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Issues

Volume 1, Issue 3
September 2023
Issue Cover
All Issues
EISSN 2770-9019
In this Issue

EDITORIALS

Tutorials at APL Machine Learning: To share, to envision, and to help others learn
Shijing Sun
APL Mach. Learn. 1, 030401 (2023) https://doi.org/10.1063/5.0175787
View articletitled, Tutorials at <em>APL Machine Learning</em>: To share, to envision, and to help others learn
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REVIEWS

Flexible optoelectronic synaptic transistors for neuromorphic visual systems
Xiao Liu; Dongke Li; Yue Wang; Deren Yang; Xiaodong Pi
APL Mach. Learn. 1, 031501 (2023) https://doi.org/10.1063/5.0163926
View articletitled, Flexible optoelectronic synaptic transistors for neuromorphic visual systems
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ARTICLES

Resistance transient dynamics in switchable perovskite memristors
Juan Bisquert; Agustín Bou; Antonio Guerrero; Enrique Hernández-Balaguera
APL Mach. Learn. 1, 036101 (2023) https://doi.org/10.1063/5.0153289
View articletitled, Resistance transient dynamics in switchable perovskite memristors
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Machine learning guided optimal composition selection of niobium alloys for high temperature applications
Trupti Mohanty; K. S. Ravi Chandran; Taylor D. Sparks
APL Mach. Learn. 1, 036102 (2023) https://doi.org/10.1063/5.0129528
View articletitled, Machine learning guided optimal composition selection of niobium alloys for high temperature applications
Supplementary Material
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Automatic graph representation algorithm for heterogeneous catalysis
Zachary Gariepy; ZhiWen Chen; Isaac Tamblyn; Chandra Veer Singh; Conrard Giresse Tetsassi Feugmo
APL Mach. Learn. 1, 036103 (2023) https://doi.org/10.1063/5.0140487
View articletitled, Automatic graph representation algorithm for heterogeneous catalysis
Supplementary Material
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Simulation of the effect of material properties on yttrium oxide memristor-based artificial neural networks
F. Aguirre; E. Piros; N. Kaiser; T. Vogel; S. Petzold; J. Gehrunger; T. Oster; K. Hofmann; C. Hochberger; J. Suñé; L. Alff; E. Miranda
APL Mach. Learn. 1, 036104 (2023) https://doi.org/10.1063/5.0143926
View articletitled, Simulation of the effect of material properties on yttrium oxide memristor-based artificial neural networks
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Scalable wavelength-multiplexing photonic reservoir computing
Rui-Qian Li; Yi-Wei Shen; Bao-De Lin; Jingyi Yu; Xuming He; Cheng Wang
APL Mach. Learn. 1, 036105 (2023) https://doi.org/10.1063/5.0158939
View articletitled, Scalable wavelength-multiplexing photonic reservoir computing
Supplementary Material
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3D CNN and grad-CAM based visualization for predicting generation of dislocation clusters in multicrystalline silicon
Kyoka Hara; Takuto Kojima; Kentaro Kutsukake; Hiroaki Kudo; Noritaka Usami
APL Mach. Learn. 1, 036106 (2023) https://doi.org/10.1063/5.0156044
View articletitled, 3D CNN and grad-CAM based visualization for predicting generation of dislocation clusters in multicrystalline silicon
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Improved prediction for failure time of multilayer ceramic capacitors (MLCCs): A physics-based machine learning approach
Pedram Yousefian; Alireza Sepehrinezhad; Adri C. T. van Duin; Clive A. Randall
APL Mach. Learn. 1, 036107 (2023) https://doi.org/10.1063/5.0158360
View articletitled, Improved prediction for failure time of multilayer ceramic capacitors (MLCCs): A physics-based machine learning approach
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Deep ensemble inverse model for image-based estimation of solar cell parameters
M. Battaglia; E. Comi; T. Stadelmann; R. Hiestand; B. Ruhstaller; E. Knapp
APL Mach. Learn. 1, 036108 (2023) https://doi.org/10.1063/5.0139707
View articletitled, Deep ensemble inverse model for image-based estimation of solar cell parameters
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Classification of multi-frequency RF signals by extreme learning, using magnetic tunnel junctions as neurons and synapses
Nathan Leroux; Danijela Marković; Dédalo Sanz-Hernández; Juan Trastoy; Paolo Bortolotti; Alejandro Schulman; Luana Benetti; Alex Jenkins; Ricardo Ferreira; Julie Grollier; Frank Alice Mizrahi
APL Mach. Learn. 1, 036109 (2023) https://doi.org/10.1063/5.0155447
View articletitled, Classification of multi-frequency RF signals by extreme learning, using magnetic tunnel junctions as neurons and synapses
Supplementary Material
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KoopmanLab: Machine learning for solving complex physics equations
Wei Xiong; Muyuan Ma; Xiaomeng Huang; Ziyang Zhang; Pei Sun; Yang Tian
APL Mach. Learn. 1, 036110 (2023) https://doi.org/10.1063/5.0157763
View articletitled, KoopmanLab: Machine learning for solving complex physics equations
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Experimental realization of a quantum classification: Bell state measurement via machine learning
Qing-Yuan Wu; Zhe Meng; Xiao-Xiao Chen; Jian Li; Jia-Zhi Yang; An-Ning Zhang
APL Mach. Learn. 1, 036111 (2023) https://doi.org/10.1063/5.0149414
View articletitled, Experimental realization of a quantum classification: Bell state measurement via machine learning
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Accelerated and interpretable prediction of local properties in composites
Shengtong Zhang; Satyajit Mojumder; Wing Kam Liu; Wei Chen; Daniel W. Apley
APL Mach. Learn. 1, 036112 (2023) https://doi.org/10.1063/5.0156517
View articletitled, Accelerated and interpretable prediction of local properties in composites
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Flexible optoelectronic synaptic transistors for neuromorphic visual systems
Deep language models for interpretative and predictive materials science
Scalable wavelength-multiplexing photonic reservoir computing
  • Online ISSN 2770-9019

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