arXiv - PHYS - Materials Science最新文献_第7页

Quantifying Implantation Damage and Point Defects with Multislice Electron Ptychography 利用多层电子断层扫描量化植入损伤和点状缺陷

arXiv - PHYS - Materials Science Pub Date : 2024-09-11 DOI: arxiv-2409.06987

Junghwa Kim, Colin Gilgenbach, Aaditya Bhat, James LeBeau

{"title":"Quantifying Implantation Damage and Point Defects with Multislice Electron Ptychography","authors":"Junghwa Kim, Colin Gilgenbach, Aaditya Bhat, James LeBeau","doi":"arxiv-2409.06987","DOIUrl":"https://doi.org/arxiv-2409.06987","url":null,"abstract":"Ion implantation is widely used to dope semiconductors for electronic device\u0000fabrication, but techniques to quantify point defects and induced damage are\u0000limited. While several techniques can measure dopant concentration profiles\u0000with high accuracy, none allow for simultaneous atomic resolution structural\u0000analysis. Here, we use multislice electron ptychography to quantify the damage\u0000induced by erbium implantation in a wide band gap semiconductor 4H-SiC over a\u00001,000 nmtextsuperscript{3} volume region. This damage extends further into the\u0000sample than expected from implantation simulations that do not consider\u0000crystallography. Further, the technique's sensitivity to dopants and vacancies\u0000is evaluated as a function of damage. As each reconstructed analysis volume\u0000contains approximately 10$^5$ atoms, sensitivity of 10textsuperscript{18}\u0000cmtextsuperscript{-3} (in the order of 10 ppm) is demonstrated in the\u0000implantation tail region. After point defect identification, the local\u0000distortions surrounding ch{Er_{Si}} and ch{v_{Si}} defects are quantified.\u0000These results underscore the power of multislice electron ptychography to\u0000enable the investigation of point defects as a tool to guide the fabrication of\u0000future electronic devices.","PeriodicalId":501234,"journal":{"name":"arXiv - PHYS - Materials Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187918","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}

引用次数: 0

VQCrystal: Leveraging Vector Quantization for Discovery of Stable Crystal Structures VQCrystal：利用矢量量化发现稳定晶体结构

arXiv - PHYS - Materials Science Pub Date : 2024-09-10 DOI: arxiv-2409.06191

ZiJie Qiu, Luozhijie Jin, Zijian Du, Hongyu Chen, Yan Cen, Siqi Sun, Yongfeng Mei, Hao Zhang

{"title":"VQCrystal: Leveraging Vector Quantization for Discovery of Stable Crystal Structures","authors":"ZiJie Qiu, Luozhijie Jin, Zijian Du, Hongyu Chen, Yan Cen, Siqi Sun, Yongfeng Mei, Hao Zhang","doi":"arxiv-2409.06191","DOIUrl":"https://doi.org/arxiv-2409.06191","url":null,"abstract":"Discovering functional crystalline materials through computational methods\u0000remains a formidable challenge in materials science. Here, we introduce\u0000VQCrystal, an innovative deep learning framework that leverages discrete latent\u0000representations to overcome key limitations in current approaches to crystal\u0000generation and inverse design. VQCrystal employs a hierarchical VQ-VAE\u0000architecture to encode global and atom-level crystal features, coupled with a\u0000machine learning-based inter-atomic potential(IAP) model and a genetic\u0000algorithm to realize property-targeted inverse design. Benchmark evaluations on\u0000diverse datasets demonstrate VQCrystal's advanced capabilities in\u0000representation learning and novel crystal discovery. Notably, VQCrystal\u0000achieves state-of-the-art performance with 91.93% force validity and a\u0000Fr'echet Distance of 0.152 on MP-20, indicating both strong validity and high\u0000diversity in the sampling process. To demonstrate real-world applicability, we\u0000apply VQCrystal for both 3D and 2D material design. For 3D materials, the\u0000density-functional theory validation confirmed that 63.04% of bandgaps and\u000099% of formation energies of the 56 filtered materials matched the target\u0000range. Moreover, 437 generated materials were validated as existing entries in\u0000the full database outside the training set. For the discovery of 2D materials,\u000073.91% of 23 filtered structures exhibited high stability with formation\u0000energies below -1 eV/atom. Our results highlight VQCrystal's potential to\u0000accelerate the discovery of novel materials with tailored properties.","PeriodicalId":501234,"journal":{"name":"arXiv - PHYS - Materials Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187926","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}

引用次数: 0

Data-efficient and Interpretable Inverse Materials Design using a Disentangled Variational Autoencoder 利用离散变异自动编码器实现数据高效、可解释的逆材料设计

arXiv - PHYS - Materials Science Pub Date : 2024-09-10 DOI: arxiv-2409.06740

Cheng Zeng, Zulqarnain Khan, Nathan L. Post

{"title":"Data-efficient and Interpretable Inverse Materials Design using a Disentangled Variational Autoencoder","authors":"Cheng Zeng, Zulqarnain Khan, Nathan L. Post","doi":"arxiv-2409.06740","DOIUrl":"https://doi.org/arxiv-2409.06740","url":null,"abstract":"Inverse materials design has proven successful in accelerating novel material\u0000discovery. Many inverse materials design methods use unsupervised learning\u0000where a latent space is learned to offer a compact description of materials\u0000representations. A latent space learned this way is likely to be entangled, in\u0000terms of the target property and other properties of the materials. This makes\u0000the inverse design process ambiguous. Here, we present a semi-supervised\u0000learning approach based on a disentangled variational autoencoder to learn a\u0000probabilistic relationship between features, latent variables and target\u0000properties. This approach is data efficient because it combines all labelled\u0000and unlabelled data in a coherent manner, and it uses expert-informed prior\u0000distributions to improve model robustness even with limited labelled data. It\u0000is in essence interpretable, as the learnable target property is disentangled\u0000out of the other properties of the materials, and an extra layer of\u0000interpretability can be provided by a post-hoc analysis of the classification\u0000head of the model. We demonstrate this new approach on an experimental\u0000high-entropy alloy dataset with chemical compositions as input and single-phase\u0000formation as the single target property. While single property is used in this\u0000work, the disentangled model can be extended to customize for inverse design of\u0000materials with multiple target properties.","PeriodicalId":501234,"journal":{"name":"arXiv - PHYS - Materials Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187921","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}

引用次数: 0

Performance of Exchange-Correlation Approximations to Density-Functional Theory for Rare-earth Oxides 稀土氧化物密度函数理论的交换相关性近似值的性能

arXiv - PHYS - Materials Science Pub Date : 2024-09-10 DOI: arxiv-2409.06145

Mary Kathleen Caucci, Jacob T. Sivak, Saeed S. I. Almishal, Christina M. Rost, Ismaila Dabo, Jon-Paul Maria, Susan B. Sinnott

{"title":"Performance of Exchange-Correlation Approximations to Density-Functional Theory for Rare-earth Oxides","authors":"Mary Kathleen Caucci, Jacob T. Sivak, Saeed S. I. Almishal, Christina M. Rost, Ismaila Dabo, Jon-Paul Maria, Susan B. Sinnott","doi":"arxiv-2409.06145","DOIUrl":"https://doi.org/arxiv-2409.06145","url":null,"abstract":"Rare-earth oxides (REOs) are an important class of materials owing to their\u0000unique properties, including high ionic conductivities, large dielectric\u0000constants, and elevated melting temperatures, making them relevant to several\u0000technological applications such as catalysis, ionic conduction, and sensing.\u0000The ability to predict these properties at moderate computational cost is\u0000essential to guiding materials discovery and optimizing materials performance.\u0000Although density-functional theory (DFT) is the favored approach for predicting\u0000electronic and atomic structures, its accuracy is limited in describing strong\u0000electron correlation and localization inherent to REOs. The newly developed\u0000strongly constrained and appropriately normed (SCAN) meta-generalized-gradient\u0000approximations (meta-GGAs) promise improved accuracy in modeling these strongly\u0000correlated systems. We assess the performance of these meta-GGAs on binary REOs\u0000by comparing the numerical accuracy of thirteen exchange-correlation\u0000approximations in predicting structural, magnetic, and electronic properties.\u0000Hubbard U corrections for self-interaction errors and spin-orbit coupling are\u0000systematically considered. Our comprehensive assessment offers insights into\u0000the physical properties and functional performance of REOs predicted by\u0000first-principles and provides valuable guidance for selecting optimal DFT\u0000functionals for exploring these materials.","PeriodicalId":501234,"journal":{"name":"arXiv - PHYS - Materials Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142224529","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}

引用次数: 0

Complexities in the growth and stabilization of polar phase in the Hf$_{0.5}$Zr$_{0.5}$O$_2$ thin films grown by Pulsed Laser Deposition 脉冲激光沉积法生长的 Hf$_{0.5}$Zr$_{0.5}$O$_2$ 薄膜中极性相的生长和稳定过程的复杂性

arXiv - PHYS - Materials Science Pub Date : 2024-09-10 DOI: arxiv-2409.06549

Deepak Kumar

{"title":"Complexities in the growth and stabilization of polar phase in the Hf$_{0.5}$Zr$_{0.5}$O$_2$ thin films grown by Pulsed Laser Deposition","authors":"Deepak Kumar","doi":"arxiv-2409.06549","DOIUrl":"https://doi.org/arxiv-2409.06549","url":null,"abstract":"After the discovery of ferroelectricity in HfO$_2$ based thin films a decade\u0000ago, ferroelectric Hf$_{0.5}$Zr$_{0.5}$O$_2$ (HZO) thin films are frequently\u0000being utilized in the CMOS (Complementary Metal- Oxide Semiconductor) and logic\u0000devices, thanks to their large remnant polarization, high retention and\u0000endurance. A great deal of effort has been made towards understanding the\u0000origin of ferroelectricity in epitaxial HZO thin films and controlling the\u0000microstructure at the atomic level which governs the ferroelectric phase.\u0000Nevertheless, the HZO films still suffer from fundamental questions, such as\u0000(1) the vagueness of interfacial mechanisms between HZO, buffer layer and the\u0000substrate which controls the polar phase; (2) the nature of the metastable\u0000polar phase responsible for the ferroelectricity, be it orthorhombic or\u0000rhombohedral; which are poorly understood. Here, we have addressed these issues\u0000by employing the in-situ reflection high energy electron diffraction --\u0000assisted pulsed laser deposition and mapping the asymmetrical polar maps on\u0000high quality HZO films grown on functional perovskite oxide substrates. The\u0000interface between La$_{0.7}$Sr$_{0.3}$MnO$_3$ (LSMO) and the substrate is shown\u0000to be quite important, and a slightly rougher interface of the former\u0000destabilizes the ferroelectric phase of HZO irrespective of well-controlled\u0000growth of the ferroelectric layers. A rhombohedral-like symmetry of HZO unit\u0000cell is extracted through the x-ray diffraction asymmetrical polar maps. The\u0000ferroelectric measurements on a nearly 7 nm HZO film on STO(001) substrate\u0000display a remnant polarization close to 8 uC/cm$^2$. These results highlight\u0000the complexities involved at the atomic scale interface in the binary oxides\u0000thin films and can be of importance to the HfO$_2$-based ferroelectric\u0000community which is still at its infancy.","PeriodicalId":501234,"journal":{"name":"arXiv - PHYS - Materials Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142224525","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}

引用次数: 0

Multi-Physics Modeling Of Phase Change Memory Operations in Ge-rich Ge$_2$Sb$_2$Te$_5$ Alloys 富锗 Ge$_2$Sb$_2$Te$_5$ 合金中相变记忆操作的多物理场建模

arXiv - PHYS - Materials Science Pub Date : 2024-09-10 DOI: arxiv-2409.06463

Robin Miquel, Thomas Cabout, Olga Cueto, Benoît Sklénard, Mathis Plapp

引用次数: 0

Generative Hierarchical Materials Search 生成式分层材料搜索

arXiv - PHYS - Materials Science Pub Date : 2024-09-10 DOI: arxiv-2409.06762

Sherry Yang, Simon Batzner, Ruiqi Gao, Muratahan Aykol, Alexander L. Gaunt, Brendan McMorrow, Danilo J. Rezende, Dale Schuurmans, Igor Mordatch, Ekin D. Cubuk

{"title":"Generative Hierarchical Materials Search","authors":"Sherry Yang, Simon Batzner, Ruiqi Gao, Muratahan Aykol, Alexander L. Gaunt, Brendan McMorrow, Danilo J. Rezende, Dale Schuurmans, Igor Mordatch, Ekin D. Cubuk","doi":"arxiv-2409.06762","DOIUrl":"https://doi.org/arxiv-2409.06762","url":null,"abstract":"Generative models trained at scale can now produce text, video, and more\u0000recently, scientific data such as crystal structures. In applications of\u0000generative approaches to materials science, and in particular to crystal\u0000structures, the guidance from the domain expert in the form of high-level\u0000instructions can be essential for an automated system to output candidate\u0000crystals that are viable for downstream research. In this work, we formulate\u0000end-to-end language-to-structure generation as a multi-objective optimization\u0000problem, and propose Generative Hierarchical Materials Search (GenMS) for\u0000controllable generation of crystal structures. GenMS consists of (1) a language\u0000model that takes high-level natural language as input and generates\u0000intermediate textual information about a crystal (e.g., chemical formulae), and\u0000(2) a diffusion model that takes intermediate information as input and\u0000generates low-level continuous value crystal structures. GenMS additionally\u0000uses a graph neural network to predict properties (e.g., formation energy) from\u0000the generated crystal structures. During inference, GenMS leverages all three\u0000components to conduct a forward tree search over the space of possible\u0000structures. Experiments show that GenMS outperforms other alternatives of\u0000directly using language models to generate structures both in satisfying user\u0000request and in generating low-energy structures. We confirm that GenMS is able\u0000to generate common crystal structures such as double perovskites, or spinels,\u0000solely from natural language input, and hence can form the foundation for more\u0000complex structure generation in near future.","PeriodicalId":501234,"journal":{"name":"arXiv - PHYS - Materials Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187920","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}

引用次数: 0

Investigating Ionic Diffusivity in Amorphous Solid Electrolytes using Machine Learned Interatomic Potentials 利用机器学习原子间位势研究无定形固体电解质中的离子扩散性

arXiv - PHYS - Materials Science Pub Date : 2024-09-10 DOI: arxiv-2409.06242

Aqshat Seth, Rutvij Pankaj Kulkarni, Gopalakrishnan Sai Gautam

{"title":"Investigating Ionic Diffusivity in Amorphous Solid Electrolytes using Machine Learned Interatomic Potentials","authors":"Aqshat Seth, Rutvij Pankaj Kulkarni, Gopalakrishnan Sai Gautam","doi":"arxiv-2409.06242","DOIUrl":"https://doi.org/arxiv-2409.06242","url":null,"abstract":"Investigating Li$^+$ transport within the amorphous lithium phosphorous\u0000oxynitride (LiPON) framework, especially across a Li||LiPON interface, has\u0000proven challenging due to its amorphous nature and varying stoichiometry,\u0000necessitating large supercells and long timescales for computational models.\u0000Notably, machine learned interatomic potentials (MLIPs) can combine the\u0000computational speed of classical force fields with the accuracy of density\u0000functional theory (DFT), making them the ideal tool for modelling such\u0000amorphous materials. Thus, in this work, we train and validate the neural\u0000equivariant Interatomic potential (NequIP) framework on a comprehensive\u0000DFT-based dataset consisting of 13,454 chemically relevant structures to\u0000describe LiPON. With an optimized training (validation) energy and force mean\u0000absolute errors of 5.5 (6.1) meV/atom and 13.6 (13.2) meV/{AA}, respectively,\u0000we employ the trained potential in model Li-transport in both bulk LiPON and\u0000across a Li||LiPON interface. Amorphous LiPON structures generated by the\u0000optimized potential do resemble those generated by ab initio molecular\u0000dynamics, with N being incorporated on non-bridging apical and bridging sites.\u0000Subsequent analysis of Li$^+$ diffusivity in the bulk LiPON structures\u0000indicates broad agreement with computational and experimental literature so\u0000far. Further, we investigate the anisotropy in Li$^+$ transport across the\u0000Li(110)||LiPON interface, where we observe Li-transport across the interface to\u0000be one order-of-magnitude slower than Li-motion within the bulk Li and LiPON\u0000phases. Nevertheless, we note that this anisotropy of Li-transport across the\u0000interface is minor and do not expect it to cause any significant impedance\u0000buildup. Finally, our work highlights the efficiency of MLIPs in enabling\u0000high-fidelity modelling of complex non-crystalline systems over large length\u0000and time scales.","PeriodicalId":501234,"journal":{"name":"arXiv - PHYS - Materials Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187928","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}

引用次数: 0

The resistivity of rare earth impurities diluted in Lanthanum (Part I) 稀土杂质在镧中的电阻率（第一部分）

arXiv - PHYS - Materials Science Pub Date : 2024-09-10 DOI: arxiv-2409.06400

Viviana P. Ramunni

引用次数: 0

Beyond designer's knowledge: Generating materials design hypotheses via large language models 超越设计师的知识：通过大型语言模型生成材料设计假设

arXiv - PHYS - Materials Science Pub Date : 2024-09-10 DOI: arxiv-2409.06756

Quanliang Liu, Maciej P. Polak, So Yeon Kim, MD Al Amin Shuvo, Hrishikesh Shridhar Deodhar, Jeongsoo Han, Dane Morgan, Hyunseok Oh

{"title":"Beyond designer's knowledge: Generating materials design hypotheses via large language models","authors":"Quanliang Liu, Maciej P. Polak, So Yeon Kim, MD Al Amin Shuvo, Hrishikesh Shridhar Deodhar, Jeongsoo Han, Dane Morgan, Hyunseok Oh","doi":"arxiv-2409.06756","DOIUrl":"https://doi.org/arxiv-2409.06756","url":null,"abstract":"Materials design often relies on human-generated hypotheses, a process\u0000inherently limited by cognitive constraints such as knowledge gaps and limited\u0000ability to integrate and extract knowledge implications, particularly when\u0000multidisciplinary expertise is required. This work demonstrates that large\u0000language models (LLMs), coupled with prompt engineering, can effectively\u0000generate non-trivial materials hypotheses by integrating scientific principles\u0000from diverse sources without explicit design guidance by human experts. These\u0000include design ideas for high-entropy alloys with superior cryogenic properties\u0000and halide solid electrolytes with enhanced ionic conductivity and formability.\u0000These design ideas have been experimentally validated in high-impact\u0000publications in 2023 not available in the LLM training data, demonstrating the\u0000LLM's ability to generate highly valuable and realizable innovative ideas not\u0000established in the literature. Our approach primarily leverages materials\u0000system charts encoding processing-structure-property relationships, enabling\u0000more effective data integration by condensing key information from numerous\u0000papers, and evaluation and categorization of numerous hypotheses for human\u0000cognition, both through the LLM. This LLM-driven approach opens the door to new\u0000avenues of artificial intelligence-driven materials discovery by accelerating\u0000design, democratizing innovation, and expanding capabilities beyond the\u0000designer's direct knowledge.","PeriodicalId":501234,"journal":{"name":"arXiv - PHYS - Materials Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187922","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}

引用次数: 0