A$_{2}$BX$_{6}$ vacancy-ordered double perovskites (VODPs) have captured substantial research interest in the scientific community as they offer environmentally friendly and stable alternatives to lead halide perovskites. In this study, we investigate Rb$_{2}$BCl$_{6}$ (B = Ti, Se, Ru, Pd) VODPs as promising optoelectronic materials employing state-of-the-art first-principles-based methodologies, specifically density functional theory combined with density functional perturbation theory (DFPT) and many-body perturbation theory [within the framework of GW and BSE]. Our calculations reveal that all these materials possess a cubic lattice structure and are both dynamically and mechanically stable. Interestingly, they all exhibit indirect bandgaps, except Rb$_{2}$RuCl$_{6}$ displays a metallic character. The G$_{0}$W$_{0}$ bandgap values for these compounds fall within the range of 3.63 to 5.14 eV. Additionally, the results of the BSE indicate that they exhibit exceptional absorption capabilities across the near-ultraviolet to mid-ultraviolet light region. Furthermore, studies on transport and excitonic properties suggest that they exhibit lower effective electron masses compared to holes, with exciton binding energies spanning between 0.16$-$0.98 eV. We additionally observed a prevalent hole-phonon coupling compared to electron-phonon coupling in these compounds. Overall, this study provides valuable insights to guide the design of vacancy-ordered double perovskites as promising lead-free candidates for future optoelectronic applications.
{"title":"Probing Optoelectronic Properties of Stable Vacancy-Ordered Double Perovskites: Insights from Many-Body Perturbation Theory","authors":"Surajit Adhikari, Priya Johari","doi":"arxiv-2409.05538","DOIUrl":"https://doi.org/arxiv-2409.05538","url":null,"abstract":"A$_{2}$BX$_{6}$ vacancy-ordered double perovskites (VODPs) have captured\u0000substantial research interest in the scientific community as they offer\u0000environmentally friendly and stable alternatives to lead halide perovskites. In\u0000this study, we investigate Rb$_{2}$BCl$_{6}$ (B = Ti, Se, Ru, Pd) VODPs as\u0000promising optoelectronic materials employing state-of-the-art\u0000first-principles-based methodologies, specifically density functional theory\u0000combined with density functional perturbation theory (DFPT) and many-body\u0000perturbation theory [within the framework of GW and BSE]. Our calculations\u0000reveal that all these materials possess a cubic lattice structure and are both\u0000dynamically and mechanically stable. Interestingly, they all exhibit indirect\u0000bandgaps, except Rb$_{2}$RuCl$_{6}$ displays a metallic character. The\u0000G$_{0}$W$_{0}$ bandgap values for these compounds fall within the range of 3.63\u0000to 5.14 eV. Additionally, the results of the BSE indicate that they exhibit\u0000exceptional absorption capabilities across the near-ultraviolet to\u0000mid-ultraviolet light region. Furthermore, studies on transport and excitonic\u0000properties suggest that they exhibit lower effective electron masses compared\u0000to holes, with exciton binding energies spanning between 0.16$-$0.98 eV. We\u0000additionally observed a prevalent hole-phonon coupling compared to\u0000electron-phonon coupling in these compounds. Overall, this study provides\u0000valuable insights to guide the design of vacancy-ordered double perovskites as\u0000promising lead-free candidates for future optoelectronic applications.","PeriodicalId":501234,"journal":{"name":"arXiv - PHYS - Materials Science","volume":"68 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142188196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
NiO/ZnO heterostructures are grown on c-sapphire substrates using pulsed laser deposition (PLD) technique. X-ray diffraction study shows that the ZnO layer epitaxially grows along [0001]-direction on (0001)sapphire surface as expected. While, the epitaxial NiO film is found to be deposited along [001]-direction on the (0001)ZnO surface. Moreover, the presence of three (001)NiO domains laterally rotated by 30{deg} with respect to each other, has also been observed in our NiO films. The study reveals the continuous nature of the NiO film, which also possesses a very smooth surface morphology. In a sharp contrast, ZnO films are found to grow along [0001]-direction when deposited on (111)NiO layers. These films also show columnar morphology. (001)NiO/(0001)ZnO layers exhibit the rectifying current-voltage characteristics that suggests the existence of p-n junction in these devices. However, the behavior could not be observed in (0001)ZnO/(111)NiO heterojunctions. The reason could be the columnar morphology of the ZnO layer. Such a morphology can facilitate the propagation of the metal ions from the contact pads to the underlying NiO layer and suppress the p-n junction effect.
利用脉冲激光沉积（PLD）技术在 c 蓝宝石衬底上生长出氧化镍/氧化锌异质结构。X 射线衍射研究表明，氧化锌层沿着[0001]方向外延生长在(0001)蓝宝石表面，符合预期。而氧化镍外延膜则是沿着[001]方向沉积在（0001）氧化锌表面。此外，在我们的氧化镍薄膜中还观察到了三个相对于彼此横向旋转 30{/deg}的（001）氧化镍畴。这项研究揭示了氧化镍薄膜的连续性，它还具有非常光滑的表面形态。与此形成鲜明对比的是，氧化锌薄膜沉积在(111)氧化镍层上时沿着[0001]方向生长。这些薄膜也呈现柱状形态。(001)氧化镍/(0001)氧化锌层表现出整流电流-电压特性，这表明这些器件中存在 p-n 结。然而，在(0001)氧化锌/(111)氧化镍异质结中却观察不到这种行为。原因可能是氧化锌层的柱状形态。这种形态有利于金属离子从接触垫传播到下面的氧化镍层，从而抑制了 p-n 结效应。
{"title":"p-(001)NiO/n-(0001)ZnO heterostructures grown by pulsed laser deposition technique","authors":"Bhabani Prasad Sahu, Amandeep Kaur, Simran Arora, Subhabrata Dhar","doi":"arxiv-2409.05003","DOIUrl":"https://doi.org/arxiv-2409.05003","url":null,"abstract":"NiO/ZnO heterostructures are grown on c-sapphire substrates using pulsed\u0000laser deposition (PLD) technique. X-ray diffraction study shows that the ZnO\u0000layer epitaxially grows along [0001]-direction on (0001)sapphire surface as\u0000expected. While, the epitaxial NiO film is found to be deposited along\u0000[001]-direction on the (0001)ZnO surface. Moreover, the presence of three\u0000(001)NiO domains laterally rotated by 30{deg} with respect to each other, has\u0000also been observed in our NiO films. The study reveals the continuous nature of\u0000the NiO film, which also possesses a very smooth surface morphology. In a sharp\u0000contrast, ZnO films are found to grow along [0001]-direction when deposited on\u0000(111)NiO layers. These films also show columnar morphology. (001)NiO/(0001)ZnO\u0000layers exhibit the rectifying current-voltage characteristics that suggests the\u0000existence of p-n junction in these devices. However, the behavior could not be\u0000observed in (0001)ZnO/(111)NiO heterojunctions. The reason could be the\u0000columnar morphology of the ZnO layer. Such a morphology can facilitate the\u0000propagation of the metal ions from the contact pads to the underlying NiO layer\u0000and suppress the p-n junction effect.","PeriodicalId":501234,"journal":{"name":"arXiv - PHYS - Materials Science","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142188200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Polycrystal plasticity in metals is characterized by nonlinear behavior and strain hardening, making numerical models computationally intensive. We employ Graph Neural Network (GNN) to surrogate polycrystal plasticity from finite element method (FEM) simulations. We present a novel message-passing GNN that encodes nodal strain and edge distances between FEM mesh cells, aggregates them to obtain embeddings, and combines the decoded embeddings with the nodal strains to predict stress tensors on graph nodes. We demonstrate training GNN based on subgraphs generated from FEM mesh-graphs, in which the mesh cells are converted to nodes and edges are created between adjacent cells. The GNN is trained on 72 graphs and tested on 18 graphs. We apply the trained GNN to periodic polycrystals and learn the stress-strain maps based on strain-gradient plasticity theory. The GNN is accurately trained based on FEM graphs, in which the $R^2$ for both training and testing sets are 0.993. The proposed GNN plasticity constitutive model speeds up more than 150 times compared with the benchmark FEM method on randomly selected test polycrystals. We also apply the trained GNN to 30 unseen FEM simulations and the GNN generalizes well with an overall $R^2$ of 0.992. Analysis of the von Mises stress distributions in polycrystals shows that the GNN model accurately learns the stress distribution with low error. By comparing the error distribution across training, testing, and unseen datasets, we can deduce that the proposed model does not overfit and generalizes well beyond the training data. This work is expected to pave the way for using graphs as surrogates in polycrystal plasticity modeling.
{"title":"Learning polycrystal plasticity using mesh-based subgraph geometric deep learning","authors":"Hanfeng Zhai","doi":"arxiv-2409.05169","DOIUrl":"https://doi.org/arxiv-2409.05169","url":null,"abstract":"Polycrystal plasticity in metals is characterized by nonlinear behavior and\u0000strain hardening, making numerical models computationally intensive. We employ\u0000Graph Neural Network (GNN) to surrogate polycrystal plasticity from finite\u0000element method (FEM) simulations. We present a novel message-passing GNN that\u0000encodes nodal strain and edge distances between FEM mesh cells, aggregates them\u0000to obtain embeddings, and combines the decoded embeddings with the nodal\u0000strains to predict stress tensors on graph nodes. We demonstrate training GNN\u0000based on subgraphs generated from FEM mesh-graphs, in which the mesh cells are\u0000converted to nodes and edges are created between adjacent cells. The GNN is\u0000trained on 72 graphs and tested on 18 graphs. We apply the trained GNN to\u0000periodic polycrystals and learn the stress-strain maps based on strain-gradient\u0000plasticity theory. The GNN is accurately trained based on FEM graphs, in which\u0000the $R^2$ for both training and testing sets are 0.993. The proposed GNN\u0000plasticity constitutive model speeds up more than 150 times compared with the\u0000benchmark FEM method on randomly selected test polycrystals. We also apply the\u0000trained GNN to 30 unseen FEM simulations and the GNN generalizes well with an\u0000overall $R^2$ of 0.992. Analysis of the von Mises stress distributions in\u0000polycrystals shows that the GNN model accurately learns the stress distribution\u0000with low error. By comparing the error distribution across training, testing,\u0000and unseen datasets, we can deduce that the proposed model does not overfit and\u0000generalizes well beyond the training data. This work is expected to pave the\u0000way for using graphs as surrogates in polycrystal plasticity modeling.","PeriodicalId":501234,"journal":{"name":"arXiv - PHYS - Materials Science","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142188199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lucile FégerGREMAN UMR7347, CNRS, University of Tours, INSA Centre Val de Loire, Tours, France, Carlos Escorihuela-SayaleroDepartament de Física, Universitat Politècnica de Catalunya, Campus Nord B4-B5, Barcelona, Spain, Jean-Michel RampnouxUniversité de Bordeaux, CNRS, LOMA, UMR 5798, Talence, France, Kyriaki KontouUniv Lyon, CNRS, INSA-Lyon, Université Claude Bernard Lyon 1, CETHIL UMR5008, Villeurbanne, France, Micka BahGREMAN UMR7347, CNRS, University of Tours, INSA Centre Val de Loire, Tours, France, Jorge Íñiguez-GonzálezMaterials Research and Technology Department, Luxembourg Institute of Science and TechnologyDepartment of Physics and Materials Science, University of Luxembourg, Belvaux, Luxembourg, Claudio CazorlaDepartament de Física, Universitat Politècnica de Catalunya, Campus Nord B4-B5, Barcelona, Spain, Isabelle Monot-LaffezGREMAN UMR7347, CNRS, University of Tours, INSA Centre Val de Loire, Tours, France, Sarah DouriUniv Lyon, CNRS, INSA-Lyon, Université Claude Bernard Lyon 1, CETHIL UMR5008, Villeurbanne, FranceLaboratoire National de Métrologie et d'Essais, Stéphane GraubyUniversité de Bordeaux, CNRS, LOMA, UMR 5798, Talence, France, Riccardo RuraliInstitut de Ciència de Materials de Barcelona, ICMAB-CSIC, Campus UAB, Bellaterra, Spain, Stefan DilhaireUniversité de Bordeaux, CNRS, LOMA, UMR 5798, Talence, France, Séverine GomèsUniv Lyon, CNRS, INSA-Lyon, Université Claude Bernard Lyon 1, CETHIL UMR5008, Villeurbanne, France, Guillaume F. NatafGREMAN UMR7347, CNRS, University of Tours, INSA Centre Val de Loire, Tours, France
Materials with on-demand control of thermal conductivity are the prerequisites to build thermal conductivity switches, where the thermal conductivity can be turned ON and OFF. However, the ideal switch, while required to develop novel approaches to solid-state refrigeration, energy harvesting, and even phononic circuits, is still missing. It should consist of an active material only, be environment friendly, and operate near room temperature with a reversible, fast, and large switching ratio. Here, we first predict by ab initio electronic structure calculations that ferroelectric domains in barium titanate exhibit anisotropic thermal conductivities. We confirm this prediction by combining frequency-domain thermoreflectance and scanning thermal microscopy measurements on a single crystal of barium titanate. We then use this gained knowledge to propose a lead-free thermal conductivity switch without inactive material, operating reversibly with an electric field. At room temperature, we find a switching ratio of 1.6 $pm$ 0.3, exceeding the performances of state-of-the-art materials suggested for thermal conductivity switches.
按需控制热导率的材料是制造热导率开关的先决条件，在这种开关中，热导率可以打开或关闭。然而，要开发固态制冷、能量收集甚至声波电路的新方法，理想的开关仍未出现。它应该只由活性材料组成，对环境友好，在室温附近工作，具有可逆、快速和大开关比。在这里，我们首先通过ab initio 电子结构计算预测出钛酸钡中的铁电层具有各向异性的热导率。通过对钛酸钡单晶体进行频域热反射和扫描热显微镜测量，我们证实了这一预测。然后，我们利用所获得的知识提出了一种无铅热导开关，它不含非活性材料，在电场作用下可逆运行。在室温下，我们发现开关比为 1.6 美元/pm$0.3，超过了建议用于热导开关的最先进材料的性能。
{"title":"Lead-free room-temperature ferroelectric thermal conductivity switch using anisotropies in thermal conductivities","authors":"Lucile FégerGREMAN UMR7347, CNRS, University of Tours, INSA Centre Val de Loire, Tours, France, Carlos Escorihuela-SayaleroDepartament de Física, Universitat Politècnica de Catalunya, Campus Nord B4-B5, Barcelona, Spain, Jean-Michel RampnouxUniversité de Bordeaux, CNRS, LOMA, UMR 5798, Talence, France, Kyriaki KontouUniv Lyon, CNRS, INSA-Lyon, Université Claude Bernard Lyon 1, CETHIL UMR5008, Villeurbanne, France, Micka BahGREMAN UMR7347, CNRS, University of Tours, INSA Centre Val de Loire, Tours, France, Jorge Íñiguez-GonzálezMaterials Research and Technology Department, Luxembourg Institute of Science and TechnologyDepartment of Physics and Materials Science, University of Luxembourg, Belvaux, Luxembourg, Claudio CazorlaDepartament de Física, Universitat Politècnica de Catalunya, Campus Nord B4-B5, Barcelona, Spain, Isabelle Monot-LaffezGREMAN UMR7347, CNRS, University of Tours, INSA Centre Val de Loire, Tours, France, Sarah DouriUniv Lyon, CNRS, INSA-Lyon, Université Claude Bernard Lyon 1, CETHIL UMR5008, Villeurbanne, FranceLaboratoire National de Métrologie et d'Essais, Stéphane GraubyUniversité de Bordeaux, CNRS, LOMA, UMR 5798, Talence, France, Riccardo RuraliInstitut de Ciència de Materials de Barcelona, ICMAB-CSIC, Campus UAB, Bellaterra, Spain, Stefan DilhaireUniversité de Bordeaux, CNRS, LOMA, UMR 5798, Talence, France, Séverine GomèsUniv Lyon, CNRS, INSA-Lyon, Université Claude Bernard Lyon 1, CETHIL UMR5008, Villeurbanne, France, Guillaume F. NatafGREMAN UMR7347, CNRS, University of Tours, INSA Centre Val de Loire, Tours, France","doi":"arxiv-2409.05216","DOIUrl":"https://doi.org/arxiv-2409.05216","url":null,"abstract":"Materials with on-demand control of thermal conductivity are the\u0000prerequisites to build thermal conductivity switches, where the thermal\u0000conductivity can be turned ON and OFF. However, the ideal switch, while\u0000required to develop novel approaches to solid-state refrigeration, energy\u0000harvesting, and even phononic circuits, is still missing. It should consist of\u0000an active material only, be environment friendly, and operate near room\u0000temperature with a reversible, fast, and large switching ratio. Here, we first\u0000predict by ab initio electronic structure calculations that ferroelectric\u0000domains in barium titanate exhibit anisotropic thermal conductivities. We\u0000confirm this prediction by combining frequency-domain thermoreflectance and\u0000scanning thermal microscopy measurements on a single crystal of barium\u0000titanate. We then use this gained knowledge to propose a lead-free thermal\u0000conductivity switch without inactive material, operating reversibly with an\u0000electric field. At room temperature, we find a switching ratio of 1.6 $pm$\u00000.3, exceeding the performances of state-of-the-art materials suggested for\u0000thermal conductivity switches.","PeriodicalId":501234,"journal":{"name":"arXiv - PHYS - Materials Science","volume":"60 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142224530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yibo Liu, Yangyang Feng, Ying Dai, Baibiao Huang, Yandong Ma
Ferro-valleytricity, a fundamental phenomenon that manifests spontaneous valley polarization, is generally considered to occur in two-dimensional (2D) materials with out-of-plane magnetization. Here, we propose a mechanism to realize ferro-valleytricity in 2D materials with in-plane magnetization, wherein the physics correlates to non-collinear magnetism in triangular lattice. Our model analysis provides comprehensive ingredients that allows for in-plane ferro-valleytricity, revealing that mirror symmetry is required for remarkable valley polarization and time-reversal-mirror joint-symmetry should be excluded. Through modulating in-plane magnetization offset, the valley polarization could be reversed. Followed by first-principles, such mechanism is demonstrated in a multiferroic triangular lattice of single-layer W3Cl8. We further show that the reversal of valley polarization could also be driven by applying electric field that modulates ferroelectricity. Our findings greatly enrich the valley physics research and significantly extend the scope for material classes of ferro-valleytricity.
{"title":"Ferro-Valleytricity with In-Plane Magnetization","authors":"Yibo Liu, Yangyang Feng, Ying Dai, Baibiao Huang, Yandong Ma","doi":"arxiv-2409.04739","DOIUrl":"https://doi.org/arxiv-2409.04739","url":null,"abstract":"Ferro-valleytricity, a fundamental phenomenon that manifests spontaneous\u0000valley polarization, is generally considered to occur in two-dimensional (2D)\u0000materials with out-of-plane magnetization. Here, we propose a mechanism to\u0000realize ferro-valleytricity in 2D materials with in-plane magnetization,\u0000wherein the physics correlates to non-collinear magnetism in triangular\u0000lattice. Our model analysis provides comprehensive ingredients that allows for\u0000in-plane ferro-valleytricity, revealing that mirror symmetry is required for\u0000remarkable valley polarization and time-reversal-mirror joint-symmetry should\u0000be excluded. Through modulating in-plane magnetization offset, the valley\u0000polarization could be reversed. Followed by first-principles, such mechanism is\u0000demonstrated in a multiferroic triangular lattice of single-layer W3Cl8. We\u0000further show that the reversal of valley polarization could also be driven by\u0000applying electric field that modulates ferroelectricity. Our findings greatly\u0000enrich the valley physics research and significantly extend the scope for\u0000material classes of ferro-valleytricity.","PeriodicalId":501234,"journal":{"name":"arXiv - PHYS - Materials Science","volume":"45 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142188201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Accurate crystal structure determination is critical across all scientific disciplines involving crystalline materials. However, solving and refining inorganic crystal structures from powder X-ray diffraction (PXRD) data is traditionally a labor-intensive and time-consuming process that demands substantial expertise. In this work, we introduce PXRDGen, an end-to-end neural network that determines crystal structures by learning joint structural distributions from experimentally stable crystals and their PXRD, producing atomically accurate structures refined through PXRD data. PXRDGen integrates a pretrained XRD encoder, a diffusion/flow-based structure generator, and a Rietveld refinement module, enabling the solution of structures with unparalleled accuracy in a matter of seconds. Evaluation on MP-20 inorganic dataset reveals a remarkable matching rate of 82% (1 sample) and 96% (20 samples) for valid compounds, with Root Mean Square Error (RMSE) approaching the precision limits of Rietveld refinement. PXRDGen effectively tackles key challenges in XRD, such as the precise localization of light atoms, differentiation of neighboring elements, and resolution of overlapping peaks. Overall, PXRDGen marks a significant advancement in the automated determination of crystal structures from powder diffraction data.
{"title":"Powder Diffraction Crystal Structure Determination Using Generative Models","authors":"Qi Li, Rui Jiao, Liming Wu, Tiannian Zhu, Wenbing Huang, Shifeng Jin, Yang Liu, Hongming Weng, Xiaolong Chen","doi":"arxiv-2409.04727","DOIUrl":"https://doi.org/arxiv-2409.04727","url":null,"abstract":"Accurate crystal structure determination is critical across all scientific\u0000disciplines involving crystalline materials. However, solving and refining\u0000inorganic crystal structures from powder X-ray diffraction (PXRD) data is\u0000traditionally a labor-intensive and time-consuming process that demands\u0000substantial expertise. In this work, we introduce PXRDGen, an end-to-end neural\u0000network that determines crystal structures by learning joint structural\u0000distributions from experimentally stable crystals and their PXRD, producing\u0000atomically accurate structures refined through PXRD data. PXRDGen integrates a\u0000pretrained XRD encoder, a diffusion/flow-based structure generator, and a\u0000Rietveld refinement module, enabling the solution of structures with\u0000unparalleled accuracy in a matter of seconds. Evaluation on MP-20 inorganic\u0000dataset reveals a remarkable matching rate of 82% (1 sample) and 96% (20\u0000samples) for valid compounds, with Root Mean Square Error (RMSE) approaching\u0000the precision limits of Rietveld refinement. PXRDGen effectively tackles key\u0000challenges in XRD, such as the precise localization of light atoms,\u0000differentiation of neighboring elements, and resolution of overlapping peaks.\u0000Overall, PXRDGen marks a significant advancement in the automated determination\u0000of crystal structures from powder diffraction data.","PeriodicalId":501234,"journal":{"name":"arXiv - PHYS - Materials Science","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142188203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Accurate and efficient calculation of optical response properties of solid materials is still challenging. We present a meta-generalized gradient approximation (metaGGA) density functional based time-dependent and dielectric function dependent method for calculating optical absorption, exciton binding energy and intrinsic exciton lifetime for bulk solids and two-dimensional (2D) monolayer materials. This method uses advanced metaGGA functionals to describe the band structures, and a dielectric function mBSE (model Bethe-Salpeter equation) to capture the screening effect accurately and efficiently and the interaction between electrons and holes. The calculated optical absorption spectra of bulk Si, diamond, SiC, MgO, and monolayer MoS2 qualitatively agree with experimental results. The exciton binding energies of the first prominent peak in the optical absorption spectra of the direct band gap solids Ar, NaCl and MgO from mBSE qualitatively agree with those from standard GW-BSE. For monolayer MoS2, mBSE predicts quantitatively accurate binding energy for the first prominent peak, better than GW-BSE does. The calculated intrinsic exciton lifetimes for materials considered here show magnitudes of several nanoseconds for most bright excitons. The presented mtaGGA-mBSE method is established as a computationally efficient alternative for optical properties of materials with an overall qualitative accuracy.
{"title":"A meta-generalized gradient approximation-based time-dependent and dielectric function dependent method for optical properties of solid materials","authors":"Hong Tang, Niraj Pangeni, Adrienn Ruzsinszky","doi":"arxiv-2409.04904","DOIUrl":"https://doi.org/arxiv-2409.04904","url":null,"abstract":"Accurate and efficient calculation of optical response properties of solid\u0000materials is still challenging. We present a meta-generalized gradient\u0000approximation (metaGGA) density functional based time-dependent and dielectric\u0000function dependent method for calculating optical absorption, exciton binding\u0000energy and intrinsic exciton lifetime for bulk solids and two-dimensional (2D)\u0000monolayer materials. This method uses advanced metaGGA functionals to describe\u0000the band structures, and a dielectric function mBSE (model Bethe-Salpeter\u0000equation) to capture the screening effect accurately and efficiently and the\u0000interaction between electrons and holes. The calculated optical absorption\u0000spectra of bulk Si, diamond, SiC, MgO, and monolayer MoS2 qualitatively agree\u0000with experimental results. The exciton binding energies of the first prominent\u0000peak in the optical absorption spectra of the direct band gap solids Ar, NaCl\u0000and MgO from mBSE qualitatively agree with those from standard GW-BSE. For\u0000monolayer MoS2, mBSE predicts quantitatively accurate binding energy for the\u0000first prominent peak, better than GW-BSE does. The calculated intrinsic exciton\u0000lifetimes for materials considered here show magnitudes of several nanoseconds\u0000for most bright excitons. The presented mtaGGA-mBSE method is established as a\u0000computationally efficient alternative for optical properties of materials with\u0000an overall qualitative accuracy.","PeriodicalId":501234,"journal":{"name":"arXiv - PHYS - Materials Science","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142188204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Application of artificial intelligence (AI) has been ubiquitous in the growth of research in the areas of basic sciences. Frequent use of machine learning (ML) and deep learning (DL) based methodologies by researchers has resulted in significant advancements in the last decade. These techniques led to notable performance enhancements in different tasks such as protein structure prediction, drug-target binding affinity prediction, and molecular property prediction. In material science literature, it is well-known that crystalline materials exhibit topological structures. Such topological structures may be represented as graphs and utilization of graph neural network (GNN) based approaches could help encoding them into an augmented representation space. Primarily, such frameworks adopt supervised learning techniques targeted towards downstream property prediction tasks on the basis of electronic properties (formation energy, bandgap, total energy, etc.) and crystalline structures. Generally, such type of frameworks rely highly on the handcrafted atom feature representations along with the structural representations. In this paper, we propose an unsupervised framework namely, CrysAtom, using untagged crystal data to generate dense vector representation of atoms, which can be utilized in existing GNN-based property predictor models to accurately predict important properties of crystals. Empirical results show that our dense representation embeds chemical properties of atoms and enhance the performance of the baseline property predictor models significantly.
{"title":"CrysAtom: Distributed Representation of Atoms for Crystal Property Prediction","authors":"Shrimon Mukherjee, Madhusudan Ghosh, Partha Basuchowdhuri","doi":"arxiv-2409.04737","DOIUrl":"https://doi.org/arxiv-2409.04737","url":null,"abstract":"Application of artificial intelligence (AI) has been ubiquitous in the growth\u0000of research in the areas of basic sciences. Frequent use of machine learning\u0000(ML) and deep learning (DL) based methodologies by researchers has resulted in\u0000significant advancements in the last decade. These techniques led to notable\u0000performance enhancements in different tasks such as protein structure\u0000prediction, drug-target binding affinity prediction, and molecular property\u0000prediction. In material science literature, it is well-known that crystalline\u0000materials exhibit topological structures. Such topological structures may be\u0000represented as graphs and utilization of graph neural network (GNN) based\u0000approaches could help encoding them into an augmented representation space.\u0000Primarily, such frameworks adopt supervised learning techniques targeted\u0000towards downstream property prediction tasks on the basis of electronic\u0000properties (formation energy, bandgap, total energy, etc.) and crystalline\u0000structures. Generally, such type of frameworks rely highly on the handcrafted\u0000atom feature representations along with the structural representations. In this\u0000paper, we propose an unsupervised framework namely, CrysAtom, using untagged\u0000crystal data to generate dense vector representation of atoms, which can be\u0000utilized in existing GNN-based property predictor models to accurately predict\u0000important properties of crystals. Empirical results show that our dense\u0000representation embeds chemical properties of atoms and enhance the performance\u0000of the baseline property predictor models significantly.","PeriodicalId":501234,"journal":{"name":"arXiv - PHYS - Materials Science","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142188206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dae-Yun Kim, Imane Berrai, T. S. Suraj, Yves Roussigne, Shuhan Yang, Mohamed Belmeguenai, Fanrui Hu, Guoyi Shi, Hui Ru Tan, Jifei Huang, Anjan Soumyanarayanan, Kyoung-Whan Kim, Salim Mourad Cherif, Hyunsoo Yang
Chiral magnets have garnered significant interest due to the emergence of unique phenomena prohibited in inversion-symmetric magnets. While the equilibrium characteristics of chiral magnets have been extensively explored through the Dzyaloshinskii-Moriya interaction (DMI), non-equilibrium properties like magnetic damping have received comparatively less attention. We present the inaugural direct observation of chiral damping through Brillouin light scattering (BLS) spectroscopy. Employing BLS spectrum analysis, we independently deduce the Dzyaloshinskii-Moriya interaction (DMI) and chiral damping, extracting them from the frequency shift and linewidth of the spectrum peak, respectively. The resulting linewidths exhibit clear odd symmetry with respect to the magnon wave vector, unambiguously confirming the presence of chiral damping. Our study introduces a novel methodology for quantifying chiral damping, with potential ramifications on diverse nonequilibrium phenomena within chiral magnets.
{"title":"Chiral damping of magnons","authors":"Dae-Yun Kim, Imane Berrai, T. S. Suraj, Yves Roussigne, Shuhan Yang, Mohamed Belmeguenai, Fanrui Hu, Guoyi Shi, Hui Ru Tan, Jifei Huang, Anjan Soumyanarayanan, Kyoung-Whan Kim, Salim Mourad Cherif, Hyunsoo Yang","doi":"arxiv-2409.04713","DOIUrl":"https://doi.org/arxiv-2409.04713","url":null,"abstract":"Chiral magnets have garnered significant interest due to the emergence of\u0000unique phenomena prohibited in inversion-symmetric magnets. While the\u0000equilibrium characteristics of chiral magnets have been extensively explored\u0000through the Dzyaloshinskii-Moriya interaction (DMI), non-equilibrium properties\u0000like magnetic damping have received comparatively less attention. We present\u0000the inaugural direct observation of chiral damping through Brillouin light\u0000scattering (BLS) spectroscopy. Employing BLS spectrum analysis, we\u0000independently deduce the Dzyaloshinskii-Moriya interaction (DMI) and chiral\u0000damping, extracting them from the frequency shift and linewidth of the spectrum\u0000peak, respectively. The resulting linewidths exhibit clear odd symmetry with\u0000respect to the magnon wave vector, unambiguously confirming the presence of\u0000chiral damping. Our study introduces a novel methodology for quantifying chiral\u0000damping, with potential ramifications on diverse nonequilibrium phenomena\u0000within chiral magnets.","PeriodicalId":501234,"journal":{"name":"arXiv - PHYS - Materials Science","volume":"60 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142188205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Julián A. Zúñiga, Arles V. Gil Rebaza, Diego F. Coral Coral
In this work, a theoretical study of spin transport in a pseudovalve spin (PSV) heterostructure is conducted. For the semiconductor (SC), the conduction band at the $Gamma$ point of reciprocal space and spin-orbit coupling (SOC) are considered. For the ferromagnetic (FM) electrodes on the left ($l$) and right ($r$), the internal exchange energy ($Delta_j$, where $j = left(l,rright)$) and the magnetization normal vector ($mathbf{n}_j$) on the barrier plane are taken into account. An analytical expression for the transmission probability as a function of $mathbf{n}_j$ direction was obtained from the {em Schr"odinger-Pauli} equations with the boundary conditions. Furthermore, the tunnel magnetoresistance (TMR) at T $approx$ 0 K was calculated, depending on the direction of the crystallographic axis favoring the magnetization ($theta_m$) of the FM and the thickness of the SC, using the {em Landauer-B"{u}ttiker} formula for a single channel. It is observed that the TMR reaches its maximum value when the $mathbf{n}_l$ direction is parallel to $theta_m$. Applying this physico-mathematical model to the Fe/SC/Fe PSV, with SC as GaAs, GaSb, and InAs, it was found that the {em Dresselhaus} SOC does not significantly contribute to the TMR.
{"title":"Theoretical spin transport analysis for a spin pseudovalve-type $mathrm{L}_j$/semiconductor/$mathrm{L}_j$ trilayer (with $mathrm{L}_j$ = ferromagnetic)","authors":"Julián A. Zúñiga, Arles V. Gil Rebaza, Diego F. Coral Coral","doi":"arxiv-2409.04635","DOIUrl":"https://doi.org/arxiv-2409.04635","url":null,"abstract":"In this work, a theoretical study of spin transport in a pseudovalve spin\u0000(PSV) heterostructure is conducted. For the semiconductor (SC), the conduction\u0000band at the $Gamma$ point of reciprocal space and spin-orbit coupling (SOC)\u0000are considered. For the ferromagnetic (FM) electrodes on the left ($l$) and\u0000right ($r$), the internal exchange energy ($Delta_j$, where $j =\u0000left(l,rright)$) and the magnetization normal vector ($mathbf{n}_j$) on the\u0000barrier plane are taken into account. An analytical expression for the\u0000transmission probability as a function of $mathbf{n}_j$ direction was obtained\u0000from the {em Schr\"odinger-Pauli} equations with the boundary conditions.\u0000Furthermore, the tunnel magnetoresistance (TMR) at T $approx$ 0 K was\u0000calculated, depending on the direction of the crystallographic axis favoring\u0000the magnetization ($theta_m$) of the FM and the thickness of the SC, using the\u0000{em Landauer-B\"{u}ttiker} formula for a single channel. It is observed that\u0000the TMR reaches its maximum value when the $mathbf{n}_l$ direction is parallel\u0000to $theta_m$. Applying this physico-mathematical model to the Fe/SC/Fe PSV,\u0000with SC as GaAs, GaSb, and InAs, it was found that the {em Dresselhaus} SOC\u0000does not significantly contribute to the TMR.","PeriodicalId":501234,"journal":{"name":"arXiv - PHYS - Materials Science","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142188207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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