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Materials Science (cond-mat.mtrl-sci)

Thu, 04 May 2023

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1.Ultra-high-density double-atom catalyst with spin moment as activity descriptor for oxygen reduction reaction

Authors:Peng Lv, Wenjing Lv, Donghai Wu, Gang Tang, Xunwang Yan, Zhansheng Lu, Dongwei Ma

Abstract: One of the great challenges facing atomically dispersed catalysts, including single-atom catalyst (SAC) and double-atom catalyst (DAC) is their ultra-low metal loading (typically less than 5 wt%), basically limiting the practical catalytic application, such as oxygen reduction reaction (ORR) crucial to hydrogen fuel cell and metal-air battery. Although some important progresses have been achieved on ultra-high-density (UHD) SACs, the reports on UHD-DACs with stable uniform dispersion is still lacking. Herein, based on the experimentally synthesized M2N6 motif (M = Sc-Zn), we theoretically demonstrated the existence of the UHD-DACs with the metal loading > 40 wt%, which were confirmed by systematic analysis of dynamic, thermal, mechanical, thermodynamic, and electrochemical stabilities. Furthermore, ORR activities of the UHD-DACs are comparable with or even better than those of the experimentally synthesized low-density (LD) counterparts, and the Fe2N6 and Co2N6 UHD-DACs locate at the peak of the activity volcano with ultra-low overpotentials of 0.31 and 0.33 V, respectively. Finally, spin magnetic moment of active center is found to be a catalytic descriptor for ORR on the DACs. Our work will stimulate the experimental exploration of the ultra-high-density DACs and provides the novel insight into the relationship between ORR activity of the DACs and their spin states.

2.Accelerated Screening of Ternary Chalcogenides for High-Performance Optoelectronic Materials

Authors:Chen Shen, Tianshu Li, Yixuan Zhang, Teng Long, Nuno Miguel Fortunato, Fei Liang, Mian Dai, Jiahong Shen, Chris Wolverton, Hongbin Zhang

Abstract: Chalcogenides, which refer to chalcogen anions, have attracted considerable attention in multiple fields of applications, such as optoelectronics, thermoelectrics, transparent contacts, and thin film transistors. In comparison to oxide counterparts, chalcogenides have demonstrated higher mobility and \textit{p}-type dopability, owing to larger orbital overlaps between metal-X covalent chemical bondings and higher-energy valence bands derived by p-orbitals. Despite the potential of chalcogenides, the number of successfully synthesized compounds remains relatively low compared to oxides, suggesting the presence of numerous unexplored chalcogenides with fascinating physical characteristics. In this study, we implemented a systematic high-throughput screening process combined with first-principles calculations on ternary chalcogenides using 34 crystal structure prototypes. We generated a computational material database containing over 400,000 compounds by exploiting the ion-substitution approach at different atomic sites with elements in the periodic table. The thermodynamic stabilities of the candidates were validated using the chalcogenides included in the Open Quantum Materials Database. Moreover, we trained a model based on Crystal Graph Convolutional Neural Networks to predict the thermodynamic stability of novel materials. Furthermore, we theoretically evaluated the electronic structures of the stable candidates using accurate hybrid functionals. A series of in-depth characteristics, including the carrier effective masses, electronic configuration, and photovoltaic conversion efficiency, was also investigated. Our work provides useful guidance for further experimental research in the synthesis and characterization of such chalcogenides as promising candidates, as well as charting the stability and optoelectronic performance of ternary chalcogenides.

3.Influence of high pressure on the remarkable itinerant electron behaviour in Y$_{0.7}$Er$_{0.3}$Fe$_2$D$_{4.2}$ compounds

Authors:Z. Arnold, O. Isnard, V. Paul-Boncour

Abstract: Monoclinic Y$_{0.7}$Er$_{0.3}$Fe$_2$D$_{4.2}$ compound exhibits unusual magnetic properties with different field induced magnetic transitions. The deuteride is ferrimagnetic at low temperature and the Er and Fe sublattices present magnetic transitions at different temperatures. The Er moments are ordered below T$_{Er}$=55 K, whereas the Fe moments remain ferromagnetically coupled up to T$_{M0}$ = 66 K. At T$_{M0}$ the Fe moments display a sharp ferromagnetic-antiferromagnetic transition (FM-AFM) through an itinerant electron metamagnetic (IEM) behaviour very sensitive to any volume change. Y$_{0.7}$Er$_{0.3}$Fe$_2$D$_{4.2}$ becomes paramagnetic above T$_N$=125 K. The pressure dependence of T$_{Er}$ and T$_{M0}$ have been extracted from magnetic measurements under hydrostatic pressure up to 0.49 GPa. Both temperatures decrease linearly upon applied pressure with dT$_{Er}$/dP=-126 and dTM0/dP=-140 K.GPa$^{-1}$ for a field of B=0.03 T. Both magnetic Er and ferromagnetic Fe order disappear at P=0.44(4) GPa. However, under a larger applied field B=5 T, dT$_{M0}$/dP=-156 K.GPa$^{-1}$ whereas dT$_{Er}$/dP=-134 K.GPa$^{-1}$ showing a weaker sensitivity to pressure and magnetic field. At 2 K the decrease of the saturation magnetization under pressure can be attributed to a reduction of the mean Er moment due to canting and/or crystal field effect. Above T$_{M0}$ the magnetization curves display a metamagnetic behaviour from AFM to FM state, which is also very sensitive to the applied pressure. The transition field B$_{trans}$, which increases linearly upon heating, is shifted to lower temperature upon applied pressure with dT=-17 K between 0 and 0.11 GPa. These results show a strong decoupling of the Er and Fe magnetic sublattices versus temperature, applied field and pressure.

4.Structural, Vibrational, and Electronic Behavior of Two GaGeTe Polymorphs under compression

Authors:Enrico Bandiello, Samuel Gallego-Parra, Akun Liang, J. A. Sans, Vanesa Cuenca-Gotor, Estelina Lora da Silva, Rosario Vilaplana, Plácida. Rodríguez-Hernández, Alfonso Muñoz, Daniel Diaz-Anichtchenko, Catalin Popescu, Frederico Gil Alabarse, Carlos Rudamas, Čestmír Drašar, Alfredo Segura, Daniel Errandonea, F. J. Manjón

Abstract: GaGeTe is a layered topological semimetal that has been recently found to exist in at least two different polytypes, $\alpha$-GaGeTe ($R\bar{3}m$) and $\beta$-GaGeTe ($P6_3 mc$). Here we report a joint experimental and theoretical study of the structural, vibrational, and electronic properties of these two polytypes at high pressure. Both polytypes show anisotropic compressibility and two phase transitions, above 7 and 15 GPa, respectively, as confirmed by XRD and Raman spectroscopy measurements. Although the nature of the high-pressure phases is not confirmed, comparison with other chalcogenides and total-energy calculations allow us to propose possible high-pressure phases for both polytypes with an increase in coordination for Ga and Ge atoms from 4 to 6. In particular, the simplification of the X-ray patterns for both polytypes above 15 GPa suggests a transition to a structure of relatively higher symmetry than the original one. This result is consistent with the rocksalt-like high-pressure phases observed in parent III-VI semiconductors, such as GaTe, GaSe, and InSe. Pressure-induced amorphization is observed upon pressure release. The electronic band structures of $\alpha$-GaGeTe and $\beta$-GaGeTe and their pressure dependence also show similarities to III-VI semiconductors, thus suggesting that the germanene-like sublayer induces a semimetallic character in both GaGeTe polytypes. Above 3 GPa, both polytypes lose their topological features, due to the opening of the direct band gap, while the reduction of the interlayer space increases the thermal conductivity at high pressure.

5.Study of novel properties of graphene-ZnO heterojunction interface using density functional theory

Authors:H. D. Etea, K. N. Nigussa

Abstract: Studies of the structural, electronic, and optical characteristics of the interfaces between graphene and ZnO polar surfaces is carried out using first-principles simulations. At the interface, a strong van der Waals force is present, and because of the different work functions of graphene and ZnO, charge transfer takes place. Graphene's superior conductivity is not impacted by its interaction with ZnO, since its Dirac point is unaffected despite its adsorption on ZnO. In hybrid systems, excited electrons with energies between 0 and 3 eV (above Fermi energy) are primarily accumulated on graphene. The calculations offer a theoretical justification for the successful operation of graphene / ZnO hybrid materials as photocatalysts and solar cells. ZnO semiconductor is found to be a suitable material with modest band gap, ($\sim$ 3 eV), having high transparency in visible region and a high optical conductivity.

6.A Hybrid-DFT Study of Intrinsic Point Defects in $MX_2$ ($M$=Mo, W; $X$=S, Se) Monolayers

Authors:Alaa Akkoush, Yair Litman, Mariana Rossi

Abstract: Defects can strongly influence the electronic, optical and mechanical properties of 2D materials, making defect stability under different thermodynamic conditions crucial for material-property engineering. In this paper, we present an account of the structural and electronic characteristics of point defects in monolayer transition metal dichalcogenides $MX_2$ with $M$=Mo/W and $X$= S/Se, calculated with density-functional theory using the hybrid HSE06 exchange correlation functional including many-body dispersion corrections. For the simulation of charged defects, we employ a charge compensation scheme based on the virtual crystal approximation (VCA). We relate the stability and the electronic structure of charged vacancy defects in monolayer MoS$_2$ to an explicit calculation of the S monovacancy in MoS$_2$ supported on Au(111), and find convincing indication that the defect is negatively charged. Moreover, we show that the finite-temperature vibrational contributions to the free energy of defect formation can change the stability transition between adatoms and monovacancies by 300--400 K. Finally, we probe defect vibrational properties by calculating a tip-enhanced Raman scattering image of a vibrational mode of a MoS$_2$ cluster with and without an S monovacancy.

7.Magnetic properties of Nd6Fe13Cu single crystals

Authors:Jianing Liu, Ruiwen Xie, Alex Aubert, Lukas Schäfer, Hongbin Zhang, Oliver Gutfleisch, Konstantin Skokov

Abstract: The understanding of coercivity mechanism in high performance Nd-Fe-B permanent magnets relies on the analysis of the magnetic properties of all phases present in the magnets. By adding Cu in such compounds, a new Nd6Fe13Cu grain boundary phase is formed, however, the magnetic properties of this phase and its role in the magnetic decoupling of the matrix Nd2Fe14B grains are still insufficiently studied. In this work, we have grown Nd6Fe13Cu single crystals by the reactive flux method and studied their magnetic properties in detail. It is observed that below the N\'eel temperature (TN = 410 K), the Nd6Fe13Cu is antiferromagnetic in zero magnetic field; whereas when a magnetic field is applied along the a-axis, a spin-flop transition occurs at approx. 6 T, indicating a strong competition between antiferromagnetic and ferromagnetic interactions in two Nd layers below and above the Cu layers. Our atomistic spin dynamics simulation confirms that an increase in temperature and/or magnetic field can significantly change the antiferromagnetic coupling between the two Nd layers below and above the Cu layers, which, in turn, is the reason for the observed spin-flop transition. These results suggest that the role of antiferromagnetic Nd6Fe13Cu grain boundary phase in the coercivity enhancement of Nd-Fe-B-Cu magnets is more complex than previously thought, mainly due to the competition between its antiferro- and ferro-magnetic exchange interactions.

8.Bridging Theory with Experiment: Digital Twins and Deep Learning Segmentation of Defects in Monolayer MX2 Phases

Authors:Addis S. Fuhr, Panchapakesan Ganesh, Rama K. Vasudevan, Bobby G. Sumpter

Abstract: Developing methods to understand and control defect formation in nanomaterials offers a promising route for materials discovery. Monolayer MX2 phases represent a particularly compelling case for defect engineering of nanomaterials due to the large variability in their physical properties as different defects are introduced into their structure. However, effective identification and quantification of defects remains a challenge even as high-throughput scanning tunneling electron microscopy (STEM) methods improve. This study highlights the benefits of employing first principles calculations to produce digital twins for training deep learning segmentation models for defect identification in monolayer MX2 phases. Around 600 defect structures were obtained using density functional theory calculations, with each monolayer MX2 structure being subjected to multislice simulations for the purpose of generating the digital twins. Several deep learning segmentation architectures were trained on this dataset, and their performances evaluated under a variety of conditions such as recognizing defects in the presence of unidentified impurities, beam damage, grain boundaries, and with reduced image quality from low electron doses. This digital twin approach allows benchmarking different deep learning architectures on a theory dataset, which enables the study of defect classification under a broad array of finely controlled conditions. It thus opens the door to resolving the underpinning physical reasons for model shortcomings, and potentially chart paths forward for automated discovery of materials defect phases in experiments.

9.Ultrahigh oxygen ion mobility in ferroelectric hafnia

Authors:Liyang Ma, Jing Wu, Tianyuan Zhu, Yiwei Huang, Qiyang Lu, Shi Liu

Abstract: Ferroelectrics and ionic conductors are important functional materials, each supporting a plethora of applications in information and energy technology. The underlying physics governing their functional properties is ionic motion, and yet studies of ferroelectrics and ionic conductors are often considered separate fields. Based on first-principles calculations and deep-learning-assisted large-scale molecular dynamics (MD) simulations, we report ferroelectric-switching-promoted oxygen ion transport in HfO2, a wide-band-gap insulator with both ferroelectricity and ionic conductivity. Applying a unidirectional bias can activate multiple switching pathways in ferroelectric HfO2, leading to polar-antipolar phase cycling that appears to contradict classical electrodynamics. This apparent conflict is resolved by the geometric-quantum-phase nature of electric polarization that carries no definite direction. Our MD simulations demonstrate bias-driven successive ferroelectric transitions facilitate ultrahigh oxygen ion mobility at moderate temperatures, highlighting the potential of combining ferroelectricity and ionic conductivity for the development of advanced materials and technologies.

10.Quantifying the magnetic interactions governing chiral spin textures using deep neural networks

Authors:Jian Feng Kong, Yuhua Ren, M. S. Nicholas Tey, Pin Ho, Khoong Hong Khoo, Xiaoye Chen, Anjan Soumyanarayanan

Abstract: The interplay of magnetic interactions in chiral multilayer films gives rise to nanoscale topological spin textures, which form attractive elements for next-generation computing. Quantifying these interactions requires several specialized, time-consuming, and resource-intensive experimental techniques. Imaging of ambient domain configurations presents a promising avenue for high-throughput extraction of the parent magnetic interactions. Here we present a machine learning-based approach to determine the key interactions -- symmetric exchange, chiral exchange, and anisotropy -- governing chiral domain phenomenology in multilayers. Our convolutional neural network model, trained and validated on over 10,000 domain images, achieved $R^2 > 0.85$ in predicting the parameters and independently learned physical interdependencies between them. When applied to microscopy data acquired across samples, our model-predicted parameter trends are consistent with independent experimental measurements. These results establish ML-driven techniques as valuable, high-throughput complements to conventional determination of magnetic interactions, and serve to accelerate materials and device development for nanoscale electronics.

11.Effective rectification of THz electromagnetic fields in a ferrimagnetic iron garnet

Authors:T. G. H. Blank, E. A. Mashkovich, K. A. Grishunin, C. Schippers, M. V. Logunov, B. Koopmans, A. K. Zvezdin, A. V. Kimel

Abstract: It is found that single-cycle THz electromagnetic fields efficiently excite a GHz spin resonance mode in ferrimagnetic Tm$_3$Fe$_5$O$_{12}$, despite the near absence of GHz spectral components in the exciting THz pulse. By analyzing how the efficiency of excitation depends on the orientation and strength of the THz electric field, we show that it can be explained in terms of the nonlinear THz inverse Cotton-Mouton effect. Here, the THz electric field gets effectively rectified and acts on the ferrimagnetic spins as a uni-polar effective magnetic field pulse. This interpretation is confirmed by a theoretical model based on the phenomenological analysis of the effective magnetic field, combined with the equations of motion derived from the effective Lagrangian for a ferrimagnet. Moreover, by using the outcome of two-dimensional THz spectroscopy, we conjecture a quantum-mechanical interpretation of the observed effect in terms of stimulated Raman scattering of THz photons by the crystal-field split f-f electronic transitions of Tm$^{3+}$.

12.Accelerating GW calculations through machine learned dielectric matrices

Authors:Mario G. Zauchner, Andrew Horsfield, Johannes Lischner

Abstract: The GW approach produces highly accurate quasiparticle energies, but its application to large systems is computationally challenging, which can be largely attributed to the difficulty in computing the inverse dielectric matrix. To address this challenge, we develop a machine learning approach to efficiently predict density-density response functions (DDRF) in materials. For this, an atomic decomposition of the DDRF is introduced as well as the neighbourhood density-matrix descriptor both of which transform in the same way under rotations. The resulting DDRFs are then used to evaluate quasiparticle energies via the GW approach. This technique is called the ML-GW approach. To assess the accuracy of this method, we apply it to hydrogenated silicon clusters and find that it reliably reproduces HOMO-LUMO gaps and quasiparticle energy levels. The accuracy of the predictions deteriorates when the approach is applied to larger clusters than those included in the training set. These advances pave the way towards GW calculations of complex systems, such as disordered materials, liquids, interfaces and nanoparticles.