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.

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.

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.

Mobile Ions in Composite Solids.

Fast ion conduction in solid-state matrices constitutes the foundation for a wide spectrum of electrochemical systems that use solid electrolytes (SEs), examples of which include solid-state batteries (SSBs), solid oxide fuel cells (SOFCs), and diversified gas sensors. Mixing different solid conductors to form composite solid electrolytes (CSEs) introduces unique opportunities for SEs to possess exceptional overall performance far superior to their individual parental solids, thanks to the abundant chemistry and physics at the new interfaces thus created. In this review, we provide a comprehensive and in-depth examination of the development and understanding of CSEs for SSBs, with special focus on their physiochemical properties and mechanisms of ion transport therein. The origin of the enhanced ionic conductivity in CSEs relative to their single-phase parents is discussed in the context of defect chemistry and interfacial reactions. The models/theories for ion movement in diversified composites are critically reviewed to interrogate a general strategy to the design of novel CSEs, while properties such as mechanical strength and electrochemical stability are discussed in view of their perspective applications in lithium metal batteries and beyond. As an integral component of understanding how ions interact with their composite environments, characterization techniques to probe the ion transport kinetics across different temporal and spatial time scales are also summarized.

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.

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.

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.

Visualization of the Diffusion Pathway of Protons in (NH4)2Si0.5Ti0.5P4O13 as an Electrolyte for Intermediate-Temperature Fuel Cells.

We demonstrate that (NH4)2Si0.5Ti0.5P4O13 is an excellent proton conductor. The crystallographic information concerning the hydrogen positions is unraveled from neutron-powder-diffraction (NPD) data for the first time. This study shows that all the hydrogen atoms are connected though H bonds, establishing a two-dimensional path between the [(Si0.5Ti0.5)P4O132-]n layers for proton diffusion across the crystal structure by breaking and reconstructing intermediate H-O═P bonds. This transient species probably reduces the potential energy of the H jump from an ammonium unit to the next neighboring NH4+ unit. Both theoretical and experimental results support an interstitial-proton-conduction mechanism. The proton conductivities of (NH4)2Si0.5Ti0.5P4O13 reach 0.0061 and 0.024 S cm-1 in humid air at 125 and 250 °C, respectively. This finding demonstrates that (NH4)2Si0.5Ti0.5P4O13 is a promising electrolyte material operating at 150-250 °C. This work opens up a new avenue for designing and fabricating high-performance inorganic electrolytes.

Quantitative description on structure-property relationships of Li-ion battery materials for high-throughput computations.

Li-ion batteries are a key technology for addressing the global challenge of clean renewable energy and environment pollution. Their contemporary applications, for portable electronic devices, electric vehicles, and large-scale power grids, stimulate the development of high-performance battery materials with high energy density, high power, good safety, and long lifetime. High-throughput calculations provide a practical strategy to discover new battery materials and optimize currently known material performances. Most cathode materials screened by the previous high-throughput calculations cannot meet the requirement of practical applications because only capacity, voltage and volume change of bulk were considered. It is important to include more structure-property relationships, such as point defects, surface and interface, doping and metal-mixture and nanosize effects, in high-throughput calculations. In this review, we established quantitative description of structure-property relationships in Li-ion battery materials by the intrinsic bulk parameters, which can be applied in future high-throughput calculations to screen Li-ion battery materials. Based on these parameterized structure-property relationships, a possible high-throughput computational screening flow path is proposed to obtain high-performance battery materials.

A Novel Small-Molecule Compound of Lithium Iodine and 3-Hydroxypropionitride as a Solid-State Electrolyte for Lithium-Air Batteries.

A novel small-molecule compound of lithium iodine and 3-hydroxypropionitrile (HPN) has been successfully synthesized. Our combined experimental and theoretical studies indicated that LiIHPN is a Li-ion conductor, which is utterly different from the I(-)-anion conductor of LiI(HPN)2 reported previously. Solid-state lithium-air batteries based on LiIHPN as the electrolyte exhibit a reversible discharge capacity of more than 2100 mAh g(-1) with a cyclic performance over 10 cycles. Our findings provide a new way to design solid-state electrolytes toward high-performance lithium-air batteries.

Hydrogen-doping induced reduction in the phase transition temperature of VO2: a first-principles study.

VO2 is a promising thermochromic material that can intelligently control the transmittance of sunlight in the near-infrared region in response to temperature change, although the high phase transition temperature (Tc) of 340 K restricts its wide application. Our first-principles calculations show that hydrogen is an efficient dopant which can stabilize the metallic VO2 phase at ambient temperature through reducing Tc by 38 K/at% H. The reduction in Tc is coupled with the changes in atomic and electronic structures, i.e., the V-V chains feature the dimerization characteristics in H-doped VO2(R) and the V-O bonds become less ionic due to the formation of a typical H-O covalent bond. In addition, hydrogen-doped VO2 is more sensitive to external strain as compared with pure VO2, implying that Tc can be further regulated through a combination of H-doping and strain.

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.

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.

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.

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.

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.

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.

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.

Direct calculation of Li-ion transport in the solid electrolyte interphase.

The mechanism of Li(+) transport through the solid electrolyte interphase (SEI), a passivating film on electrode surfaces, has never been clearly elucidated despite its overwhelming importance to Li-ion battery operation and lifetime. The present paper develops a multiscale theoretical methodology to reveal the mechanism of Li(+) transport in a SEI film. The methodology incorporates the boundary conditions of the first direct diffusion measurements on a model SEI consisting of porous (outer) organic and dense (inner) inorganic layers (similar to typical SEI films). New experimental evidence confirms that the inner layer in the ∼20 nm thick model SEI is primarily crystalline Li(2)CO(3). Using density functional theory, we first determined that the dominant diffusion carrier in Li(2)CO(3) below the voltage range of SEI formation is excess interstitial Li(+). This diffuses via a knock-off mechanism to maintain higher O-coordination, rather than direct-hopping through empty spaces in the Li(2)CO(3) lattice. Mesoscale diffusion equations were then formulated upon a new two-layer/two-mechanism model: pore diffusion in the outer layer and knock-off diffusion in the inner layer. This diffusion model predicted the unusual isotope ratio (6)Li(+)/(7)Li(+) profile measured by TOF-SIMS, which increases from the SEI/electrolyte surface and peaks at a depth of 5 nm, and then gradually decreases within the dense layer. With no fitting parameters, our approach is applicable to model general transport properties, such as ionic conductivity, for SEI films on the surface of other electrodes, from the atomic scale to the mesoscale, as well as aging phenomenon.