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EDITORIALS
Webinar Recap: Fostering a New Data Culture with APL Machine Learning
APL Mach. Learn. 2, 030401 (2024)
https://doi.org/10.1063/5.0230221
TUTORIALS
Tutorials: Physics-informed machine learning methods of computing 1D phase-field models
APL Mach. Learn. 2, 031101 (2024)
https://doi.org/10.1063/5.0205159
ARTICLES
Domain wall and magnetic tunnel junction hybrid for on-chip learning in UNet architecture
APL Mach. Learn. 2, 036101 (2024)
https://doi.org/10.1063/5.0214042
Machine-learning nowcasting of the Atlantic Meridional Overturning Circulation
APL Mach. Learn. 2, 036103 (2024)
https://doi.org/10.1063/5.0207539
Sim2Real in reconstructive spectroscopy: Deep learning with augmented device-informed data simulation
APL Mach. Learn. 2, 036106 (2024)
https://doi.org/10.1063/5.0209339
Accelerating simulations of strained-film growth by deep learning: Finite element method accuracy over long time scales
Daniele Lanzoni; Fabrizio Rovaris; Luis Martín-Encinar; Andrea Fantasia; Roberto Bergamaschini; Francesco Montalenti
APL Mach. Learn. 2, 036108 (2024)
https://doi.org/10.1063/5.0221363
Dreaming of electrical waves: Generative modeling of cardiac excitation waves using diffusion models
APL Mach. Learn. 2, 036113 (2024)
https://doi.org/10.1063/5.0194391
Dual-modality ghost diffraction in a complex disordered environment using untrained neural networks
APL Mach. Learn. 2, 036114 (2024)
https://doi.org/10.1063/5.0222851
Machine-learning-derived thermal conductivity of two-dimensional TiS2/MoS2 van der Waals heterostructures
APL Mach. Learn. 2, 036115 (2024)
https://doi.org/10.1063/5.0205702
Brain-inspired learning in artificial neural networks: A review
Samuel Schmidgall, Rojin Ziaei, et al.
In-memory computing with emerging memory devices: Status and outlook
P. Mannocci, M. Farronato, et al.
Deep language models for interpretative and predictive materials science
Yiwen Hu, Markus J. Buehler