IOP出版社6月精选文章——Machine Learning

IOP出版社每月从年度重点期刊中精选两个主题的研究文章供大家阅读，本月的主题为Machine Learning/AI和Catalysis/Photocatalyst。这些文章体现了IOP期刊的高质量和创新性，并呈现了一些受关注的研究工作。欢迎大家阅读下载！

***Machine Learning/AI***

《Journal of Physics: Condensed Matter》
Machine learning approach to study quantum phase transitions of a frustrated one dimensional spin-1/2 system
Sk Saniur Rahaman, Sumit Haldar and Manoranjan Kumar
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Expectation–maximization machine learning model for micromechanical evaluation of thermally-cycled solder joints in a semiconductor
Tzu-Chia Chen
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Automated characterization of spatial and dynamical heterogeneity in supercooled liquids via implementation of machine learning
Viet Nguyen and Xueyu Song
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Machine learning molecular dynamics simulation of CO-driven formation of Cu clusters on the Cu(111) surface
Harry H Halim, Ryo Ueda and Yoshitada Morikawa
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Thermal conductivity of van der Waals heterostructure of 2D GeS and SnS based on machine learning interatomic potential
Wentao Li and Chenxiu Yang
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Predicting mechanical properties of CO2 hydrates: machine learning insights from molecular dynamics simulations
Yu Zhang, Zixuan Song, Yanwen Lin, Qiao Shi, Yongchao Hao, Yuequn Fu, Jianyang Wu and Zhisen Zhang
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Physics mechanisms underlying the optimization of coherent heat transfer across width-modulated nanowaveguides with calculations and machine learning
Antonios-Dimitrios Stefanou and Xanthippi Zianni
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Graph attention neural networks for mapping materials and molecules beyond short-range interatomic correlations
Yuanbin Liu, Xin Liu and Bingyang Cao
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《Journal of Physics D: Applied Physics》
Predicting the properties of perovskite materials by improved compositionally restricted attention-based networks and explainable machine learning
Zhan Hui, Min Wang, Jiacheng Wang, Jialu Chen, Xiang Yin and Yunliang Yue
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Machine learning-assisted inverse design of wide-bandgap acoustic topological devices
Xinxin Li, Yao Qin, Guangchen He, Feiyu Lian, Shuyu Zuo and Chengxin Cai
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《Material Research Express》
Comparative assessment of supervised machine learning algorithms for predicting geometric characteristics of laser cladded inconel 718
Hao Yang, Heran Geng, Marco Alfano and Junfeng Yuan
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Experimental study of the effect of different 3D printing parameters on tensile strength, using artificial neural network
Lahcen Hamouti, Omar El Farissi and Maryam Laouardi
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Investigation of effect of processing parameters of 3D printed NHS/EDC crosslinked carboxy methyl cellulose/gelatin hydrogels with machine learning techniques
Duygu Ege and Şule Arıcı
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Application of machine learning in MOFs for gas adsorption and separation
Chao Yang, Jingjing Qi, Anquan Wang, Jingyu Zha, Chao Liu and Shupeng Yao
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《JPhys Materials》
A critical review on the application of machine learning in supporting auxetic metamaterial design
Chonghui Zhang and Yaoyao Fiona Zhao
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Graph representation of multi-dimensional materials
Carina T Cai, Amanda J Parker and Amanda S Barnard
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Surface segregation in high-entropy alloys from alchemical machine learning
Arslan Mazitov, Maximilian A Springer, Nataliya Lopanitsyna, Guillaume Fraux, Sandip De and Michele Ceriotti
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Structural mode coupling in perovskite oxides using hypothesis-driven active learning
Ayana Ghosh, Palanichamy Gayathri, Monirul Shaikh and Saurabh Ghosh
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《Measurement Science and Technology》
Physics-informed deep-learning applications to experimental fluid mechanics
Hamidreza Eivazi, Yuning Wang and Ricardo Vinuesa
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A review on convolutional neural network in rolling bearing fault diagnosis
Xin Li, Zengqiang Ma, Zonghao Yuan, Tianming Mu, Guoxin Du, Yan Liang and Jingwen Liu
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Application of deep learning to fault diagnosis of rotating machineries
Hao Su, Ling Xiang and Aijun Hu
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《Neuromorphic Computing and Engineering》
Spike-based computation using classical recurrent neural networks
Florent De Geeter, Damien Ernst and Guillaume Drion
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Exploiting deep learning accelerators for neuromorphic workloads
Pao-Sheng Vincent Sun, Alexander Titterton, Anjlee Gopiani, Tim Santos, Arindam Basu, Wei D Lu and Jason K Eshraghian
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《Smart Materials and Structures》
Recent progress and future outlook of digital twins in structural health monitoring of civil infrastructure
Micheal Sakr and Ayan Sadhu
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《Machine Learning: Science and Technology》
WaveFormer: transformer-based denoising method for gravitational-wave data
He Wang, Yue Zhou, Zhoujian Cao, Zongkuan Guo and Zhixiang Ren
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Mud-Net: multi-domain deep unrolling network for simultaneous sparse-view and metal artifact reduction in computed tomography
Baoshun Shi, Ke Jiang, Shaolei Zhang, Qiusheng Lian, Yanwei Qin and Yunsong Zhao
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Variance extrapolation method for neural-network variational Monte Carlo
Weizhong Fu, Weiluo Ren and Ji Chen
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Transforming two-dimensional tensor networks into quantum circuits for supervised learning
Zhihui Song, Jinchen Xu, Xin Zhou, Xiaodong Ding and Zheng Shan
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δARD loss for low-contrast medical image segmentation
Yu Zhao, Xiaoyan Shen, Jiadong Chen, Wei Qian, He Ma and Liang Sang
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《Environmental Research Letters》
First estimation of hourly full-coverage ground-level ozone from Fengyun-4A satellite using machine learning
Ling Gao, Han Zhang, Fukun Yang, Wangshu Tan, Ronghua Wu and Yi Song
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《Journal of Neural Engineering》
Data augmentation for invasive brain–computer interfaces based on stereo-electroencephalography (SEEG)
Xiaolong Wu, Dingguo Zhang, Guangye Li, Xin Gao, Benjamin Metcalfe and Liang Chen
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