 nanowires were comprehensively investigated as anode materials for IT-SOFCs, by a combination of theoretical predictions and experimental characterizations. The results demonstrate: (1) Oxygen vacancies can be energetically favorably introduced into the CeO2 lattice by Eu doping; (2) The lattice parameter of the ceria increases linearly with the Eu content when it varies from 0 to 35 mol.%, simultaneously resulting in a significant increase in oxygen vacancies. The concentration of oxygen vacancies reaches its maximum at a ca. 10 mol.% Eu doping level and decreases thereafter; (3) The highest oxygen ion conductivity is achieved at a Eu content of 15 mol.%; while the 10 mol.% Eu-doped CeO2 sample displays the highest catalytic activity for H2-TPR and CO oxidization reactions. The conducting and catalytic properties benefit from the expanded lattice, the large amount of oxygen vacancies, the enhanced reactivity of surface oxygen and the promoted mobility of bulk oxygen ions. These results provide an avenue toward designing and optimizing CeO2 as a promising anode for SOFCs.

CAVD, towards better characterization of void space for ionic transport analysis.

Geometric crystal structure analysis using three-dimensional Voronoi tessellation provides intuitive insights into the ionic transport behavior of metal-ion electrode materials or solid electrolytes by mapping the void space in a framework onto a network. The existing tools typically consider only the local voids by mapping them with Voronoi polyhedra vertices and then define the mobile ions pathways using the Voronoi edges connecting these vertices. We show that in some structures mobile ions are located on Voronoi polyhedra faces and thus cannot be located by a standard approach. To address this deficiency, we extend the method to include Voronoi faces in the constructed network. This method has been implemented in the CAVD python package. Its effectiveness is demonstrated by 99% recovery rate for the lattice sites of mobile ions in 6,955 Li-, Na-, Mg- and Al-containing ionic compounds extracted from the Inorganic Crystal Structure Database. In addition, various quantitative descriptors of the network can be used to identify and rank the materials and further used in materials databases for machine learning.