APL Machine Learning最新文献_第3页

Simulation of the effect of material properties on yttrium oxide memristor-based artificial neural networks 材料性质对氧化钇忆阻器影响的人工神经网络模拟

APL Machine Learning Pub Date : 2023-07-05 DOI: 10.1063/5.0143926

F. Aguirre, E. Piros, N. Kaiser, T. Vogel, S. Petzold, J. Gehrunger, T. Oster, K. Hofmann, C. Hochberger, J. Suñé, L. Alff, E. Miranda

{"title":"Simulation of the effect of material properties on yttrium oxide memristor-based artificial neural networks","authors":"F. Aguirre, E. Piros, N. Kaiser, T. Vogel, S. Petzold, J. Gehrunger, T. Oster, K. Hofmann, C. Hochberger, J. Suñé, L. Alff, E. Miranda","doi":"10.1063/5.0143926","DOIUrl":"https://doi.org/10.1063/5.0143926","url":null,"abstract":"This paper reports a simulation study concerning the effect of yttrium oxide stoichiometry on output features of a memristor-based single layer perceptron neural network. To carry out this investigation, a material-oriented behavioral compact model for bipolar-type memristive devices was developed and tested. The model is written for the SPICE (Simulation Program with Integrated Circuits Emphasis) simulator and considers as one of its inputs a measure of the oxygen flow used during the deposition of the switching layer. After a thorough statistical calibration of the model parameters using experimental current–voltage characteristics associated with different fabrication conditions, the corresponding curves were simulated and the results were compared with the original data. In this way, the average switching behavior of the structures (low and high current states, set and reset voltages, etc.) as a function of the oxygen content can be forecasted. In a subsequent phase, the collective response of the devices when used in a neural network was investigated in terms of the output features of the network (mainly power dissipation and power efficiency). The role played by parasitic elements, such as the line resistance and the read voltage influence on the inference accuracy, was also explored. Since a similar strategy can be applied to any other material-related fabrication parameter, the proposed approach opens up a new dimension for circuit designers, as the behavior of complex circuits employing devices with specific characteristics can be realistically assessed before fabrication.","PeriodicalId":229559,"journal":{"name":"APL Machine Learning","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126508664","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

Machine learning guided optimal composition selection of niobium alloys for high temperature applications

APL Machine Learning Pub Date : 2023-07-05 DOI: 10.1063/5.0129528

T. Mohanty, K. Chandran, Taylor D. Sparks

{"title":"Machine learning guided optimal composition selection of niobium alloys for high temperature applications","authors":"T. Mohanty, K. Chandran, Taylor D. Sparks","doi":"10.1063/5.0129528","DOIUrl":"https://doi.org/10.1063/5.0129528","url":null,"abstract":"Nickel- and cobalt-based superalloys are commonly used as turbine materials for high-temperature applications. However, their maximum operating temperature is limited to about 1100 °C. Therefore, to improve turbine efficiency, current research is focused on designing materials that can withstand higher temperatures. Niobium-based alloys can be considered as promising candidates because of their exceptional properties at elevated temperatures. The conventional approach to alloy design relies on phase diagrams and structure–property data of limited alloys and extrapolates this information into unexplored compositional space. In this work, we harness machine learning and provide an efficient design strategy for finding promising niobium-based alloy compositions with high yield and ultimate tensile strength. Unlike standard composition-based features, we use domain knowledge-based custom features and achieve higher prediction accuracy. We apply Bayesian optimization to screen out novel Nb-based quaternary and quinary alloy compositions and find these compositions have superior predicted strength over a range of temperatures. We develop a detailed design flow and include Python programming code, which could be helpful for accelerating alloy design in a limited alloy data regime.","PeriodicalId":229559,"journal":{"name":"APL Machine Learning","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121957202","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: 1

Resistance transient dynamics in switchable perovskite memristors 可开关钙钛矿记忆电阻器的电阻瞬态动力学

APL Machine Learning Pub Date : 2023-07-05 DOI: 10.1063/5.0153289

J. Bisquert, A. Bou, Antonio Guerrero, E. Hernández‐Balaguera

{"title":"Resistance transient dynamics in switchable perovskite memristors","authors":"J. Bisquert, A. Bou, Antonio Guerrero, E. Hernández‐Balaguera","doi":"10.1063/5.0153289","DOIUrl":"https://doi.org/10.1063/5.0153289","url":null,"abstract":"Memristor devices have been investigated for their properties of resistive modulation that can be used in data storage and brain-like computation elements as artificial synapses and neurons. Memristors are characterized by an onset of high current values under applied voltage that produces a transition to a low resistance state or successively to different stable states of increasing conductivity that implement synaptic weights. Here, we develop a nonlinear model to explain the variation with time of the voltage and the resistance and compare it to experimental results on ionic–electronic halide perovskite memristors. We find separate experimental signatures of the capacitive discharge and inductive current increase. We show that the capacitor produces an increase step of the resistance due to the influence of the series resistance. In contrast, the inductor feature associated with inverted hysteresis causes a decrease of the resistance, as observed experimentally. The chemical inductor feature dominates the potentiation effect in which the conductivity increases with the voltage stimulus. Our results enable a quantitative characterization of highly nonlinear electronic devices using a combination of techniques such as time transient decays and impedance spectroscopy.","PeriodicalId":229559,"journal":{"name":"APL Machine Learning","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130602332","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: 5

Automatic graph representation algorithm for heterogeneous catalysis 多相催化的自动图表示算法

APL Machine Learning Pub Date : 2023-06-07 DOI: 10.1063/5.0140487

Zachary Gariepy, Zhi-Wen Chen, Isaac Tamblyn, C. V. Singh, Conrard Giresse Tetsassi Feugmo

{"title":"Automatic graph representation algorithm for heterogeneous catalysis","authors":"Zachary Gariepy, Zhi-Wen Chen, Isaac Tamblyn, C. V. Singh, Conrard Giresse Tetsassi Feugmo","doi":"10.1063/5.0140487","DOIUrl":"https://doi.org/10.1063/5.0140487","url":null,"abstract":"One of the most appealing aspects of machine learning for material design is its high throughput exploration of chemical spaces, but to reach the ceiling of machine learning-aided exploration, more than current model architectures and processing algorithms are required. New architectures such as graph neural networks have seen significant research investments recently. For heterogeneous catalysis, defining substrate intramolecular bonds and adsorbate/substrate intermolecular bonds is a time-consuming and challenging process. Before applying a model, dataset pre-processing, node/bond descriptor design, and specific model constraints have to be considered. In this work, a framework designed to solve these issues is presented in the form of an automatic graph representation algorithm (AGRA) tool to extract the local chemical environment of metallic surface adsorption sites. This tool is able to gather multiple adsorption geometry datasets composed of different systems and combine them into a single model. To show AGRA’s excellent transferability and reduced computational cost compared to other graph representation methods, it was applied to five different catalytic reaction datasets and benchmarked against the Open Catalyst Projects graph representation method. The two oxygen reduction reaction (ORR) datasets with O/OH adsorbates obtained 0.053 eV root-mean-square deviation (RMSD) when combined together, whereas the three carbon dioxide reduction reaction datasets with CHO/CO/COOH obtained an average performance of 0.088 eV RMSD. To further display the algorithm’s versatility and extrapolation ability, a model was trained on a subset combination of all five datasets with an RMSD of 0.105 eV. This universal model was then used to predict a wide range of adsorption energies and an entirely new ORR catalyst system, which was then verified through density functional theory calculations.","PeriodicalId":229559,"journal":{"name":"APL Machine Learning","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133981411","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

Accelerating the design and development of polymeric materials via deep learning: Current status and future challenges

APL Machine Learning Pub Date : 2023-06-01 DOI: 10.1063/5.0131067

Dazi Li, Yi Ru, Zhudan Chen, Caibo Dong, Yining Dong, Jun Liu

{"title":"Accelerating the design and development of polymeric materials via deep learning: Current status and future challenges","authors":"Dazi Li, Yi Ru, Zhudan Chen, Caibo Dong, Yining Dong, Jun Liu","doi":"10.1063/5.0131067","DOIUrl":"https://doi.org/10.1063/5.0131067","url":null,"abstract":"The design and development of polymeric materials have been a hot domain for decades. However, traditional experiments and molecular simulations are time-consuming and labor-intensive, which no longer meet the requirements of new materials development. With the rapid advances of artificial intelligence and materials informatics, machine learning algorithms are increasingly applied in materials science, aiming to shorten the development period of new materials. With the evolution of polymeric materials, the structure of polymers has become more and more complex. Traditional machine learning algorithms often do not perform satisfactorily when dealing with complex data. Presently, deep learning algorithms, including deep neural networks, convolutional neural networks, generative adversarial networks, recurrent neural networks, and graph neural networks, show their uniquely excellent learning capabilities for large and complex data, which will be a powerful tool for the design and development of polymeric materials. This Review introduces principles of several currently popular deep learning algorithms and discusses their multiple applications in the materials field. Applications range from property prediction and molecular generation at the molecular level to structure identification and material synthesis in polymers. Finally, future challenges and opportunities for the application of deep learning in polymeric materials are discussed.","PeriodicalId":229559,"journal":{"name":"APL Machine Learning","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125808479","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: 1

Analysis of VMM computation strategies to implement BNN applications on RRAM arrays 在RRAM阵列上实现BNN应用的VMM计算策略分析

APL Machine Learning Pub Date : 2023-06-01 DOI: 10.1063/5.0139583

V. Parmar, Sandeep Kaur Kingra, S. Negi, M. Suri

{"title":"Analysis of VMM computation strategies to implement BNN applications on RRAM arrays","authors":"V. Parmar, Sandeep Kaur Kingra, S. Negi, M. Suri","doi":"10.1063/5.0139583","DOIUrl":"https://doi.org/10.1063/5.0139583","url":null,"abstract":"The growing interest in edge-AI solutions and advances in the field of quantized neural networks have led to hardware efficient binary neural networks (BNNs). Extreme BNNs utilize only binary weights and activations, making them more memory efficient. Such networks can be realized using exclusive-NOR (XNOR) gates and popcount circuits. The analog in-memory realization of BNNs utilizing emerging non-volatile memory devices has been widely explored recently. However, most realizations typically use 2T-2R synapses, resulting in sub-optimal area utilization. In this study, we investigate alternate computation mapping strategies to realize BNN using selectorless resistive random access memory arrays. A new differential computation scheme that shows a comparable performance with the well-established XNOR computation strategy is proposed. Through extensive experimental characterization, BNN implementation using a non-filamentary bipolar oxide-based random access memory device-based crossbar is demonstrated for two datasets: (i) experimental characterization was performed on a thermal-image based Rock-Paper-Scissors dataset to analyze the impact of sneak-paths with real-hardware experiments. (ii) Large-scale BNN simulations on the Fashion-MNIST dataset with multi-level cell characteristics of non-filamentary devices are performed to demonstrate the impact of device non-idealities.","PeriodicalId":229559,"journal":{"name":"APL Machine Learning","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126727834","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

AnalogVNN: A fully modular framework for modeling and optimizing photonic neural networks 用于建模和优化光子神经网络的完全模块化框架

APL Machine Learning Pub Date : 2023-06-01 DOI: 10.1063/5.0134156

Vivswan Shah, Nathan Youngblood

引用次数: 0

Pulse-stream impact on recognition accuracy of reservoir computing from SiO2-based low power memory devices 脉冲流对基于sio2的低功耗存储设备储层计算识别精度的影响

APL Machine Learning Pub Date : 2023-06-01 DOI: 10.1063/5.0131524

C. Tsioustas, P. Bousoulas, G. Kleitsiotis, D. Tsoukalas

{"title":"Pulse-stream impact on recognition accuracy of reservoir computing from SiO2-based low power memory devices","authors":"C. Tsioustas, P. Bousoulas, G. Kleitsiotis, D. Tsoukalas","doi":"10.1063/5.0131524","DOIUrl":"https://doi.org/10.1063/5.0131524","url":null,"abstract":"Reservoir computing (RC)-based neuromorphic applications exhibit extremely low power consumption, thus challenging the use of deep neural networks in terms of both consumption requirements and integration density. Under this perspective, this work focuses on the basic principles of RC systems. The ability of self-selective conductive-bridging random access memory devices to operate in two modes, namely, volatile and non-volatile, by regulating the applied voltage is first presented. We then investigate the relaxation time of these devices as a function of the applied amplitude and pulse duration, a critical step in determining the desired non-linearity by the reservoir. Moreover, we present an in-depth study of the impact of selecting the appropriate pulse-stream and its final effects on the total power consumption and recognition accuracy in a handwritten digit recognition application from the National Institute of Standards and Technology dataset. Finally, we conclude at the optimal pulse-stream of 3-bit, through the minimization of two cost criteria, with the total power remaining at 287 µW and simultaneously achieving 82.58% recognition accuracy upon the test set.","PeriodicalId":229559,"journal":{"name":"APL Machine Learning","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114198855","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

Materials cartography: A forward-looking perspective on materials representation and devising better maps 材料制图:对材料表示和设计更好的地图的前瞻性观点

APL Machine Learning Pub Date : 2023-06-01 DOI: 10.1063/5.0149804

Steven B. Torrisi, M. Bazant, Alexander E. Cohen, Min Gee Cho, J. Hummelshøj, Linda Hung, Gauravi Kamat, A. Khajeh, Adeesh Kolluru, Xiangyun Lei, Handong Ling, Joseph H. Montoya, Tim Mueller, Aini Palizhati, Benjamin A. Paren, Brandon Phan, J. Pietryga, Elodie Sandraz, D. Schweigert, Yang Shao-Horn, Amalie Trewartha, Ruijie Zhu, D. Zhuang, Shijing Sun

{"title":"Materials cartography: A forward-looking perspective on materials representation and devising better maps","authors":"Steven B. Torrisi, M. Bazant, Alexander E. Cohen, Min Gee Cho, J. Hummelshøj, Linda Hung, Gauravi Kamat, A. Khajeh, Adeesh Kolluru, Xiangyun Lei, Handong Ling, Joseph H. Montoya, Tim Mueller, Aini Palizhati, Benjamin A. Paren, Brandon Phan, J. Pietryga, Elodie Sandraz, D. Schweigert, Yang Shao-Horn, Amalie Trewartha, Ruijie Zhu, D. Zhuang, Shijing Sun","doi":"10.1063/5.0149804","DOIUrl":"https://doi.org/10.1063/5.0149804","url":null,"abstract":"Machine learning (ML) is gaining popularity as a tool for materials scientists to accelerate computation, automate data analysis, and predict materials properties. The representation of input material features is critical to the accuracy, interpretability, and generalizability of data-driven models for scientific research. In this Perspective, we discuss a few central challenges faced by ML practitioners in developing meaningful representations, including handling the complexity of real-world industry-relevant materials, combining theory and experimental data sources, and describing scientific phenomena across timescales and length scales. We present several promising directions for future research: devising representations of varied experimental conditions and observations, the need to find ways to integrate machine learning into laboratory practices, and making multi-scale informatics toolkits to bridge the gaps between atoms, materials, and devices.","PeriodicalId":229559,"journal":{"name":"APL Machine Learning","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128650970","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: 1

Unsupervised machine learning discovery of structural units and transformation pathways from imaging data 从成像数据中发现结构单元和转换路径的无监督机器学习

APL Machine Learning Pub Date : 2023-06-01 DOI: 10.1063/5.0147316

S. Kalinin, Ondrej Dyck, Ayana Ghosh, Yongtao Liu, B. Sumpter, M. Ziatdinov

{"title":"Unsupervised machine learning discovery of structural units and transformation pathways from imaging data","authors":"S. Kalinin, Ondrej Dyck, Ayana Ghosh, Yongtao Liu, B. Sumpter, M. Ziatdinov","doi":"10.1063/5.0147316","DOIUrl":"https://doi.org/10.1063/5.0147316","url":null,"abstract":"We show that unsupervised machine learning can be used to learn chemical transformation pathways from observational Scanning Transmission Electron Microscopy (STEM) data. To enable this analysis, we assumed the existence of atoms, a discreteness of atomic classes, and the presence of an explicit relationship between the observed STEM contrast and the presence of atomic units. With only these postulates, we developed a machine learning method leveraging a rotationally invariant variational autoencoder (VAE) that can identify the existing molecular fragments observed within a material. The approach encodes the information contained in STEM image sequences using a small number of latent variables, allowing the exploration of chemical transformation pathways by tracing the evolution of atoms in the latent space of the system. The results suggest that atomically resolved STEM data can be used to derive fundamental physical and chemical mechanisms involved, by providing encodings of the observed structures that act as bottom-up equivalents of structural order parameters. The approach also demonstrates the potential of variational (i.e., Bayesian) methods in the physical sciences and will stimulate the development of more sophisticated ways to encode physical constraints in the encoder–decoder architectures and generative physical laws and causal relationships in the latent space of VAEs.","PeriodicalId":229559,"journal":{"name":"APL Machine Learning","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130056193","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: 1