Establishment and validation of an electron inelastic mean free path database for narrow bandgap inorganic compounds with a machine learning approach.

Narrow bandgap inorganic compounds are extremely important in many areas of physics. However, their basic parameter database for surface analysis is incomplete. Electron inelastic mean free paths (IMFPs) are important parameters in surface analysis methods, such as electron spectroscopy and electron microscopy. Our previous research has presented a machine learning (ML) method to describe and predict IMFPs from calculated IMFPs for 41 elemental solids. This paper extends the use of the same machine learning method to 42 inorganic compounds based on the experience in predicting elemental electron IMFPs. The in-depth discussion extends to including material dependence discussion and parameter value selections. After robust validation of the ML method, we have produced an extensive IMFP database for 12 039 narrow bandgap inorganic compounds. Our findings suggest that ML is very efficient and powerful for IMFP description and database completion for various materials and has many advantages, including stability and convenience, over traditional methods.

Metal-Organic Framework-Derived Graphene Mesh: a Robust Scaffold for Highly Exposed Fe-N4 Active Sites toward an Excellent Oxygen Reduction Catalyst in Acid Media.

This study demonstrates a special ultrathin N-doped graphene nanomesh (NGM) as a robust scaffold for highly exposed Fe-N4 active sites. Significantly, the pore sizes of the NGM can be elaborately regulated by adjusting the thermal exfoliation conditions to simultaneously disperse and anchor Fe-N4 moieties, ultimately leading to highly loaded Fe single-atom catalysts (SA-Fe-NGM) and a highly exposed morphology. The SA-Fe-NGM is found to deliver a superior oxygen reduction reaction (ORR) activity in acidic media (half-wave potential = 0.83 V vs RHE) and a high power density of 634 mW cm-2 in the H2/O2 fuel cell test. First-principles calculations further elucidate the possible catalytic mechanism for ORR based on the identified Fe-N4 active sites and the pore size distribution analysis. This work provides a novel strategy for constructing highly exposed transition metals and nitrogen co-doped carbon materials (M-N-C) catalysts for extended electrocatalytic and energy storage applications.

Heusler alloy catalysts for electrochemical CO2 reduction.

Developing efficient catalysts for electrochemical CO2 reduction reaction (ECO2RR) to hydrocarbons is becoming increasingly important but still challenging due to their high overpotential and poor selectivity. Here, the famous Heusler alloys are investigated as ECO2RR catalysts for the first time by means of density functional theory calculations. The linear scaling relationship between the adsorption energies of CHO (and COOH) and CO intermediates is broken and, thus, the overpotential can be tuned regularly by chemically permuting different 3d, 4d, or 5d transition metals (TMs) in Heusler alloy Cu2TMAl. Cu2ZnAl shows the best activity among all the 30 Heusler alloys considered in the present study, with 41% improvement in energy efficiency compared to pure Cu electrode. Cu2PdAl, Cu2AgAl, Cu2PtAl, and Cu2AuAl are also good candidates. The calculations on the competition between hydrogen evolution reaction and CO2RR indicate that Cu2ZnAl is also the one having the best selectivity toward hydrocarbons. This work identifies the possibility of applying the Heusler alloy as an efficient ECO2RR catalyst. Since thousands of Heusler alloys have been found in experiments, the present study also encourages the search for more promising candidates in this broad research area.

Sp-Hybridized Nitrogen as New Anchoring Sites of Iron Single Atoms to Boost the Oxygen Reduction Reaction.

Carbon supported single-atom catalysts with metal-Nx configuration are considered as one of the most efficient catalysts for the oxygen reduction reaction (ORR). However, most of the metal-Nx active sites are composed by pyridinic N at the defect locations of graphene-like supports. Here, we employ graphdiyne (GDY) as a new carbon substrate to synthesize an iron (Fe) single atom catalyst (Fe-N-GDY), showing excellent catalytic performance. Benefitting from the abundant acetylenic bonds in GDY, sp-N anchored metal atoms are created without forming defects. The sp-N and OH ligands regulate the electronic structure of Fe atoms and optimize the adsorption energy of ORR intermediates on the active sites by reducing the electron local density of Fe atoms, which accelerates the reaction kinetics and promotes the ORR activity of Fe-N-GDY. Furthermore, the practical application of Fe-N-GDY is corroborated by its high power density and long-term performance via assembling a zinc-air battery.

Formation of oxygen vacancies in Li2FeSiO4: first-principles calculations.

The formation of oxygen vacancies could affect various properties of oxides. Herein we have investigated the formation energies of an oxygen vacancy (VO) with the relevant charge states in bulk Pnma-Li2FeSiO4 using first-principles calculations. The formation energies of the VO are essentially dependent on the atomic chemical potentials that represent the experimental conditions. The calculated formation energies of an oxygen vacancy in different charge states indicate that it would be energetically favorable to fully ionize the oxygen vacancy in Li2FeSiO4. The presence of VO is accompanied by a distinct redistribution of the electronic charge densities only around the Fe and Si ions next to the O-vacancy site, which shows a very local influence on the host material arising from VO. This local characteristic is also confirmed by the calculated partial densities of states (PDOS). We also studied the influence of substitutional (MnFe and CoFe) and cation vacancy defects (i.e., VFe and VLi) in the vicinity of an O-vacancy on the formation of an O-vacancy, respectively. We find that the calculated interaction energies between these defects and the oxygen vacancy are all negative, which implies that the formation of an oxygen vacancy becomes easier when the above defects are introduced. Compared to the substitutional defects, the interaction energies between the vacancy defects and the oxygen vacancy are significantly larger. Among them, the interaction energy between VFe and VO is the largest.

Epitaxial Growth of Rectangle Shape MoS2 with Highly Aligned Orientation on Twofold Symmetry a-Plane Sapphire.

Research on transition metal dichalcogenides (TMDs) has been accelerated by the development of large-scale synthesis based on chemical vapor deposition (CVD) growth. However, in most cases, CVD-grown TMDs are composed of randomly oriented grains, and thus contain many distorted grain boundaries (GBs), which seriously degrade their electrical and photoelectrical properties. Here, the epitaxial growth of highly aligned MoS2 grains is reported on a twofold symmetry a-plane sapphire substrate. The obtained MoS2 grains have an unusual rectangle shape with perfect orientation alignment along the [1-100] crystallographic direction of a-plane sapphire. It is found that the growth temperature plays a key role in its orientation alignment and morphology evolution, and high temperature is beneficial to the initial MoS2 seeds rotate to the favorable orientation configurations. In addition, the photoluminescence quenching of the well-aligned MoS2 grains indicates a strong MoS2 -substrate interaction which induces the anisotropic growth of MoS2 , and thus brings the formation of rectangle shape grains. Moreover, the well-aligned MoS2 grains splice together without GB formation, and thus that has negligible effect on its electrical transport properties. The progress achieved in this work could promote the controlled synthesis of large-area TMDs single crystal film and the scalable fabrication of high-performance electronic devices.

Rational Design of Nanoporous MoS2 /VS2 Heteroarchitecture for Ultrahigh Performance Ammonia Sensors.

2D transition metal dichalcogenides (TMDs) have received widespread interest by virtue of their excellent electrical, optical, and electrochemical characteristics. Recent studies on TMDs have revealed their versatile utilization as electrocatalysts, supercapacitors, battery materials, and sensors, etc. In this study, MoS2 nanosheets are successfully assembled on the porous VS2 (P-VS2 ) scaffold to form a MoS2 /VS2 heterostructure. Their gas-sensing features, such as sensitivity and selectivity, are investigated by using a quartz crystal microbalance (QCM) technique. The QCM results and density functional theory (DFT) calculations reveal the impressive affinity of the MoS2 /VS2 heterostructure sensor toward ammonia with a higher adsorption uptake than the pristine MoS2 or P-VS2 sensor. Furthermore, the adsorption kinetics of the MoS2 /VS2 heterostructure sensor toward ammonia follow the pseudo-first-order kinetics model. The excellent sensing features of the MoS2 /VS2 heterostructure render it attractive for high-performance ammonia sensors in diverse applications.

Crystal Growth, Structural Analysis, and Pressure-Induced Superconductivity in a AgIn5Se8 Single Crystal Explored by a Data-Driven Approach.

A high-throughput first-principles calculation-assisted data-driven approach based on an inorganic materials database named AtomWork was performed to explore new superconducting materials. Specific band structures of a small band gap and flat band at band edges were used in a screening procedure. Among the candidates studied, we focused on AgIn5Se8, which shows a high density of state at the Fermi level. Single crystals of AgIn5Se8 were successfully obtained via a melt and slow cooling method. The valence states in AgIn5Se8 were estimated to be Ag1+, In3+, and Se2- using X-ray photoelectron spectroscopy. An electrical transport property of resistance was measured under high pressure using an electrodes-inserted diamond anvil cell. The sample exhibited an insulator-to-metal transition with a drastic decrease of the resistance by increasing the pressure up to 24.8 GPa. A possibility of a pressure-driven phase transition below this pressure was indicated by an enthalpy calculation. At a higher pressure region of 52.5 GPa, a pressure-induced superconducting transition was observed at 3.4 K. The maximum transition temperature was increased up to 3.7 K under the pressure of 74.0 GPa.

Unveiling the principle descriptor for predicting the electron inelastic mean free path based on a machine learning framework.

The TPP-2M formula is the most popular empirical formula for the estimation of the electron inelastic mean free paths (IMFPs) in solids from several simple material parameters. The TPP-2M formula, however, poorly describes several materials because it relies heavily on the traditional least-squares analysis. Herein, we propose a new framework based on machine learning to overcome the weakness. This framework allows a selection from an enormous number of combined terms (descriptors) to build a new formula that describes the electron IMFPs. The resulting framework not only provides higher average accuracy and stability but also reveals the physics meanings of several newly found descriptors. Using the identified principle descriptors, a complete physics picture of electron IMFPs is obtained, including both single and collective electron behaviors of inelastic scattering. Our findings suggest that machine learning is robust and efficient to predict the IMFP and has great potential in building a regression framework for data-driven problems. Furthermore, this method could be applicable to find empirical formula for given experimental data using a series of parameters given a priori, holds potential to find a deeper connection between experimental data and a priori parameters.

Structure prediction of boron-doped graphene by machine learning.

Heteroatom doping has endowed graphene with manifold aspects of material properties and boosted its applications. The atomic structure determination of doped graphene is vital to understand its material properties. Motivated by the recently synthesized boron-doped graphene with relatively high concentration, here we employ machine learning methods to search the most stable structures of doped boron atoms in graphene, in conjunction with the atomistic simulations. From the determined stable structures, we find that in the free-standing pristine graphene, the doped boron atoms energetically prefer to substitute for the carbon atoms at different sublattice sites and that the para configuration of boron-boron pair is dominant in the cases of high boron concentrations. The boron doping can increase the work function of graphene by 0.7 eV for a boron content higher than 3.1%.

Data-driven exploration of new pressure-induced superconductivity in PbBi2Te4.

Candidate compounds for new thermoelectric and superconducting materials, which have narrow band gap and flat bands near band edges, were exhaustively searched by the high-throughput first-principles calculation from an inorganic materials database named AtomWork. We focused on PbBi2Te4 which has the similar electronic band structure and the same crystal structure with those of a pressure-induced superconductor SnBi2Se4 explored by the same data-driven approach. The PbBi2Te4 was successfully synthesized as single crystals using a melt and slow cooling method. The core level X-ray photoelectron spectroscopy analysis revealed Pb2+, Bi3+ and Te2- valence states in PbBi2Te4. The thermoelectric properties of the PbBi2Te4 sample were measured at ambient pressure and the electrical resistance was also evaluated under high pressure using a diamond anvil cell with boron-doped diamond electrodes. The resistance decreased with increasing of the pressure, and pressure-induced superconducting transitions were discovered at 2.5 K under 10 GPa. The maximum superconducting transition temperature increased up to 8.4 K at 21.7 GPa. The data-driven approach shows promising power to accelerate the discovery of new thermoelectric and superconducting materials.

MDTS: automatic complex materials design using Monte Carlo tree search.

Complex materials design is often represented as a black-box combinatorial optimization problem. In this paper, we present a novel python library called MDTS (Materials Design using Tree Search). Our algorithm employs a Monte Carlo tree search approach, which has shown exceptional performance in computer Go game. Unlike evolutionary algorithms that require user intervention to set parameters appropriately, MDTS has no tuning parameters and works autonomously in various problems. In comparison to a Bayesian optimization package, our algorithm showed competitive search efficiency and superior scalability. We succeeded in designing large Silicon-Germanium (Si-Ge) alloy structures that Bayesian optimization could not deal with due to excessive computational cost. MDTS is available at https://github.com/tsudalab/MDTS.

Active sites and mechanisms for oxygen reduction reaction on nitrogen-doped carbon alloy catalysts: Stone-Wales defect and curvature effect.

Carbon alloy catalysts (CACs) are promising oxygen reduction reaction (ORR) catalysts to substitute platinum. However, despite extensive studies on CACs, the reaction sites and mechanisms for ORR are still in controversy. Herein, we present rather general consideration on possible ORR mechanisms for various structures in nitrogen doped CACs based on the first-principles calculations. Our study indicates that only a particular structure of a nitrogen pair doped Stone-Wales defect provides a good active site. The ORR activity of this structure can be tuned by the curvature around the active site, which makes its limiting potential approaching the maximum limiting potential (0.80 V) in the volcano plot for the ORR activity of CACs. The calculated results can be compared with the recent experimental ones of the half-wave potential for CAC systems that range from 0.60 to 0.80 V in the reversible-hydrogen-electrode (RHE) scale.

Theoretical characterization of X-ray absorption, emission, and photoelectron spectra of nitrogen doped along graphene edges.

K-edge X-ray absorption (XAS), emission (XES), and photoelectron (XPS) spectra of nitrogen doped along graphene edges are systematically investigated by using first-principles methods. In this study we considered pyridinium-like, pyridine-like, cyanide-like, and amine-like nitrogens at armchair and zigzag edges and pyrrole-like nitrogen at armchair edge as well as graphite-like nitrogen at graphene interior site. Our results indicate that nitrogen configuration and its location (armchair or zigzag edge) in nitrogen-doped graphene can be identified via the spectral analysis. Furthermore, some controversial spectral features observed in experiment for N-doped graphene-like materials are unambiguously assigned. The present analysis gives an explanation to the reason why the peak assignment is usually made differently between XPS and XAS.

Interlayer Charge Transport in 2D Lead Halide Perovskites from First Principles.

We report on the implementation of a versatile projection-operator diabatization approach to calculate electronic coupling integrals in layered periodic systems. The approach is applied to model charge transport across the saturated organic spacers in two-dimensional (2D) lead halide perovskites. The calculations yield out-of-plane charge transfer rates that decay exponentially with the increasing length of the alkyl chain, range from a few nanoseconds to milliseconds, and are supportive of a hopping mechanism. Most importantly, we show that the charge carriers strongly couple to distortions of the Pb-I framework and that accounting for the associated nonlocal dynamic disorder increases the thermally averaged interlayer rates by a few orders of magnitude compared to the frozen-ion 0 K-optimized structure. Our formalism provides the first comprehensive insight into the role of the organic spacer cation on vertical transport in 2D lead halide perovskites and can be readily extended to functional π-conjugated spacers, where we expect the improved energy alignment with the inorganic layout to speed up the charge transfer between the semiconducting layers.

Mixed Sulfur/Selenium Anions Weaken Electron-Vibrational Interaction in Cu2ZnSn(S,Se)4 Photoabsorber.

Kesterite Cu2ZnSn(S,Se)4 (CZTSSe) solar absorbers have attracted intensive investigations for next-generation photovoltaic applications. Here, by using ab initio static and molecular dynamics simulations, we investigated the anion compositional dependence of electron-vibration interaction in CZTSSe materials. We found that the conduction band fluctuates more than the valence band, and as a result, the band gap variation is more sensitive to the change of the former, which can be understood in terms of p-d hybridization in the valence bands. Electron-phonon coupling is smaller in CZTSSe alloy compared to pure S- or Se-containing structures, as evidenced by the smaller fluctuation of excitation energy, and can be attributed to the weaker structural dynamics of the metal-anion bond. Small electron-phonon coupling strength may lead to better charge transport in these materials. We also elucidated the interplay between disordered structures and S/Se stoichiometry through analysis of optical line width. The results highlight the importance of anion composition engineering and provide new insights into the rational design of high-performing kesterite absorbers for solar cells.

Charge Compensation Mechanisms and Oxygen Vacancy Formations in LiNi1/3Co1/3Mn1/3O2: First-Principles Calculations.

Charge compensation mechanisms in the delithiation processes of LiNi1/3Co1/3Mn1/3O2 (NCM111) are compared in detail by the first-principles calculations with GGA and GGA+U methods under different U values reported in the literature. The calculations suggested that different sets of U values lead to different charge compensation mechanisms in the delithiation process. Co3+/Co4+ couples were shown to dominate the redox reaction for 1 ≥ x ≥ 2/3 by using the GGA+U 1 method (U 1 = 6.0 3.4 3.9 for Ni, Co, and Mn, respectively). However, by using the GGA+U 2 (U 2 = 6.0 5.5 4.2) method, the results indicated that the redox reaction of Ni2+/Ni3+ took place in the range of 1 ≥ x ≥ 2/3. Therefore, according to our study, experimental charge compensation processes during delithiation are of great importance to evaluate the theoretical calculations. The results also indicated that all the GGA+U i (i = 1, 2, 3) schemes predicted better voltage platforms than the GGA method. The oxygen anionic redox reactions during delithiation are also compared with GGA+U calculations under different U values. The electronic density of states and magnetic moments of transition metals have been employed to illustrate the redox reactions during the lithium extractions in NCM111. We have also investigated the formation energies of an oxygen vacancy in NCM111 under different values of U, which is important in understanding the possible occurrence of oxygen release. The formation energy of an O vacancy is essentially dependent on the experimental conditions. As expected, the decreased temperature and increased oxygen partial pressure can suppress the formation of the oxygen vacancy. The calculations can help improve the stability of the lattice oxygen.

Li8MnO6: A Novel Cathode Material with Only Anionic Redox.

In Li-excess transition metal-oxide cathode materials, anionic oxygen redox can offer high capacity and high voltages, although peroxo and superoxo species may cause oxygen loss, poor cycling performance, and capacity fading. Previous work showed that undesirable formation of peroxide and superoxide bonds was controlled to some extent by Mn substitution, and the present work uses density functional calculations to examine the reasons for this by studying the anionic redox mechanism in Li8MnO6. This material is obtained by substituting Mn for Sn in Li8SnO6 or for Zr in Li8ZrO6, and we also compare this to previous work on those materials. The calculations predict that Li8MnO6 is stable at room temperature (with a band gap of 3.19 eV as calculated by HSE06 and 1.82 eV as calculated with the less reliable PBE+U), and they elucidate the chemical and structural effects involved in the inhibition of oxygen release in this cathode. Throughout the whole delithiation process, only O2- ions are oxidized. The directional Mn-O bonds formed from unfilled 3d orbitals effectively inhibit the formation of O-O bonds, and the layered structure is maintained even after removing 3 Li per Li8MnO6 formula unit. The calculated average voltage for removal of 3 Li is 3.69 V by HSE06, and the corresponding capacity is 389 mAh/g. The high voltage of oxygen anionic redox and the high capacity result in a high energy density of 1436 Wh/kg. The Li-ion diffusion barrier for the dominant interlayer diffusion path along the c axis is 0.57 eV by PBE+U. These results help us to understand the oxygen redox mechanism in a new lithium-rich Li8MnO6 cathode material and contribute to the design of high-energy density lithium-ion battery cathode materials with favorable electrochemical properties based on anionic oxygen redox.

Fe-Al-Si Thermoelectric (FAST) Materials and Modules: Diffusion Couple and Machine-Learning-Assisted Materials Development.

To lower the introduction and maintenance costs of autonomous power supplies for driving Internet-of-things (IoT) devices, we have developed low-cost Fe-Al-Si-based thermoelectric (FAST) materials and power generation modules. Our development approach combines computational science, experiments, mapping measurements, and machine learning (ML). FAST materials have a good balance of mechanical properties and excellent chemical stability, superior to that of conventional Bi-Te-based materials. However, it remains challenging to enhance the power factor (PF) and lower the thermal conductivity of FAST materials to develop reliable power generation devices. This forum paper describes the current status of materials development based on experiments and ML with limited data, together with power generation module fabrication related to FAST materials with a view to commercialization. Combining bulk combinatorial methods with diffusion couple and mapping measurements could accelerate the search to enhance PF for FAST materials. We report that ML prediction is a powerful tool for finding unexpected off-stoichiometric compositions of the Fe-Al-Si system and dopant concentrations of a fourth element to enhance the PF, i.e., Co substitution for Fe atoms in FAST materials.

Identifying and Passivating Killer Defects in Pb-Free Double Cs2AgBiBr6 Perovskite.

Pb-free double perovskites, such as Cs2AgBiBr6, are alternatives to lead halide perovskites for photovoltaic applications due to superior stability, low toxicity, and promising optoelectronic properties. However, their performance is subpar. We combine nonadiabatic molecular dynamics and real-time time-dependent density-functional theory to show that the negatively charged Br vacancy in Cs2AgBiBr6 creates an extremely detrimental donor-yielded (DY) center, which is a typical defect in six-coordinated semiconductors. Ag+ and Bi3+ form a bond by attraction through the anisotropic vacancy charge, generating a midgap state that traps holes within tens of picoseconds. Substituting Ag with indium by doping produces a weak and long In-Bi bond, lifting the defect energy level to the conduction band. Hole trapping slows down by an order or magnitude, and trap-assisted charge recombination decreases 4-fold. The simulations bring atomistic insights into defects of Pb-free double perovskites and provide a defect mitigation strategy for rational design of high-performance optoelectronic devices.