APL Machine Learning最新文献

Computational experiments with cellular-automata generated images reveal intrinsic limitations of convolutional neural networks on pattern recognition tasks 细胞自动生成图像的计算实验揭示了卷积神经网络在模式识别任务中的内在局限性

APL Machine Learning Pub Date : 2024-07-15 DOI: 10.1063/5.0213905

Weihua Lei, Cleber Zanchettin, Flávio A. O. Santos, Luís A. Nunes Amaral

{"title":"Computational experiments with cellular-automata generated images reveal intrinsic limitations of convolutional neural networks on pattern recognition tasks","authors":"Weihua Lei, Cleber Zanchettin, Flávio A. O. Santos, Luís A. Nunes Amaral","doi":"10.1063/5.0213905","DOIUrl":"https://doi.org/10.1063/5.0213905","url":null,"abstract":"The extraordinary success of convolutional neural networks (CNNs) in various computer vision tasks has revitalized the field of artificial intelligence. The out-sized expectations created by this extraordinary success have, however, been tempered by a recognition of CNNs’ fragility. Importantly, the magnitude of the problem is unclear due to a lack of rigorous benchmark datasets. Here, we propose a solution to the benchmarking problem that reveals the extent of the vulnerabilities of CNNs and of the methods used to provide interpretability to their predictions. We employ cellular automata (CA) to generate images with rigorously controllable characteristics. CA allow for the definition of both extraordinarily simple and highly complex discrete functions and allow for the generation of boundless datasets of images without repeats. In this work, we systematically investigate the fragility and interpretability of the three popular CNN architectures using CA-generated datasets. We find a sharp transition from a learnable phase to an unlearnable phase as the latent space entropy of the discrete CA functions increases. Furthermore, we demonstrate that shortcut learning is an inherent trait of CNNs. Given a dataset with an easy-to-learn and strongly predictive pattern, CNN will consistently learn the shortcut even if the pattern occurs only on a small fraction of the image. Finally, we show that widely used attribution methods aiming to add interpretability to CNN outputs are strongly CNN-architecture specific and vary widely in their ability to identify input regions of high importance to the model. Our results provide significant insight into the limitations of both CNNs and the approaches developed to add interpretability to their predictions and raise concerns about the types of tasks that should be entrusted to them.","PeriodicalId":502250,"journal":{"name":"APL Machine Learning","volume":"115 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141647072","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

Simulation-trained machine learning models for Lorentz transmission electron microscopy 用于洛伦兹透射电子显微镜的模拟训练机器学习模型

APL Machine Learning Pub Date : 2024-06-01 DOI: 10.1063/5.0197138

A. McCray, Alec Bender, Amanda Petford-Long, C. Phatak

{"title":"Simulation-trained machine learning models for Lorentz transmission electron microscopy","authors":"A. McCray, Alec Bender, Amanda Petford-Long, C. Phatak","doi":"10.1063/5.0197138","DOIUrl":"https://doi.org/10.1063/5.0197138","url":null,"abstract":"Understanding the collective behavior of complex spin textures, such as lattices of magnetic skyrmions, is of fundamental importance for exploring and controlling the emergent ordering of these spin textures and inducing phase transitions. It is also critical to understand the skyrmion–skyrmion interactions for applications such as magnetic skyrmion-enabled reservoir or neuromorphic computing. Magnetic skyrmion lattices can be studied using in situ Lorentz transmission electron microscopy (LTEM), but quantitative and statistically robust analysis of the skyrmion lattices from LTEM images can be difficult. In this work, we show that a convolutional neural network, trained on simulated data, can be applied to perform segmentation of spin textures and to extract quantitative data, such as spin texture size and location, from experimental LTEM images, which cannot be obtained manually. This includes quantitative information about skyrmion size, position, and shape, which can, in turn, be used to calculate skyrmion–skyrmion interactions and lattice ordering. We apply this approach to segmenting images of Néel skyrmion lattices so that we can accurately identify skyrmion size and deformation in both dense and sparse lattices. The model is trained using a large set of micromagnetic simulations as well as simulated LTEM images. This entirely open-source training pipeline can be applied to a wide variety of magnetic features and materials, enabling large-scale statistical studies of spin textures using LTEM.","PeriodicalId":502250,"journal":{"name":"APL Machine Learning","volume":"130 33","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141281750","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

Enhanced spectrum prediction using deep learning models with multi-frequency supplementary inputs 利用多频率补充输入的深度学习模型加强频谱预测

APL Machine Learning Pub Date : 2024-05-16 DOI: 10.1063/5.0203931

Xiaohua Xing, Yuqi Ren, Die Zou, Qiankun Zhang, Bingxuan Mao, Jianquan Yao, Deyi Xiong, Liang Wu

引用次数: 0

Cell detection with convolutional spiking neural network for neuromorphic cytometry 利用卷积尖峰神经网络进行细胞检测，实现神经形态细胞测定法

APL Machine Learning Pub Date : 2024-05-08 DOI: 10.1063/5.0199514

Ziyao Zhang, Haoxiang Yang, J. K. Eshraghian, Jiayin Li, Ken-Tye Yong, D. Vigolo, Helen M. McGuire, Omid Kavehei

{"title":"Cell detection with convolutional spiking neural network for neuromorphic cytometry","authors":"Ziyao Zhang, Haoxiang Yang, J. K. Eshraghian, Jiayin Li, Ken-Tye Yong, D. Vigolo, Helen M. McGuire, Omid Kavehei","doi":"10.1063/5.0199514","DOIUrl":"https://doi.org/10.1063/5.0199514","url":null,"abstract":"Imaging flow cytometry (IFC) is an advanced cell-analytic technology offering rich spatial information and fluorescence intensity for multi-parametric characterization. Manual gating in cytometry data enables the classification of discrete populations from the sample based on extracted features. However, this expert-driven technique can be subjective and laborious, often presenting challenges in reproducibility and being inherently limited to bivariate analysis. Numerous AI-driven cell classifications have recently emerged to automate the process of including multivariate data with enhanced reproducibility and accuracy. Our previous work demonstrated the early development of neuromorphic imaging cytometry, evaluating its feasibility in resolving conventional frame-based imaging systems’ limitations in data redundancy, fluorescence sensitivity, and compromised throughput. Herein, we adopted a convolutional spiking neural network (SNN) combined with the YOLOv3 model (SNN-YOLO) to perform cell classification and detection on label-free samples under neuromorphic vision. Spiking techniques are inherently suitable post-processing techniques for neuromorphic vision sensing. The experiment was conducted with polystyrene-based microparticles, THP-1, and LL/2 cell lines. The network’s performance was compared with that of a traditional YOLOv3 model fed with event-generated frame data to serve as a baseline. In this work, our SNN-YOLO outperformed the YOLOv3 baseline by achieving the highest average class accuracy of 0.974, compared to 0.962 for YOLOv3. Both models reported comparable performances across other key metrics and should be further explored for future auto-gating strategies and cytometry applications.","PeriodicalId":502250,"journal":{"name":"APL Machine Learning","volume":" 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140998464","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 development of thermodynamically consistent and physics-informed equation-of-state model through machine learning 通过机器学习开发热力学一致和物理信息的状态方程模型

APL Machine Learning Pub Date : 2024-05-07 DOI: 10.1063/5.0192447

J. Hinz, Dayou Yu, Deep Shankar Pandey, Hitesh Sapkota, Qi Yu, D. Mihaylov, V. V. Karasiev, S. Hu

引用次数: 0

Simulating CO2 diffusivity in rigid and flexible Mg-MOF-74 with machine-learning force fields 利用机器学习力场模拟刚性和柔性 Mg-MOF-74 中的二氧化碳扩散性

APL Machine Learning Pub Date : 2024-05-07 DOI: 10.1063/5.0190372

Bowen Zheng, Grace X. Gu, Carine Ribeiro dos Santos, Rodrigo Neumann Barros Ferreira, Mathias Steiner, Binquan Luan

引用次数: 0

Exploring the optimal design space of transparent perovskite solar cells for four-terminal tandem applications through Pareto front optimization 通过帕累托前沿优化探索四端串联应用透明过氧化物太阳能电池的最佳设计空间

APL Machine Learning Pub Date : 2024-04-24 DOI: 10.1063/5.0187208

Hu Quee Tan, Xinhai Zhao, Akhil Ambardekar, Erik Birgersson, Hansong Xue

{"title":"Exploring the optimal design space of transparent perovskite solar cells for four-terminal tandem applications through Pareto front optimization","authors":"Hu Quee Tan, Xinhai Zhao, Akhil Ambardekar, Erik Birgersson, Hansong Xue","doi":"10.1063/5.0187208","DOIUrl":"https://doi.org/10.1063/5.0187208","url":null,"abstract":"Machine learning algorithms can enhance the design and experimental processing of solar cells, resulting in increased conversion efficiency. In this study, we introduce a novel machine learning-based methodology for optimizing the Pareto front of four-terminal (4T) perovskite-copper indium selenide (CIS) tandem solar cells (TSCs). By training a neural network using the Bayesian regularization-backpropagation algorithm via Hammersley sampling, we achieve high prediction accuracy when testing with unseen data through random sampling. This surrogate model not only reduces computational costs but also potentially enhances device performance, increasing from 29.4% to 30.4% while simultaneously reducing material costs for fabrication by 50%. Comparing experimentally fabricated cells with the predicted optimal cells, the latter show a thinner front contact electrode, charge-carrier transport layer, and back contact electrode. Highly efficient perovskite cells identified from the Pareto front have a perovskite layer thickness ranging from 420 to 580 nm. Further analysis reveals the front contact electrode needs to be thin, while the back contact electrode can have a thickness ranging from 100 to 145 nm and still achieve high efficiency. The charge-carrier transport layers play a crucial role in minimizing interface recombination and ensuring unidirectional current flow. The optimal design space suggests thinner electron and hole transport layer thicknesses of 7 nm, down from 23 to 10 nm, respectively. It indicates a balanced charge-carrier extraction is crucial for an optimized perovskite cell. Overall, the presented methodology and optimized design parameters have the potential to enhance the performance of 4T perovskite/CIS TSC while reducing material fabrication costs.","PeriodicalId":502250,"journal":{"name":"APL Machine Learning","volume":"100 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140659225","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

AlGaN/GaN MOS-HEMT enabled optoelectronic artificial synaptic devices for neuromorphic computing 用于神经形态计算的 AlGaN/GaN MOS-HEMT 光电人工突触器件

APL Machine Learning Pub Date : 2024-04-24 DOI: 10.1063/5.0194083

Jiaxiang Chen, Haitao Du, Haolan Qu, Han Gao, Yitian Gu, Yitai Zhu, Wenbo Ye, Jun Zou, Hongzhi Wang, Xinbo Zou

{"title":"AlGaN/GaN MOS-HEMT enabled optoelectronic artificial synaptic devices for neuromorphic computing","authors":"Jiaxiang Chen, Haitao Du, Haolan Qu, Han Gao, Yitian Gu, Yitai Zhu, Wenbo Ye, Jun Zou, Hongzhi Wang, Xinbo Zou","doi":"10.1063/5.0194083","DOIUrl":"https://doi.org/10.1063/5.0194083","url":null,"abstract":"Artificial optoelectronic synaptic transistors have attracted extensive research interest as an essential component for neuromorphic computing systems and brain emulation applications. However, performance challenges still remain for synaptic devices, including low energy consumption, high integration density, and flexible modulation. Employing trapping and detrapping relaxation, a novel optically stimulated synaptic transistor enabled by the AlGaN/GaN hetero-structure metal-oxide semiconductor high-electron-mobility transistor has been successfully demonstrated in this study. Synaptic functions, including excitatory postsynaptic current (EPSC), paired-pulse facilitation index, and transition from short-term memory to long-term memory, are well mimicked and explicitly investigated. In a single EPSC event, the AlGaN/GaN synaptic transistor shows the characteristics of low energy consumption and a high signal-to-noise ratio. The EPSC of the synaptic transistor can be synergistically modulated by both optical stimulation and gate/drain bias. Moreover, utilizing a convolution neural network, hand-written digit images were used to verify the data preprocessing capability for neuromorphic computing applications.","PeriodicalId":502250,"journal":{"name":"APL Machine Learning","volume":"51 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140664450","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

Self-supervised learning of shedding droplet dynamics during steam condensation 蒸汽冷凝过程中脱落液滴动力学的自监督学习

APL Machine Learning Pub Date : 2024-04-10 DOI: 10.1063/5.0188620

Siavash Khodakarami, Pouya Kabirzadeh, Nenad Miljkovic

{"title":"Self-supervised learning of shedding droplet dynamics during steam condensation","authors":"Siavash Khodakarami, Pouya Kabirzadeh, Nenad Miljkovic","doi":"10.1063/5.0188620","DOIUrl":"https://doi.org/10.1063/5.0188620","url":null,"abstract":"Knowledge of condensate shedding droplet dynamics provides important information for the characterization of two-phase heat and mass transfer phenomena. Detecting and segmenting the droplets during shedding requires considerable time and effort if performed manually. Here, we developed a self-supervised deep learning model for segmenting shedding droplets from a variety of dropwise and filmwise condensing surfaces. The model eliminates the need for image annotation by humans in the training step and, therefore, reduces labor significantly. The trained model achieved an average accuracy greater than 0.9 on a new unseen test dataset. After extracting the shedding droplet size and speed, we developed a data-driven model for shedding droplet dynamics based on condensation heat flux and surface properties such as wettability and tube diameter. Our results demonstrate that condensate droplet departure size is both heat flux and tube size dependent and follows different trends based on the condensation mode. The results of this work provide an annotation-free methodology for falling droplet segmentation as well as a statistical understanding of droplet dynamics during condensation.","PeriodicalId":502250,"journal":{"name":"APL Machine Learning","volume":"156 20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140717731","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

Multivariate Gaussian process surrogates for predicting basic structural parameters of refractory non-dilute random alloys 用于预测难熔非稀释随机合金基本结构参数的多变量高斯过程替代物

APL Machine Learning Pub Date : 2024-04-10 DOI: 10.1063/5.0186045

Cesar Ruiz, Anshu Raj, Shuozhi Xu

{"title":"Multivariate Gaussian process surrogates for predicting basic structural parameters of refractory non-dilute random alloys","authors":"Cesar Ruiz, Anshu Raj, Shuozhi Xu","doi":"10.1063/5.0186045","DOIUrl":"https://doi.org/10.1063/5.0186045","url":null,"abstract":"Refractory non-dilute random alloys consist of two or more principal refractory metals with complex interactions that modify their basic structural properties such as lattice parameters and elastic constants. Atomistic simulations (ASs) are an effective method to compute such basic structural parameters. However, accurate predictions from ASs are computationally expensive due to the size and number of atomistic structures required. To reduce the computational burden, multivariate Gaussian process regression (MVGPR) is proposed as a surrogate model that only requires computing a small number of configurations for training. The elemental atom percentage in the hyper-spherical coordinates is demonstrated to be an effective feature for surrogate modeling. An additive approximation of the full MVGPR model is also proposed to further reduce computations. To improve surrogate accuracy, active learning is used to select a small number of alloys to simulate. Numerical studies based on AS data show the accuracy of the surrogate methodology and the additive approximation, as well as the effectiveness and robustness of the active learning for selecting new alloy designs to simulate.","PeriodicalId":502250,"journal":{"name":"APL Machine Learning","volume":"104 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140720480","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