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EDITORIALS
Tutorials at APL Machine Learning: To share, to envision, and to help others learn
APL Mach. Learn. 1, 030401 (2023)
https://doi.org/10.1063/5.0175787
REVIEWS
Flexible optoelectronic synaptic transistors for neuromorphic visual systems
APL Mach. Learn. 1, 031501 (2023)
https://doi.org/10.1063/5.0163926
ARTICLES
Resistance transient dynamics in switchable perovskite memristors
APL Mach. Learn. 1, 036101 (2023)
https://doi.org/10.1063/5.0153289
Machine learning guided optimal composition selection of niobium alloys for high temperature applications
APL Mach. Learn. 1, 036102 (2023)
https://doi.org/10.1063/5.0129528
Automatic graph representation algorithm for heterogeneous catalysis
APL Mach. Learn. 1, 036103 (2023)
https://doi.org/10.1063/5.0140487
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
Scalable wavelength-multiplexing photonic reservoir computing
APL Mach. Learn. 1, 036105 (2023)
https://doi.org/10.1063/5.0158939
Deep ensemble inverse model for image-based estimation of solar cell parameters
APL Mach. Learn. 1, 036108 (2023)
https://doi.org/10.1063/5.0139707
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
KoopmanLab: Machine learning for solving complex physics equations
APL Mach. Learn. 1, 036110 (2023)
https://doi.org/10.1063/5.0157763
Experimental realization of a quantum classification: Bell state measurement via machine learning
APL Mach. Learn. 1, 036111 (2023)
https://doi.org/10.1063/5.0149414
Accelerated and interpretable prediction of local properties in composites
APL Mach. Learn. 1, 036112 (2023)
https://doi.org/10.1063/5.0156517