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Register a new userStructure-Composition-Property Relationships in Antiperovskite Nitrides
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ABX3 perovskites have attracted intensive research interest in recent years due to their versatile composition and superior optoelectronic properties. Their counterparts, antiperovskites (X3BA), can be viewed as electronically inverted perovskite derivatives, but they have not been extensively studied for solar applications. Therefore, understanding their composition-property relationships is crucial for future photovoltaic application. Here, taking six antiperovskite nitrides X3NA (X2+ = Mg, Ca, Sr; A3- = P, As, Sb, Bi) as an example, we investigate the effect of X- and A-sites on the electronic, dielectric, and mechanical properties from the viewpoint of the first-principles calculations. Our calculation results show that the X-site dominates the conduction band, and the A-site has a non-negligible contribution to the band edge. These findings are completely different from traditional halide perovskites. Interestingly, when changing X- or A-site elements, a linear relationship between the tolerance factor and physical quantities, such as electronic parameters, dielectric constants, and Youngs modulus, is observed. By designing the Mg3NAs1-xBix alloys, we further verify this power of the linear relationship, which provides a predictive guidance for experimental preparation of antiperovskite alloys. Finally, we make a comprehensive comparison between the antiperovskite nitrides and conventional halide perovskites for pointing out the future device applications.
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Asceding interest of the scientific community in layered hybrid halide perovskites (LHHPs) as materials for innovative photovoltaic and optoelectronic applications led to unprecedented expansion of this family of compounds, reaching now several hundred refined structures. Despite the unique structural diversity of LHHPs, traditional approaches of describing their structures, such as dividing into Dion-Jacobson (DJ) or Ruddlesden-Popper (RP) phases for mostt structures are ambiguous and unquantifiable. Here, we introduced a quantitative layer shift factor (LSF) for a univocal classification and quantitative comparison of the structures. We also developed an algorithm for automatic calculation of the LSF for such structures. We demonstrate the application of the proposed approach for an analysis of correlations between LSF and band gap to reveal structure-property relationships. Our study gives a simple and useful approach to classify of either the layered perovskite-like structures or other layered compounds composed of layers of vertex-connected octahedra as a structural unit.
The anomalous Nernst effect (ANE) - the generation of a transverse electric voltage by a longitudinal heat current in conducting ferromagnets or antiferromagnets - is an appealing approach for thermoelectric power generation in spin caloritronics. The ANE in antiferromagnets is particularly convenient for the fabrication of highly efficient and densely integrated thermopiles as lateral configurations of thermoelectric modules increase the coverage of heat source without suffering from the stray fields that are intrinsic to ferromagnets. In this work, using first-principles calculations together with a group theory analysis, we systematically investigate the spin order-dependent ANE in noncollinear antiferromagnetic Mn-based antiperovskite nitrides Mn$_{3}X$N ($X$ = Ga, Zn, Ag, and Ni). The ANE in Mn$_{3}X$N is forbidden by symmetry in the R1 phase but amounts to its maximum value in the R3 phase. Among all Mn$_{3}X$N compounds, Mn$_{3}$NiN presents the most significant anomalous Nernst conductivity of 1.80 AK$^{-1}$m$^{-1}$ at 200 K, which can be further enhanced if strain, electric, or magnetic fields are applied. The ANE in Mn$_{3}$NiN, being one order of magnitude larger than that in the famous Mn$_{3}$Sn, is the largest one discovered in antiferromagnets so far. The giant ANE in Mn$_{3}$NiN originates from the sharp slope of the anomalous Hall conductivity at the Fermi energy, which can be understood well from the Mott relation. Our findings provide a novel host material for realizing antiferromagnetic spin caloritronics which promises exciting applications in energy conversion and information processing.
Emergent functionalities of structural and topological defects in ferroelectric materials underpin an extremely broad spectrum of applications ranging from domain wall electronics to high dielectric and electromechanical responses. Many of these have been discovered and quantified via local scanning probe microscopy methods. However, the search for these functionalities has until now been based by either trial and error or using auxiliary information such as topography or domain wall structure to identify potential objects of interest based on the intuition of operator or preexisting hypotheses, with subsequent manual exploration. Here, we report the development and implementation of a machine learning framework that actively discovers relationships between local domain structure and polarization switching characteristics in ferroelectric materials encoded in the hysteresis loop. The latter and descriptors such as nucleation bias, coercive bias, hysteresis loop area, or more complex functionals of hysteresis loop shape and corresponding uncertainties are used to guide the discovery via automated piezoresponse force microscopy (PFM) and spectroscopy experiments. As such, this approach combines the power of machine learning methods to learn the correlative relationships between high dimensional data, and human-based physics insights encoded in the acquisition function. For ferroelectric, this automated workflow demonstrates that the discovery path and sampling points of on-field and off-field hysteresis loops are largely different, indicating the on-field and off-field hysteresis loops are dominated by different mechanisms. The proposed approach is universal and can be applied to a broad range of modern imaging and spectroscopy methods ranging from other scanning probe microscopy modalities to electron microscopy and chemical imaging.
Electric-field control of magnetization promises to substantially enhance the energy efficiency of device applications ranging from data storage to solid-state cooling. However, the intrinsic linear magnetoelectric effect is typically small in bulk materials. In thin films electric-field tuning of spin-orbit interaction phenomena (e.g., magnetocrystalline anisotropy) has been reported to achieve a partial control of the magnetic state. Here we explore the piezomagnetic effect (PME), driven by frustrated exchange interactions, which can induce a net magnetization in an antiferromagnet and reverse its direction via elastic strain generated piezoelectrically. Our $ab~initio$ study of PME in Mn-antiperovskite nitrides identified an extraordinarily large PME in Mn$_3$SnN available at room temperature. We explain the magnitude of PME based on features of the electronic structure and show an inverse-proportionality between the simulated zero-temperature PME and the negative thermal expansion at the magnetic (Neel) transition measured by Takenaka et al. in 9 antiferromagnetic Mn$_3$AN systems.
Machine learning (ML) of quantum mechanical properties shows promise for accelerating chemical discovery. For transition metal chemistry where accurate calculations are computationally costly and available training data sets are small, the molecular representation becomes a critical ingredient in ML model predictive accuracy. We introduce a series of revised autocorrelation functions (RACs) that encode relationships between the heuristic atomic properties (e.g., size, connectivity, and electronegativity) on a molecular graph. We alter the starting point, scope, and nature of the quantities evaluated in standard ACs to make these RACs amenable to inorganic chemistry. On an organic molecule set, we first demonstrate superior standard AC performance to other presently-available topological descriptors for ML model training, with mean unsigned errors (MUEs) for atomization energies on set-aside test molecules as low as 6 kcal/mol. For inorganic chemistry, our RACs yield 1 kcal/mol ML MUEs on set-aside test molecules in spin-state splitting in comparison to 15-20x higher errors from feature sets that encode whole-molecule structural information. Systematic feature selection methods including univariate filtering, recursive feature elimination, and direct optimization (e.g., random forest and LASSO) are compared. Random-forest- or LASSO-selected subsets 4-5x smaller than RAC-155 produce sub- to 1-kcal/mol spin-splitting MUEs, with good transferability to metal-ligand bond length prediction (0.004-5 {AA} MUE) and redox potential on a smaller data set (0.2-0.3 eV MUE). Evaluation of feature selection results across property sets reveals the relative importance of local, electronic descriptors (e.g., electronegativity, atomic number) in spin-splitting and distal, steric effects in redox potential and bond lengths.
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