The piezoelectric materials enable the mutual conversion between mechanical and electrical energy,which drive a multi-billion dollar industry through their applications as sensors,actuators,and energy harvesters.The t...The piezoelectric materials enable the mutual conversion between mechanical and electrical energy,which drive a multi-billion dollar industry through their applications as sensors,actuators,and energy harvesters.The third-rank piezoelectric tensor is the core matrices for piezoelectric materials and their devices.However,the high costs of obtaining full piezoelectric tensor data through either experimental or computational methods make a significant challenge.Here,we propose an equivariant attention tensor graph neural network(EATGNN)that can identify crystal symmetry and remain independent of the reference frame,ultimately enabling the accurate prediction of the complete third-rank piezoelectric tensor.Especially,we perform an irreducible decomposition of the piezoelectric tensor into four irreducible representations to efficiently reserve the symmetry under group transformation operations.Our results further demonstrate that this model performs well in both bulk and twodimensional materials.Finally,combining EATGNN with first-principles calculations,we discovered several potential high-performance piezoelectric materials.展开更多
Designing materials with targeted lattice thermal conductivity(LTC)demands electronic-level insight into chemical bonding.We introduce two bonding descriptors,namely normalized negative integrated COHP(-ICOHP)and norm...Designing materials with targeted lattice thermal conductivity(LTC)demands electronic-level insight into chemical bonding.We introduce two bonding descriptors,namely normalized negative integrated COHP(-ICOHP)and normalized integrated COBI,that correlate strongly with LTC and rattling(meansquared displacement),surpassing empirical rules and the unnormalized−ICOHP across>4500 inorganic crystals by first-principles.We train a crystal attention graph neural network(CATGNN)to predict these descriptors and screen~200,000 database structures for extreme LTCs.From 367(533)candidates with low(high)normalized-ICOHP and normalized ICOBI,first-principles validation identifies 106 dynamically stable compounds with LTC<5Wm^(−1)K^(−1)(68%<2Wm^(−1)K^(−1))and 13 stable compounds with LTC>100Wm^(−1)K^(−1).The descriptors’low cost and clear physical meaning provide a rapid,reliable route to high-throughput discovery and inverse design of crystalline materials with ultralow or ultrahigh LTC for applications in thermal insulation,thermoelectrics,and electronics cooling.展开更多
The impact of pesticides on insect pollinators has caused worldwide concern. Both global bee decline and stopping the use of pesticides may have serious consequences for food security. Automated and accurate predictio...The impact of pesticides on insect pollinators has caused worldwide concern. Both global bee decline and stopping the use of pesticides may have serious consequences for food security. Automated and accurate prediction of chemical poisoning of honey bees is a challenging task owing to a lack of understanding of chemical toxicity and introspection. Deep learning(DL) shows potential utility for general and highly variable tasks across fields. Here, we developed a new DL model of deep graph attention convolutional neural networks(GACNN) with the combination of undirected graph(UG) and attention convolutional neural networks(ACNN) to accurately classify chemical poisoning of honey bees. We used a training dataset of 720 pesticides and an external validation dataset of 90 pesticides, which is one order of magnitude larger than the previous datasets. We tested its performance in two ways: poisonous versus nonpoisonous and GACNN versus other frequently-used machine learning models. The first case represents the accuracy in identifying bee poisonous chemicals. The second represents performance advantages. The GACNN achieved ~6% higher performance for predicting toxic samples and more stable with ~7%Matthews Correlation Coefficient(MCC) higher compared to all tested models, demonstrating GACNN is capable of accurately classifying chemicals and has considerable potential in practical applications.In addition, we also summarized and evaluated the mechanisms underlying the response of honey bees to chemical exposure based on the mapping of molecular similarity. Moreover, our cloud platform(http://beetox.cn) of this model provides low-cost universal access to information, which could vitally enhance environmental risk assessment.展开更多
Sensors produce a large amount of multivariate time series data to record the states of Internet of Things(IoT)systems.Multivariate time series timestamp anomaly detection(TSAD)can identify timestamps of attacks and m...Sensors produce a large amount of multivariate time series data to record the states of Internet of Things(IoT)systems.Multivariate time series timestamp anomaly detection(TSAD)can identify timestamps of attacks and malfunctions.However,it is necessary to determine which sensor or indicator is abnormal to facilitate a more detailed diagnosis,a process referred to as fine-grained anomaly detection(FGAD).Although further FGAD can be extended based on TSAD methods,existing works do not provide a quantitative evaluation,and the performance is unknown.Therefore,to tackle the FGAD problem,this paper first verifies that the TSAD methods achieve low performance when applied to the FGAD task directly because of the excessive fusion of features and the ignoring of the relationship’s dynamic changes between indicators.Accordingly,this paper proposes a mul-tivariate time series fine-grained anomaly detection(MFGAD)framework.To avoid excessive fusion of features,MFGAD constructs two sub-models to independently identify the abnormal timestamp and abnormal indicator instead of a single model and then combines the two kinds of abnormal results to detect the fine-grained anomaly.Based on this framework,an algorithm based on Graph Attention Neural Network(GAT)and Attention Convolutional Long-Short Term Memory(A-ConvLSTM)is proposed,in which GAT learns temporal features of multiple indicators to detect abnormal timestamps and A-ConvLSTM captures the dynamic relationship between indicators to identify abnormal indicators.Extensive simulations on a real-world dataset demonstrate that the proposed algorithm can achieve a higher F1 score and hit rate than the extension of existing TSAD methods with the benefit of two independent sub-models for timestamp and indicator detection.展开更多
基金supported by the National Key R&D Program of China(2019YFE0112000)the Zhejiang Provincial Natural Science Foundation of China(LR21A040001,LDT23F04014F01)the National Natural Science Foundation of China(No.11974307).
文摘The piezoelectric materials enable the mutual conversion between mechanical and electrical energy,which drive a multi-billion dollar industry through their applications as sensors,actuators,and energy harvesters.The third-rank piezoelectric tensor is the core matrices for piezoelectric materials and their devices.However,the high costs of obtaining full piezoelectric tensor data through either experimental or computational methods make a significant challenge.Here,we propose an equivariant attention tensor graph neural network(EATGNN)that can identify crystal symmetry and remain independent of the reference frame,ultimately enabling the accurate prediction of the complete third-rank piezoelectric tensor.Especially,we perform an irreducible decomposition of the piezoelectric tensor into four irreducible representations to efficiently reserve the symmetry under group transformation operations.Our results further demonstrate that this model performs well in both bulk and twodimensional materials.Finally,combining EATGNN with first-principles calculations,we discovered several potential high-performance piezoelectric materials.
基金supported in part by the NSF(award numbers 2110033,2311202,2320292)SC EPSCoR/IDeA Program under NSF OIA-1655740(23-GC01)+2 种基金R.R.acknowledges financial support by the Severo Ochoa Centers of Excellence Program under grant CEX2023-001263-Sby the Generalitat de Catalunya under grant 2021 SGR 01519Calculations were performed at the Centro de Supercomputación de Galicia(CESGA)within actions FI-2023-1-0003,FI-2023-2-0005,and FI-2024-1-0012 of the Red Española de Supercomputación(RES).
文摘Designing materials with targeted lattice thermal conductivity(LTC)demands electronic-level insight into chemical bonding.We introduce two bonding descriptors,namely normalized negative integrated COHP(-ICOHP)and normalized integrated COBI,that correlate strongly with LTC and rattling(meansquared displacement),surpassing empirical rules and the unnormalized−ICOHP across>4500 inorganic crystals by first-principles.We train a crystal attention graph neural network(CATGNN)to predict these descriptors and screen~200,000 database structures for extreme LTCs.From 367(533)candidates with low(high)normalized-ICOHP and normalized ICOBI,first-principles validation identifies 106 dynamically stable compounds with LTC<5Wm^(−1)K^(−1)(68%<2Wm^(−1)K^(−1))and 13 stable compounds with LTC>100Wm^(−1)K^(−1).The descriptors’low cost and clear physical meaning provide a rapid,reliable route to high-throughput discovery and inverse design of crystalline materials with ultralow or ultrahigh LTC for applications in thermal insulation,thermoelectrics,and electronics cooling.
基金This work was supported in part by the National Key Research and Development Program of China(2017YFD0200506)the National Natural Science Foundation of China(21837001 and 21907036).
文摘The impact of pesticides on insect pollinators has caused worldwide concern. Both global bee decline and stopping the use of pesticides may have serious consequences for food security. Automated and accurate prediction of chemical poisoning of honey bees is a challenging task owing to a lack of understanding of chemical toxicity and introspection. Deep learning(DL) shows potential utility for general and highly variable tasks across fields. Here, we developed a new DL model of deep graph attention convolutional neural networks(GACNN) with the combination of undirected graph(UG) and attention convolutional neural networks(ACNN) to accurately classify chemical poisoning of honey bees. We used a training dataset of 720 pesticides and an external validation dataset of 90 pesticides, which is one order of magnitude larger than the previous datasets. We tested its performance in two ways: poisonous versus nonpoisonous and GACNN versus other frequently-used machine learning models. The first case represents the accuracy in identifying bee poisonous chemicals. The second represents performance advantages. The GACNN achieved ~6% higher performance for predicting toxic samples and more stable with ~7%Matthews Correlation Coefficient(MCC) higher compared to all tested models, demonstrating GACNN is capable of accurately classifying chemicals and has considerable potential in practical applications.In addition, we also summarized and evaluated the mechanisms underlying the response of honey bees to chemical exposure based on the mapping of molecular similarity. Moreover, our cloud platform(http://beetox.cn) of this model provides low-cost universal access to information, which could vitally enhance environmental risk assessment.
基金supported in part by the National Natural Science Foundation of China under Grant 62272062the Researchers Supporting Project number.(RSP2023R102)King Saud University+5 种基金Riyadh,Saudi Arabia,the Open Research Fund of the Hunan Provincial Key Laboratory of Network Investigational Technology under Grant 2018WLZC003the National Science Foundation of Hunan Province under Grant 2020JJ2029the Hunan Provincial Key Research and Development Program under Grant 2022GK2019the Science Fund for Creative Research Groups of Hunan Province under Grant 2020JJ1006the Scientific Research Fund of Hunan Provincial Transportation Department under Grant 202143the Open Fund of Key Laboratory of Safety Control of Bridge Engineering,Ministry of Education(Changsha University of Science Technology)under Grant 21KB07.
文摘Sensors produce a large amount of multivariate time series data to record the states of Internet of Things(IoT)systems.Multivariate time series timestamp anomaly detection(TSAD)can identify timestamps of attacks and malfunctions.However,it is necessary to determine which sensor or indicator is abnormal to facilitate a more detailed diagnosis,a process referred to as fine-grained anomaly detection(FGAD).Although further FGAD can be extended based on TSAD methods,existing works do not provide a quantitative evaluation,and the performance is unknown.Therefore,to tackle the FGAD problem,this paper first verifies that the TSAD methods achieve low performance when applied to the FGAD task directly because of the excessive fusion of features and the ignoring of the relationship’s dynamic changes between indicators.Accordingly,this paper proposes a mul-tivariate time series fine-grained anomaly detection(MFGAD)framework.To avoid excessive fusion of features,MFGAD constructs two sub-models to independently identify the abnormal timestamp and abnormal indicator instead of a single model and then combines the two kinds of abnormal results to detect the fine-grained anomaly.Based on this framework,an algorithm based on Graph Attention Neural Network(GAT)and Attention Convolutional Long-Short Term Memory(A-ConvLSTM)is proposed,in which GAT learns temporal features of multiple indicators to detect abnormal timestamps and A-ConvLSTM captures the dynamic relationship between indicators to identify abnormal indicators.Extensive simulations on a real-world dataset demonstrate that the proposed algorithm can achieve a higher F1 score and hit rate than the extension of existing TSAD methods with the benefit of two independent sub-models for timestamp and indicator detection.
