Neural network approach for ferroelectric hafnium oxide phase identification at the atomistic scale

Zhang, Cheng; Xie, Xingran; Yang, Y.-J.; Wang, Chaolun; Luo, Chen; Bi, Hengchang; Wang, Yan; Chu, Junhao; Wu, Xing

doi:10.1016/j.mtelec.2023.100027

Cited by 5 publications

(7 citation statements)

References 24 publications

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“…Using the strategy of natural learning rate reduction, − it can be seen that the loss value of the VGG16 model basically remains stable after undergoing 60 epoch training with no significant increase in accuracy. And at the end of the training, it showed a greater loss state . The ResNet18 model showed faster learning efficiency at the beginning, with a constant loss value of 0.02 after 23 epoch training and a small loss at the end of training.…”

Section: Results and Discussionmentioning

confidence: 98%

“…And at the end of the training, it showed a greater loss state. 36 The ResNet18 model showed faster learning efficiency at the beginning, with a constant loss value of 0.02 after 23 epoch training and a small loss at the end of training. Figure 5c The study further evaluated the ResNet18 and VGG16 models through the test set and drew the confusion matrix, which is an important index to evaluate the performance of the classifier and is mainly used to compare the classification results with the actual measured values, as shown in Figures 5e and S45c.…”

Section: Machine Learning Resultsmentioning

confidence: 98%

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Room-Temperature Smart Sensor Based on Indium Acetate-Functionalized Perovskite CsPbBr₃ Nanocrystals for Monitoring Electrolyte in Lithium-Ion Batteries

Gao,

Zhuang,

Gao

et al. 2024

ACS Appl. Mater. Interfaces

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Monitoring electrolyte components is an effective means of determining the safety status of lithium-ion batteries. In this study, indium acetate was taken as a ligand to functionalize perovskite CsPbBr 3 nanocrystals, and then the room-temperature electrolyte sensor based on CsPbBr 3 nanocrystals with ligand indium acetate was prepared. The sensor offers high response, longterm stability (21 days), and low detection limits for ethyl methyl carbonate (10 ppm), diethyl carbonate (10 ppm), and ethyl butyrate (1 ppm) gases at room temperature and boasts a fast response/recovery time (1500 ppm, 58.27/103.82 s, 33.58/40.62 s, and 45.05/103.08 s, respectively). Density functional theory results show that the gas sensitivity comes from the adsorption of an electrolyte, which changes the density-of-state distribution so that the electrical response curve changes. And using computational fluid dynamics simulation, it was found that the time required for gas detection by the built-in sensor (3.1 s) was 8.7 times shorter than that of the implantable sensor. This work provides inspiration and rationale for embedding and integrating room-temperature sensors into lithium-ion batteries to monitor safety and health conditions.

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Section: Results and Discussionmentioning

confidence: 98%

Section: Machine Learning Resultsmentioning

confidence: 98%

Room-Temperature Smart Sensor Based on Indium Acetate-Functionalized Perovskite CsPbBr₃ Nanocrystals for Monitoring Electrolyte in Lithium-Ion Batteries

Gao,

Zhuang,

Gao

et al. 2024

ACS Appl. Mater. Interfaces

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“…A recent article by Cheng et al compares the classification networks commonly used in materials science, demonstrating that ResNet18 has a lower computational cost and better performance among other CNN architectures. [ 128 ] Most notably, ResNet18 was shown to work even better than the VT approach, which is the state-of-the-art network for the manipulation of natural images (images that a human being would observe in the real world). To date, we have found only a handful of papers in the area of NMs that use vision transformers for image recognition.…”

Section: Machine Learning Approaches In Nanotechnologymentioning

confidence: 99%

Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures

Kuznetsova,

Coogan,

Botov

et al. 2024

Advanced Materials

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Machine learning holds significant research potential in the field of nanotechnology, enabling nanomaterial structure and property predictions, facilitating the design of novel materials, and reducing the need for time‐consuming and labour‐intensive experiments and simulations. In contrast to their achiral counterparts, the application of machine learning for chiral nanomaterials and nanostructures is still in its infancy, with a limited number of publications to date. This is despite the great potential of machine learning to advance the development of new sustainable chiral materials with high values of optical activity, circularly polarised luminescence, and enantioselectivity, as well as for the analysis of structural chirality by electron microscopy. In this review, we provide an analysis of machine learning methods used in studying achiral nanomaterials, subsequently offering guidance on adapting and extending this work to chiral nanomaterials. We present an overview of chiral nanomaterials within the framework of synthesis‐structure‐property‐application relationships and provide insights on how to leverage machine learning for the study of these highly complex relationships. We also review and discuss some key recent publications on the application of machine learning for chiral nanomaterials. Finally, the review captures the key achievements, ongoing challenges, and the prospective outlook for this very important research field.This article is protected by copyright. All rights reserved

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“…Since STEM images are two-dimensional, they contain very subtle 3D structural information, which was meticulously analyzed. In addition, in a study involving polycrystalline Hf 0.5 Zr 0.5 O 2 , deep learning was used on 4D-STEM data [217] . This revealed that it was possible to distinguish between the centrosymmetric monoclinic P2 1 /c, tetragonal P4 2 /nmc phase, and the orthorhombic Pca2 1 phase with ferroelectric polarization in the TEM samples, disregarding factors such as thickness, tilt, and rotation.…”

Section: Figure 14mentioning

confidence: 99%

Reduced dimensional ferroelectric domains and their characterization techniques

Jang,

Choi

2024

Microstructures

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Ferroelectricity is one of the most major physical phenomena in electronic devices due to its sustainable polarity in the absence of an external electric field and its switchability in response to external stimuli. In alignment with the industry trend towards increasingly integrated devices, research into smaller-sized ferroelectric materials becomes indispensable. In the pursuit of achieving the pinnacle of device miniaturization, recent studies have unveiled materials exhibiting sub-nanometric, unit cell-level domains. Concurrently, advances in transmission electron microscopy-based structural characterization techniques have been made, enabling in-depth analysis of the intricate properties of these miniaturized ferroelectric materials. This review highlights the structural mechanism of ferroelectricity in a reduced scale, as well as the recent advancements in electron microscopy techniques for characterizing miniaturized ferroelectric domains, particularly in the fields of in-situ biasing and atomic scale imaging. We believe that this work will provide structural insights for engineering and characterizing ferroelectrics for the design of downsized high-density memory devices at the quantum limit.

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Neural network approach for ferroelectric hafnium oxide phase identification at the atomistic scale

Cited by 5 publications

References 24 publications

Room-Temperature Smart Sensor Based on Indium Acetate-Functionalized Perovskite CsPbBr₃ Nanocrystals for Monitoring Electrolyte in Lithium-Ion Batteries

Room-Temperature Smart Sensor Based on Indium Acetate-Functionalized Perovskite CsPbBr₃ Nanocrystals for Monitoring Electrolyte in Lithium-Ion Batteries

Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures

Reduced dimensional ferroelectric domains and their characterization techniques

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Neural network approach for ferroelectric hafnium oxide phase identification at the atomistic scale

Cited by 5 publications

References 24 publications

Room-Temperature Smart Sensor Based on Indium Acetate-Functionalized Perovskite CsPbBr3 Nanocrystals for Monitoring Electrolyte in Lithium-Ion Batteries

Room-Temperature Smart Sensor Based on Indium Acetate-Functionalized Perovskite CsPbBr3 Nanocrystals for Monitoring Electrolyte in Lithium-Ion Batteries

Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures

Reduced dimensional ferroelectric domains and their characterization techniques

Contact Info

Product

Resources

About

Room-Temperature Smart Sensor Based on Indium Acetate-Functionalized Perovskite CsPbBr₃ Nanocrystals for Monitoring Electrolyte in Lithium-Ion Batteries

Room-Temperature Smart Sensor Based on Indium Acetate-Functionalized Perovskite CsPbBr₃ Nanocrystals for Monitoring Electrolyte in Lithium-Ion Batteries