ALBERT

Beschreibung: To accelerate the discovery of materials through computations and experiments, a well-established protocol closely bridging these methods is required. We introduce a high-throughput screening protocol for the discovery of bimetallic catalysts that replace palladium (Pd), where the similarities in the electronic density of states patterns were employed as a screening descriptor. Using first-principles calculations, we screened 4350 bimetallic alloy structures and proposed eight candidates expected to have catalytic performance comparable to that of Pd. Our experiments demonstrate that four bimetallic catalysts indeed exhibit catalytic properties comparable to those of Pd. Moreover, we discover a bimetallic (Ni-Pt) catalyst that has not yet been reported for H2O2 direct synthesis. In particular, Ni61Pt39 outperforms the prototypical Pd catalyst for the chemical reaction and exhibits a 9.5-fold enhancement in cost-normalized productivity. This protocol provides an opportunity for the catalyst discovery for the replacement or reduction in the use of the platinum-group metals.

Beschreibung: A Correction to this paper has been published: https://doi.org/10.1038/s41524-021-00497-6

Beschreibung: Classical turning surfaces of Kohn–Sham potentials separate classically allowed regions (CARs) from classically forbidden regions (CFRs). They are useful for understanding many chemical properties of molecules but need not exist in solids, where the density never decays to zero. At equilibrium geometries, we find that CFRs are absent in perfect metals, rare in covalent semiconductors at equilibrium, but common in ionic and molecular crystals. In all materials, CFRs appear or grow as the internuclear distances are uniformly expanded. They can also appear at a monovacancy in a metal. Calculations with several approximate density functionals and codes confirm these behaviors. A classical picture of conduction suggests that CARs should be connected in metals, and disconnected in wide-gap insulators, and is confirmed in the limits of extreme compression and expansion. Surprisingly, many semiconductors have no CFR at equilibrium, a key finding for density functional construction. Nonetheless, a strong correlation with insulating behavior can still be inferred. Moreover, equilibrium bond lengths for all cases can be estimated from the bond type and the sum of the classical turning radii of the free atoms or ions.

Beschreibung: Two-dimensional (2D) magnetic materials are essential for the development of the next-generation spintronic technologies. Recently, layered van der Waals (vdW) compound MnBi2Te4 (MBT) has attracted great interest, and its 2D structure has been reported to host coexisting magnetism and topology. Here, we design several conceptual nanodevices based on MBT monolayer (MBT-ML) and reveal their spin-dependent transport properties by means of the first-principles calculations. The pn-junction diodes and sub-3-nm pin-junction field-effect transistors (FETs) show a strong rectifying effect and a spin filtering effect, with an ideality factor n close to 1 even at a reasonably high temperature. In addition, the pip- and nin-junction FETs give an interesting negative differential resistive (NDR) effect. The gate voltages can tune currents through these FETs in a large range. Furthermore, the MBT-ML has a strong response to light. Our results uncover the multifunctional nature of MBT-ML, pave the road for its applications in diverse next-generation semiconductor spin electric devices.

Beschreibung: Double-network (DN) hydrogels, consisting of two contrasting and interpenetrating polymer networks, are considered as perhaps the toughest soft-wet materials. Current knowledge of DN gels from synthesis methods to toughening mechanisms almost exclusively comes from chemically-linked DN hydrogels by experiments. Molecular modeling and simulations of inhomogeneous DN structure in hydrogels have proved to be extremely challenging. Herein, we developed a new multiscale simulation platform to computationally investigate the early fracture of physically-chemically linked agar/polyacrylamide (agar/PAM) DN hydrogels at a long timescale. A “random walk reactive polymerization” (RWRP) was developed to mimic a radical polymerization process, which enables to construct a physically-chemically linked agar/PAM DN hydrogel from monomers, while conventional and steered MD simulations were conducted to examine the structural-dependent energy dissipation and fracture behaviors at the relax and deformation states. Collective simulation results revealed that energy dissipation of agar/PAM hydrogels was attributed to a combination of the pulling out of agar chains from the DNs, the disruption of massive hydrogen bonds between and within DN structures, and the strong association of water molecules with both networks, thus explaining a different mechanical enhancement of agar/PAM hydrogels. This computational work provided atomic details of network structure, dynamics, solvation, and interactions of a hybrid DN hydrogel, and a different structural-dependent energy dissipation mode and fracture behavior of a hybrid DN hydrogel, which help to design tough hydrogels with new network structures and efficient energy dissipation modes. Additionally, the RWRP algorithm can be generally applied to construct the radical polymerization-produced hydrogels, elastomers, and polymers.

Beschreibung: Exploration of structure-property relationships as a function of dopant concentration is commonly based on mean field theories for solid solutions. However, such theories that work well for semiconductors tend to fail in materials with strong correlations, either in electronic behavior or chemical segregation. In these cases, the details of atomic arrangements are generally not explored and analyzed. The knowledge of the generative physics and chemistry of the material can obviate this problem, since defect configuration libraries as stochastic representation of atomic level structures can be generated, or parameters of mesoscopic thermodynamic models can be derived. To obtain such information for improved predictions, we use data from atomically resolved microscopic images that visualize complex structural correlations within the system and translate them into statistical mechanical models of structure formation. Given the significant uncertainties about the microscopic aspects of the material’s processing history along with the limited number of available images, we combine model optimization techniques with the principles of statistical hypothesis testing. We demonstrate the approach on data from a series of atomically-resolved scanning transmission electron microscopy images of MoxRe1-xS2 at varying ratios of Mo/Re stoichiometries, for which we propose an effective interaction model that is then used to generate atomic configurations and make testable predictions at a range of concentrations and formation temperatures.

Beschreibung: One-dimensional inorganic nanotubes hold promise for technological applications due to their distinct physical/chemical properties, but so far advancements have been hampered by difficulties in producing single-wall nanotubes with a well-defined radius. In this work we investigate, based on Density Functional Theory (DFT), the formation mechanism of 135 different inorganic nanotubes formed by the intrinsic self-rolling driving force found in asymmetric 2D Janus sheets. We show that for isovalent Janus sheets, the lattice mismatch between inner and outer atomic layers is the driving force behind the nanotube formation, while in the non-isovalent case it is governed by the difference in chemical bond strength of the inner and outer layer leading to steric effects. From our pool of candidate structures we have identified more than 100 tubes with a preferred radius below 35 Å, which we hypothesize can display distinctive properties compared to their parent 2D monolayers. Simple descriptors have been identified to accelerate the discovery of small-radius tubes and a Bayesian regression approach has been implemented to assess the uncertainty in our predictions on the radius.

Beschreibung: We present a way to dramatically accelerate Gaussian process models for interatomic force fields based on many-body kernels by mapping both forces and uncertainties onto functions of low-dimensional features. This allows for automated active learning of models combining near-quantum accuracy, built-in uncertainty, and constant cost of evaluation that is comparable to classical analytical models, capable of simulating millions of atoms. Using this approach, we perform large-scale molecular dynamics simulations of the stability of the stanene monolayer. We discover an unusual phase transformation mechanism of 2D stanene, where ripples lead to nucleation of bilayer defects, densification into a disordered multilayer structure, followed by formation of bulk liquid at high temperature or nucleation and growth of the 3D bcc crystal at low temperature. The presented method opens possibilities for rapid development of fast accurate uncertainty-aware models for simulating long-time large-scale dynamics of complex materials.

Beschreibung: The recent discovery of topological superconductors (TSCs) has sparked enormous interest. The realization of TSC requires a delicate tuning of multiple microscopic parameters, which remains a great challenge. Here, we develop a first-principles approach to quantify realistic conditions of TSC by solving self-consistently Bogoliubov-de Gennes equation based on a Wannier function construction of band structure, in presence of Rashba spin-orbit coupling, Zeeman splitting and electron-phonon coupling. We further demonstrate the power of this method by predicting the Mn-doped GeTe (Ge1-xMnxTe) monolayer—a well-known dilute magnetic semiconductor showing superconductivity under hole doping—to be a Class D TSC with Chern number of −1 and chiral Majorana edge modes. By constructing a first-principles phase diagram in the parameter space of temperature and Mn concentration, we propose the TSC phase can be induced at a lower-limit transition temperature of ~40 mK and the Mn concentration of x~0.015%. Our approach can be generally applied to TSCs with a phonon-mediated pairing, providing useful guidance for future experiments.

Beschreibung: To help researchers conduct a systematic review or meta-analysis as efficiently and transparently as possible, we designed a tool to accelerate the step of screening titles and abstracts. For many tasks—including but not limited to systematic reviews and meta-analyses—the scientific literature needs to be checked systematically. Scholars and practitioners currently screen thousands of studies by hand to determine which studies to include in their review or meta-analysis. This is error prone and inefficient because of extremely imbalanced data: only a fraction of the screened studies is relevant. The future of systematic reviewing will be an interaction with machine learning algorithms to deal with the enormous increase of available text. We therefore developed an open source machine learning-aided pipeline applying active learning: ASReview. We demonstrate by means of simulation studies that active learning can yield far more efficient reviewing than manual reviewing while providing high quality. Furthermore, we describe the options of the free and open source research software and present the results from user experience tests. We invite the community to contribute to open source projects such as our own that provide measurable and reproducible improvements over current practice.

Beschreibung: Understanding how to optimize electronic band structures for thermoelectrics is a topic of long-standing interest in the community. Prior models have been limited to simplified bands and/or scattering models. In this study, we apply more rigorous scattering treatments to more realistic model band structures—upward-parabolic bands that inflect to an inverted-parabolic behavior—including cases of multiple bands. In contrast to common descriptors (e.g., quality factor and complexity factor), the degree to which multiple pockets improve thermoelectric performance is bounded by interband scattering and the relative shapes of the bands. We establish that extremely anisotropic “flat-and-dispersive” bands, although best-performing in theory, may not represent a promising design strategy in practice. Critically, we determine optimum bandwidth, dependent on temperature and lattice thermal conductivity, from perfect transport cutoffs that can in theory significantly boost zT beyond the values attainable through intrinsic band structures alone. Our analysis should be widely useful as the thermoelectric research community eyes zT 〉 3.

Beschreibung: The PRISMS-Fatigue open-source framework for simulation-based analysis of microstructural influences on fatigue resistance for polycrystalline metals and alloys is presented here. The framework uses the crystal plasticity finite element method as its microstructure analysis tool and provides a highly efficient, scalable, flexible, and easy-to-use ICME community platform. The PRISMS-Fatigue framework is linked to different open-source software to instantiate microstructures, compute the material response, and assess fatigue indicator parameters. The performance of PRISMS-Fatigue is benchmarked against a similar framework implemented using ABAQUS. Results indicate that the multilevel parallelism scheme of PRISMS-Fatigue is more efficient and scalable than ABAQUS for large-scale fatigue simulations. The performance and flexibility of this framework is demonstrated with various examples that assess the driving force for fatigue crack formation of microstructures with different crystallographic textures, grain morphologies, and grain numbers, and under different multiaxial strain states, strain magnitudes, and boundary conditions.

Beschreibung: Topological insulators (TIs) are considered as ideal platforms for generating large spin Hall conductivity (SHC), however, the bulk carrier problem, which is unavoidable in TIs, hinders their practical applications. Recently, topological semimetals (TSMs) have been proposed to achieve large SHC to replace TIs. However, the ideal TSM candidates with large SHC are still lacking. In terms of first-principles calculations, we predict that Ta3As family compounds exhibit complex crossing nodal-lines (CNL) properties in absence of the spin-orbit coupling (SOC). However, they transfer to Dirac TSMs under the influence of strong SOC, and present large SHC around Fermi level in particular. Remarkably, the SHC value of Ta3Y (Y = As, Sb, Bi) is around 1500–1700 $$(hbar /e)({mathrm{{Omega}}} cdot {mathrm{cm}})^{ - 1}$$ ( ħ / e ) ( Ω ⋅ cm ) − 1 , which is comparable to noble metal Pt and much larger than TIs, Weyl TSMs, and 4d/5d transition metals. Our work not only suggests a kind of TSM family with interesting Dirac CNL around Fermi level, but also paves the way for searching large intrinsic SHC materials in complex CNL TSM systems.

Beschreibung: A phase-field model is proposed to simulate coherency loss coupled with microstructure evolution. A special field variable is employed to describe the degree of coherency loss of each particle and its evolution is governed by a Ginzburg-Landau type kinetic equation. For the sake of computational efficiency, a flood-fill algorithm is introduced that can drastically reduce the required number of field variables, which allows the model to efficiently simulate a large number of particles sufficient for characterizing their statistical features during Ostwald ripening. The model can incorporate size dependence of coherency loss, metastability of coherent particles, and effectively incorporate the underlying mechanisms of coherency loss by introducing a so-called differential energy criterion. The model is applied to simulate coarsening of Al3Sc precipitates in aluminum alloy and comprehensively compared with experiments. Our results clearly show how the particle size distribution is changed during coherency loss and affects the coarsening rate.

Beschreibung: Machine learning methods offer great promise for fast and accurate detection and prognostication of coronavirus disease 2019 (COVID-19) from standard-of-care chest radiographs (CXR) and chest computed tomography (CT) images. Many articles have been published in 2020 describing new machine learning-based models for both of these tasks, but it is unclear which are of potential clinical utility. In this systematic review, we consider all published papers and preprints, for the period from 1 January 2020 to 3 October 2020, which describe new machine learning models for the diagnosis or prognosis of COVID-19 from CXR or CT images. All manuscripts uploaded to bioRxiv, medRxiv and arXiv along with all entries in EMBASE and MEDLINE in this timeframe are considered. Our search identified 2,212 studies, of which 415 were included after initial screening and, after quality screening, 62 studies were included in this systematic review. Our review finds that none of the models identified are of potential clinical use due to methodological flaws and/or underlying biases. This is a major weakness, given the urgency with which validated COVID-19 models are needed. To address this, we give many recommendations which, if followed, will solve these issues and lead to higher-quality model development and well-documented manuscripts.

Beschreibung: The Ge2Sb2Te5 alloy has served as the core material in phase-change memories with high switching speed and persistent storage capability at room temperature. However widely used, this composition is not suitable for embedded memories—for example, for automotive applications, which require very high working temperatures above 300 °C. Ge–Sb–Te alloys with higher Ge content, most prominently Ge2Sb1Te2 (‘212’), have been studied as suitable alternatives, but their atomic structures and structure–property relationships have remained widely unexplored. Here, we report comprehensive first-principles simulations that give insight into those emerging materials, located on the compositional tie-line between Ge2Sb1Te2 and elemental Ge, allowing for a direct comparison with the established Ge2Sb2Te5 material. Electronic-structure computations and smooth overlap of atomic positions (SOAP) similarity analyses explain the role of excess Ge content in the amorphous phases. Together with energetic analyses, a compositional threshold is identified for the viability of a homogeneous amorphous phase (‘zero bit’), which is required for memory applications. Based on the acquired knowledge at the atomic scale, we provide a materials design strategy for high-performance embedded phase-change memories with balanced speed and stability, as well as potentially good cycling capability.

Beschreibung: The accelerated growth rate of repository entries in crystallographic databases makes it arduous to identify and classify their prototype structures. The open-source AFLOW-XtalFinder package was developed to solve this problem. It symbolically maps structures into standard designations following the AFLOW Prototype Encyclopedia and calculates the internal degrees of freedom consistent with the International Tables for Crystallography. To ensure uniqueness, structures are analyzed and compared via symmetry, local atomic geometries, and crystal mapping techniques, simultaneously grouping them by similarity. The software (i) distinguishes distinct crystal prototypes and atom decorations, (ii) determines equivalent spin configurations, (iii) reveals compounds with similar properties, and (iv) guides the discovery of unexplored materials. The operations are accessible through a Python module ready for workflows, and through command line syntax. All the 4+ million compounds in the AFLOW.org repositories are mapped to their ideal prototype, allowing users to search database entries via symbolic structure-type. Furthermore, 15,000 unique structures — sorted by prevalence — are extracted from the AFLOW-ICSD catalog to serve as future prototypes in the Encyclopedia.

Beschreibung: The promise enabled by boron arsenide’s (BAs) high thermal conductivity (κ) in power electronics cannot be assessed without taking into account the reduction incurred when doping the material. Using first principles calculations, we determine the κ reduction induced by different group IV impurities in BAs as a function of concentration and charge state. We unveil a general trend, where neutral impurities scatter phonons more strongly than the charged ones. CB and GeAs impurities show by far the weakest phonon scattering and retain BAs κ values of over ~1000 W⋅K−1⋅m−1 even at high densities. Both Si and Ge achieve large hole concentrations while maintaining high κ. Furthermore, going beyond the doping compensation threshold associated to Fermi level pinning triggers observable changes in the thermal conductivity. This informs design considerations on the doping of BAs, and it also suggests a direct way to determine the onset of compensation doping in experimental samples.

Beschreibung: The enormous number of combinations of adsorbing molecules and porous materials that exist is known as adsorption space. The adsorption space for microporous polymers has not yet been systematically explored, especially when compared with efforts for crystalline adsorbents. We report molecular simulation data for the adsorptive and structural properties of polymers of intrinsic microporosity with a diverse set of adsorbate species with 345 distinct adsorption isotherms and over 240,000 fresh and swollen structures. These structures and isotherms were obtained using a sorption-relaxation technique that accounts for the critical role of flexibility of the polymeric adsorbents. This enables us to introduce a set of correlations that can estimate adsorbent swelling and fractional free volume dilation as a function of adsorbate uptake based on readily characterized properties. The separation selectivity of the 276 distinct binary molecular pairs in our data is reported and high-performing adsorbent systems are identified.

Beschreibung: In materials science, the discovery of recipes that yield nanomaterials with defined optical properties is costly and time-consuming. In this study, we present a two-step framework for a machine learning-driven high-throughput microfluidic platform to rapidly produce silver nanoparticles with the desired absorbance spectrum. Combining a Gaussian process-based Bayesian optimization (BO) with a deep neural network (DNN), the algorithmic framework is able to converge towards the target spectrum after sampling 120 conditions. Once the dataset is large enough to train the DNN with sufficient accuracy in the region of the target spectrum, the DNN is used to predict the colour palette accessible with the reaction synthesis. While remaining interpretable by humans, the proposed framework efficiently optimizes the nanomaterial synthesis and can extract fundamental knowledge of the relationship between chemical composition and optical properties, such as the role of each reactant on the shape and amplitude of the absorbance spectrum.

Beschreibung: Availability of affordable and widely applicable interatomic potentials is the key needed to unlock the riches of modern materials modeling. Artificial neural network-based approaches for generating potentials are promising; however, neural network training requires large amounts of data, sampled adequately from an often unknown potential energy surface. Here we propose a self-consistent approach that is based on crystal structure prediction formalism and is guided by unsupervised data analysis, to construct an accurate, inexpensive, and transferable artificial neural network potential. Using this approach, we construct an interatomic potential for carbon and demonstrate its ability to reproduce first principles results on elastic and vibrational properties for diamond, graphite, and graphene, as well as energy ordering and structural properties of a wide range of crystalline and amorphous phases.

Beschreibung: Metal halides have emerged as a new generation of semiconductors with applications ranging from solar cells to chemical sensors. We assess the thermoelectric potential of Cs3Cu2I5, which has a crystal structure formed of zero-dimensional [Cu2I5]3− anionic clusters that are separated by Cs+ counter cations. We find the compound exhibits the characteristics of a phonon-glass electron-crystal with a large imbalance in the conduction of heat and electrons predicted from first-principles transport theory. Strong anharmonic phonon–phonon scattering results in short-lived acoustic vibrations and an ultra-low lattice thermal conductivity (10 cm2 V−1 s−1). For an n-type crystal at 600 K, a thermoelectric figure-of-merit ZT of 2.6 is found to be accessible, which for a cold source of 300 K corresponds to a thermodynamic heat-to-electricity conversion efficiency of 15%.

Beschreibung: A number of large technology companies have created code-free cloud-based platforms that allow researchers and clinicians without coding experience to create deep learning algorithms. In this study, we comprehensively analyse the performance and featureset of six platforms, using four representative cross-sectional and en-face medical imaging datasets to create image classification models. The mean (s.d.) F1 scores across platforms for all model–dataset pairs were as follows: Amazon, 93.9 (5.4); Apple, 72.0 (13.6); Clarifai, 74.2 (7.1); Google, 92.0 (5.4); MedicMind, 90.7 (9.6); Microsoft, 88.6 (5.3). The platforms demonstrated uniformly higher classification performance with the optical coherence tomography modality. Potential use cases given proper validation include research dataset curation, mobile ‘edge models’ for regions without internet access, and baseline models against which to compare and iterate bespoke deep learning approaches.

Beschreibung: Recent experiments show that the chemical composition of body-centered cubic (bcc) refractory high entropy alloys (HEAs) can be tuned to enable transformation-induced plasticity (TRIP), which significantly improves the ductility of these alloys. This calls for an accurate and efficient method to map the structural stability as a function of composition. A key challenge for atomistic simulations is to separate the structural transformation between the bcc and the ω phases from the intrinsic local lattice distortions in such chemically disordered alloys. To solve this issue, we develop a method that utilizes a symmetry analysis to detect differences in the crystal structures. Utilizing this method in combination with ab initio calculations, we demonstrate that local lattice distortions largely affect the phase stability of Ti–Zr–Hf–Ta and Ti–Zr–Nb–Hf–Ta HEAs. If relaxation effects are properly taken into account, the predicted compositions near the bcc–hcp energetic equilibrium are close to the experimental compositions, for which good strength and ductility due to the TRIP effect are observed.

Beschreibung: The functionality of many materials is critically dependent on the integration of dissimilar components and on the interfaces that arise between them. The description of such heterogeneous components requires the development and deployment of first principles methods, coupled to appropriate dynamical descriptions of matter and advanced sampling techniques, in order to capture all the relevant length and time scales of importance to the materials’ performance. It is thus essential to build simple, streamlined computational schemes for the prediction and design of multiple properties of broad classes of materials, by developing interoperable codes which can be efficiently coupled to each other to perform complex tasks. We discuss the use of interoperable codes to simulate the structural and spectroscopic characterization of materials, including chemical reactions for catalysis, the description of defects for quantum information science, and heat and charge transport.

Beschreibung: Nonlinear optical properties, such as bulk photovoltaic effects, possess great potential in energy harvesting, photodetection, rectification, etc. To enable efficient light–current conversion, materials with strong photo-responsivity are highly desirable. In this work, we predict that monolayer Janus transition metal dichalcogenides (JTMDs) in the 1T′ phase possess colossal nonlinear photoconductivity owing to their topological band mixing, strong inversion symmetry breaking, and small electronic bandgap. 1T′ JTMDs have inverted bandgaps on the order of 10 meV and are exceptionally responsive to light in the terahertz (THz) range. By first-principles calculations, we reveal that 1T′ JTMDs possess shift current (SC) conductivity as large as 2300 nm μA V−2, equivalent to a photo-responsivity of 2800 mA/W. The circular current (CC) conductivity of 1T′ JTMDs is as large as ∼104 nm μA V−2. These remarkable photo-responsivities indicate that the 1T′ JTMDs can serve as efficient photodetectors in the THz range. We also find that external stimuli such as the in-plane strain and out-of-plane electric field can induce topological phase transitions in 1T′ JTMDs and that the SC can abruptly flip their directions. The abrupt change of the nonlinear photocurrent can be used to characterize the topological transition and has potential applications in 2D optomechanics and nonlinear optoelectronics.

Beschreibung: Wannier interpolation is a powerful tool for performing Brillouin zone integrals over dense grids of k points, which are essential to evaluate such quantities as the intrinsic anomalous Hall conductivity or Boltzmann transport coefficients. However, more complex physical problems and materials create harder numerical challenges, and computations with the existing codes become very expensive, which often prevents reaching the desired accuracy. In this article, I present a series of methods that boost the speed of Wannier interpolation by several orders of magnitude. They include a combination of fast and slow Fourier transforms, explicit use of symmetries, and recursive adaptive grid refinement among others. The proposed methodology has been implemented in the python code WannierBerri, which also aims to serve as a convenient platform for the future development of interpolation schemes for other phenomena.

Beschreibung: The repeated slab approach has become a de facto standard to accurately describe surface properties of materials by density functional theory calculations with periodic boundary conditions. For materials exhibiting spontaneous polarization, we show that the conventional scheme of passivation with pseudo hydrogen is unable to realize a charge-neutral surface. The presence of a net surface charge induces via Gauss’s law a macroscopic electric field through the slab and results in poor size convergence with respect to the thickness of the slab. We propose a modified passivation method that accounts for the effect of spontaneous polarization, describes the correct bulk limits and boosts convergence with respect to slab thickness. The robustness, reliability, and superior convergence of energetics and electronic structure achieved by the proposed method are demonstrated using the example of polar ZnO surfaces.

Beschreibung: The proximity to structural phase transitions in IV-VI thermoelectric materials is one of the main reasons for their large phonon anharmonicity and intrinsically low lattice thermal conductivity κ. However, the κ of GeTe increases at the ferroelectric phase transition near 700 K. Using first-principles calculations with the temperature dependent effective potential method, we show that this rise in κ is the consequence of negative thermal expansion in the rhombohedral phase and increase in the phonon lifetimes in the high-symmetry phase. Strong anharmonicity near the phase transition induces non-Lorentzian shapes of the phonon power spectra. To account for these effects, we implement a method of calculating κ based on the Green-Kubo approach and find that the Boltzmann transport equation underestimates κ near the phase transition. Our findings elucidate the influence of structural phase transitions on κ and provide guidance for design of better thermoelectric materials.

Beschreibung: The discovery and realization of topological insulators, a phase of matter which hosts metallic boundary states when the d-dimension insulating bulk is confined to (d − 1)-dimensions, led to several potential applications. Recently, it was shown that protected topological states can manifest in (d − 2)-dimensions, such as hinge and corner states for three- and two-dimensional systems, respectively. These nontrivial materials are named higher-order topological insulators (HOTIs). Here we show a connection between spin Hall effect and HOTIs using a combination of ab initio calculations and tight-binding modeling. The model demonstrates how a non-zero bulk midgap spin Hall conductivity (SHC) emerges within the HOTI phase. Following this, we performed high-throughput density functional theory calculations to find unknown HOTIs, using the SHC as a criterion. We calculated the SHC of 693 insulators resulting in seven stable two-dimensional HOTIs. Our work guides novel experimental and theoretical advances towards higher-order topological insulator realization and applications.

Beschreibung: The electronic structure, in particular the band edge position, of photocatalyst in presence of water is critical for photocatalytic water splitting. We propose a direct and systematic density functional theory (DFT) scheme to quantitatively predict band edge shifts and their microscopic origins for aqueous 2D photocatalyst, where thousands of atoms or more are able to be involved. This scheme is indispensable to correctly calculate the electronic structure of 2D photocatalyst in the presence of water, which is demonstrated in aqueous MoS2, GaS, InSe, GaSe and InS. It is found that the band edge of 2D photocatalysts are not rigidly shifted due to water as reported in previous studies of aqueous systems. Specifically, the CBM shift is quantitatively explained by geometric deformation, water dipole and charge redistribution effect while the fourth effect, i.e., interfacial chemical contact, is revealed in the VBM shift. Moreover, the revealed upshift of CBM in aqueous MoS2 should thermodynamically help carriers to participate in hydrogen evolution reaction (HER), which underpin the reported experimental findings that MoS2 is an efficient HER photocatalyst. Our work paves the way to design 2D materials in general as low-cost and high-efficiency photocatalysts.

Beschreibung: Here, we report a density functional theory (DFT)-based high-throughput screening method to successfully identify a type of alloy nanoclusters as the electrocatalyst for hydrogen evolution reaction (HER). Totally 7924 candidates of Cu-based alloy clusters of Cu55-nMn (M = Co, Ni, Ru, and Rh) are optimized and evaluated to screening for the promising catalysts. By comparing different structural patterns, Cu-based alloy clusters prefer the core–shell structures with the dopant metal in the core and Cu as the shell atoms. Generally speaking, the HER performance of the Cu-based nanoclusters can be significantly improved by doping transition metals, and the active sites are the bridge sites and three-fold sites on the outer-shell Cu atoms. Considering the structural stability and the electrochemical activity, core–shell CuNi alloy clusters are suggested to be the superior electrocatalyst for hydrogen evolution. A descriptor composing of surface charge is proposed to efficiently evaluate the HER activity of the alloy clusters supported by the DFT calculations and machine-learning techniques. Our screening strategy could accelerate the pace of discovery for promising HER electrocatalysts using metal alloy nanoclusters.

Beschreibung: We present a tensor-structured algorithm for efficient large-scale density functional theory (DFT) calculations by constructing a Tucker tensor basis that is adapted to the Kohn–Sham Hamiltonian and localized in real-space. The proposed approach uses an additive separable approximation to the Kohn–Sham Hamiltonian and an L1 localization technique to generate the 1-D localized functions that constitute the Tucker tensor basis. Numerical results show that the resulting Tucker tensor basis exhibits exponential convergence in the ground-state energy with increasing Tucker rank. Further, the proposed tensor-structured algorithm demonstrated sub-quadratic scaling with system-size for both systems with and without a gap, and involving many thousands of atoms. This reduced-order scaling has also resulted in the proposed approach outperforming plane-wave DFT implementation for systems beyond 2000 electrons.

Beschreibung: A three-dimensional phase-field model is developed to simulate grain evolutions during powder-bed-fusion (PBF) additive manufacturing, while the physically-informed temperature profile is implemented from a thermal-fluid flow model. The phase-field model incorporates a nucleation model based on classical nucleation theory, as well as the initial grain structures of powder particles and substrate. The grain evolutions during the three-layer three-track PBF process are comprehensively reproduced, including grain nucleation and growth in molten pools, epitaxial growth from powder particles, substrate and previous tracks, grain re-melting and re-growth in overlapping zones, and grain coarsening in heat-affected zones. A validation experiment has been carried out, showing that the simulation results are consistent with the experimental results in the molten pool and grain morphologies. Furthermore, the grain refinement by adding nanoparticles is preliminarily reproduced and compared against the experimental result in literature.

Beschreibung: Development of high-energy-density anode is crucial for practical application of Na-ion battery as a post Li-ion battery. Hard carbon (HC), though a promising anode candidate, still has bottlenecks of insufficient capacity and unclear microscopic picture. Usage of the micropore has been recently discussed, however, the underlying sodiation mechanism is still controversial. Herein we examined the origin for the high-capacity sodiation of HC, based on density functional theory calculations. We demonstrated that nanometer-size Na cluster with 3–6 layers is energetically stable between two sheets of graphene, a model micropore, in addition to the adsorption and intercalation mechanisms. The finding well explains the extended capacity over typical 300 mAhg−1, up to 478 mAhg−1 recently found in the MgO-templated HC. We also clarified that the MgO-template can produce suitable nanometer-size micropores with slightly defective graphitic domains in HC. The present study considerably promotes the atomistic theory of sodiation mechanism and complicated HC science.

Beschreibung: This study presents the formulation, the numerical solution, and the validation of a theoretical framework based on the concept of variable-order mechanics and capable of modeling dynamic fracture in brittle and quasi-brittle solids. More specifically, the reformulation of the elastodynamic problem via variable and fractional-order operators enables a unique and extremely powerful approach to model nucleation and propagation of cracks in solids under dynamic loading. The resulting dynamic fracture formulation is fully evolutionary, hence enabling the analysis of complex crack patterns without requiring any a priori assumption on the damage location and the growth path, and without using any algorithm to numerically track the evolving crack surface. The evolutionary nature of the variable-order formalism also prevents the need for additional partial differential equations to predict the evolution of the damage field, hence suggesting a conspicuous reduction in complexity and computational cost. Remarkably, the variable-order formulation is naturally capable of capturing extremely detailed features characteristic of dynamic crack propagation such as crack surface roughening as well as single and multiple branching. The accuracy and robustness of the proposed variable-order formulation are validated by comparing the results of direct numerical simulations with experimental data of typical benchmark problems available in the literature.

Beschreibung: Accurate theoretical predictions of desired properties of materials play an important role in materials research and development. Machine learning (ML) can accelerate the materials design by building a model from input data. For complex datasets, such as those of crystalline compounds, a vital issue is how to construct low-dimensional representations for input crystal structures with chemical insights. In this work, we introduce an algebraic topology-based method, called atom-specific persistent homology (ASPH), as a unique representation of crystal structures. The ASPH can capture both pairwise and many-body interactions and reveal the topology-property relationship of a group of atoms at various scales. Combined with composition-based attributes, ASPH-based ML model provides a highly accurate prediction of the formation energy calculated by density functional theory (DFT). After training with more than 30,000 different structure types and compositions, our model achieves a mean absolute error of 61 meV/atom in cross-validation, which outperforms previous work such as Voronoi tessellations and Coulomb matrix method using the same ML algorithm and datasets. Our results indicate that the proposed topology-based method provides a powerful computational tool for predicting materials properties compared to previous works.

Beschreibung: Dzyaloshinskii–Moriya interaction (DMI) is considered as one of the most important energies for specific chiral textures such as magnetic skyrmions. The keys of generating DMI are the absence of structural inversion symmetry and exchange energy with spin–orbit coupling. Therefore, a vast majority of research activities about DMI are mainly limited to heavy metal/ferromagnet bilayer systems, only focusing on their interfaces. Here, we report an asymmetric band formation in a superlattices (SL) which arises from inversion symmetry breaking in stacking order of atomic layers, implying the role of bulk-like contribution. Such bulk DMI is more than 300% larger than simple sum of interfacial contribution. Moreover, the asymmetric band is largely affected by strong spin–orbit coupling, showing crucial role of a heavy metal even in the non-interfacial origin of DMI. Our work provides more degrees of freedom to design chiral magnets for spintronics applications.

Beschreibung: Fluid flow in heterogeneous porous media arises in many systems, from biological tissues to composite materials, soil, wood, and paper. With advances in instrumentations, high-resolution images of porous media can be obtained and used directly in the simulation of fluid flow. The computations are, however, highly intensive. Although machine learning (ML) algorithms have been used for predicting flow properties of porous media, they lack a rigorous, physics-based foundation and rely on correlations. We introduce an ML approach that incorporates mass conservation and the Navier–Stokes equations in its learning process. By training the algorithm to relatively limited data obtained from the solutions of the equations over a time interval, we show that the approach provides highly accurate predictions for the flow properties of porous media at all other times and spatial locations, while reducing the computation time. We also show that when the network is used for a different porous medium, it again provides very accurate predictions.

Beschreibung: Quaternary chalcogenide semiconductors (I2-II-IV-X4) are key materials for thin-film photovoltaics (PVs) to alleviate the energy crisis. Scaling up of PVs requires the discovery of I2-II-IV-X4 with good photoelectric properties; however, the structure search space is significantly large to explore exhaustively. The scarcity of available data impedes even many machine learning (ML) methods. Here, we employ the unsupervised learning (UL) method to discover I2-II-IV-X4 that alleviates the challenge of data scarcity. We screen all the I2-II-IV-X4 from the periodic table as the initial data and finally select eight candidates through UL. As predicted by ab initio calculations, they exhibit good optical conversion efficiency, strong optical responses, and good thermal stabilities at room temperatures. This typical case demonstrates the potential of UL in material discovery, which overcomes the limitation of data scarcity, and shortens the computational screening cycle of I2-II-IV-X4 by ~12.1 years, providing a research avenue for rapid material discovery.

Beschreibung: The superoxide disproportionation reaction is a key step in the chemistry of aprotic metal oxygen batteries that controls the peroxide formation upon discharge and opens the way for singlet oxygen release. Here we clarify the energy landscape of the disproportionation of superoxide in aprotic media catalyzed by group 1A cations. Our analysis is based on ab initio multireference computational methods and unveils the competition between the expected reactive path leading to peroxide and an unexpected reaction channel that involves the reduction of the alkaline ion. Both channels lead to the release of triplet and singlet O2. The existence of this reduction channel not only facilitates singlet oxygen release but leads to a reactive neutral solvated species that can onset parasitic chemistries due to their well-known reducing properties. Overall, we show that the application of moderate overpotentials makes both these channels accessible in aprotic batteries.

Beschreibung: The universal mathematical form of machine-learning potentials (MLPs) shifts the core of development of interatomic potentials to collecting proper training data. Ideally, the training set should encompass diverse local atomic environments but conventional approaches are prone to sampling similar configurations repeatedly, mainly due to the Boltzmann statistics. As such, practitioners handpick a large pool of distinct configurations manually, stretching the development period significantly. To overcome this hurdle, methods are being proposed that automatically generate training data. Herein, we suggest a sampling method optimized for gathering diverse yet relevant configurations semi-automatically. This is achieved by applying the metadynamics with the descriptor for the local atomic environment as a collective variable. As a result, the simulation is automatically steered toward unvisited local environment space such that each atom experiences diverse chemical environments without redundancy. We apply the proposed metadynamics sampling to H:Pt(111), GeTe, and Si systems. Throughout these examples, a small number of metadynamics trajectories can provide reference structures necessary for training high-fidelity MLPs. By proposing a semi-automatic sampling method tuned for MLPs, the present work paves the way to wider applications of MLPs to many challenging applications.

Beschreibung: Solid-state refrigeration which is environmentally benign has attracted considerable attention. Mechanocaloric (mC) materials, in which the phase transitions can be induced by mechanical stresses, represent one of the most promising types of solid-state caloric materials. Herein, we have developed a thermodynamic phenomenological model and predicted extraordinarily large elastocaloric (eC) strengths for the (111)-oriented metal-free perovskite ferroelectric [MDABCO](NH4)I3 thin-films. The predicted room temperature isothermal eC ΔSeC/Δσ (eC entropy change under unit stress change) and adiabatic eC ΔTeC/Δσ (eC temperature change under unit stress change) for [MDABCO](NH4)I3 are −60.0 J K−1 kg−1 GPa−1 and 17.9 K GPa−1, respectively, which are 20 times higher than the traditional ferroelectric oxides such as BaTiO3 thin films. We have also demonstrated that the eC performance can be improved by reducing the Young’s modulus or enhancing the thermal expansion coefficient (which could be realized through chemical doping, etc.). We expect these discoveries to spur further interest in the potential applications of metal-free organic ferroelectrics materials towards next-generation eC refrigeration devices.

Beschreibung: Lorentz transmission electron microscopy is an advanced characterization technique that enables the simultaneous imaging of both the microstructure and functional properties of materials. Information such as magnetization and electric potentials is carried by the phase of the electron wave, and is lost during image acquisition. Various methods have been proposed to retrieve the phase of the electron wavefunction using intensities of the acquired images, most of which work only in the small defocus limit. Imaging at strong defoci not only carries more quantitative phase information, but is essential to the study of weak magnetic and electrostatic fields at the nanoscale. In this work we develop a method based on differentiable programming to solve the inverse problem of phase retrieval. We show that our method maintains a high spatial resolution and robustness against noise even at the upper defocus limit of the microscope. More importantly, our proposed method can go beyond recovering just the phase information. We demonstrate this by retrieving the electron-optical parameters of the contrast transfer function alongside the electron exit wavefunction.

Beschreibung: The automated stopping of a spectral measurement with active learning is proposed. The optimal stopping of the measurement is realised with a stopping criterion based on the upper bound of the posterior average of the generalisation error of the Gaussian process regression. It is revealed that the automated stopping criterion of the spectral measurement gives an approximated X-ray absorption spectrum with sufficient accuracy and reduced data size. The proposed method is not only a proof-of-concept of the optimal stopping problem in active learning but also the key to enhancing the efficiency of spectral measurements for high-throughput experiments in the era of materials informatics.

Beschreibung: In this work, we demonstrate that damage mechanism identification from acoustic emission (AE) signals generated in minicomposites with elastically similar constituents is possible. AE waveforms were generated by SiC/SiC ceramic matrix minicomposites (CMCs) loaded under uniaxial tension and recorded by four sensors (two models with each model placed at two ends). Signals were encoded with a modified partial power scheme and subsequently partitioned through spectral clustering. Matrix cracking and fiber failure were identified based on the frequency information contained in the AE event they produced, despite the similar constituent elastic properties of the matrix and fiber. Importantly, the resultant identification of AE events closely followed CMC damage chronology, wherein early matrix cracking is later followed by fiber breaks, even though the approach is fully domain-knowledge agnostic. Additionally, the partitions were highly precise across both the model and location of the sensors, and the partitioning was repeatable. The presented approach is promising for CMCs and other composite systems with elastically similar constituents.

Beschreibung: The discovery of substrate materials has been dominated by trial and error, opening the opportunity for a systematic search. We generate bonding networks for materials from the Materials Project and systematically break up to three bonds in the networks for three-dimensional crystals. Successful cleavage reduces the bonding network to two periodic dimensions. We identify 4693 symmetrically unique cleavage surfaces across 2133 bulk crystals, 4626 of which have a maximum Miller index of one. We characterize the likelihood of cleavage by creating monolayers of these surfaces and calculating their thermodynamic stability using density functional theory to discover 3991 potential substrates. Following, we identify distinct trends in the work of cleavage and relate them to bonding in the three-dimensional precursor. We illustrate the potential impact of the substrate database by identifying several improved epitaxial substrates for the transparent conductor BaSnO3. The open-source databases of predicted and commercial substrates are available at MaterialsWeb.org.