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Materials cartography: A forward-looking perspective on materials representation and devising better maps
DyFraNet: Forecasting and backcasting dynamic fracture mechanics in space and time using a 2D-to-3D deep neural network
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Materials cartography: A forward-looking perspective on materials representation and devising better maps
In Special Collection:
2023 Papers with Best Practices in Data Sharing and Comprehensive Background Review
Steven B. Torrisi; Martin Z. Bazant; Alexander E. Cohen; Min Gee Cho; Jens S. Hummelshøj; Linda Hung; Gaurav Kamat; Arash Khajeh; Adeesh Kolluru; Xiangyun Lei; Handong Ling; Joseph H. Montoya; Tim Mueller; Aini Palizhati; Benjamin A. Paren; Brandon Phan; Jacob Pietryga; Elodie Sandraz; Daniel Schweigert; Yang Shao-Horn; Amalie Trewartha; Ruijie Zhu; Debbie Zhuang; Shijing Sun
APL Mach. Learn. 1, 020901 (2023)
https://doi.org/10.1063/5.0149804
REVIEWS
ARTICLES
Automatic identification of edge localized modes in the DIII-D tokamak
In Special Collection:
2023 Papers with Best Practices in Data Sharing and Comprehensive Background Review
APL Mach. Learn. 1, 026102 (2023)
https://doi.org/10.1063/5.0134001
Pulse-stream impact on recognition accuracy of reservoir computing from SiO2-based low power memory devices
APL Mach. Learn. 1, 026103 (2023)
https://doi.org/10.1063/5.0131524
Stoichiometric growth of SrTiO3 films via Bayesian optimization with adaptive prior mean
In Special Collection:
2023 Papers with Best Practices in Data Sharing and Comprehensive Background Review
Yuki K. Wakabayashi; Takuma Otsuka; Yoshiharu Krockenberger; Hiroshi Sawada; Yoshitaka Taniyasu; Hideki Yamamoto
APL Mach. Learn. 1, 026104 (2023)
https://doi.org/10.1063/5.0132768
DyFraNet: Forecasting and backcasting dynamic fracture mechanics in space and time using a 2D-to-3D deep neural network
In Special Collection:
2023 Papers with Best Practices in Data Sharing and Comprehensive Background Review
APL Mach. Learn. 1, 026105 (2023)
https://doi.org/10.1063/5.0135015
Deep learning of nonlinear flame fronts development due to Darrieus–Landau instability
APL Mach. Learn. 1, 026106 (2023)
https://doi.org/10.1063/5.0139857
Analysis of VMM computation strategies to implement BNN applications on RRAM arrays
APL Mach. Learn. 1, 026108 (2023)
https://doi.org/10.1063/5.0139583
Physics-constrained 3D convolutional neural networks for electrodynamics
APL Mach. Learn. 1, 026109 (2023)
https://doi.org/10.1063/5.0132433
Label free identification of different cancer cells using deep learning-based image analysis
In Special Collection:
2023 Papers with Best Practices in Data Sharing and Comprehensive Background Review
APL Mach. Learn. 1, 026110 (2023)
https://doi.org/10.1063/5.0141730
Glass transition of amorphous polymeric materials informed by machine learning
APL Mach. Learn. 1, 026111 (2023)
https://doi.org/10.1063/5.0137357
A machine learning-based prediction of crystal orientations for multicrystalline materials
In Special Collection:
2023 Papers with Best Practices in Data Sharing and Comprehensive Background Review
APL Mach. Learn. 1, 026113 (2023)
https://doi.org/10.1063/5.0138099
AnalogVNN: A fully modular framework for modeling and optimizing photonic neural networks
APL Mach. Learn. 1, 026116 (2023)
https://doi.org/10.1063/5.0134156
Unsupervised machine learning discovery of structural units and transformation pathways from imaging data
APL Mach. Learn. 1, 026117 (2023)
https://doi.org/10.1063/5.0147316
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.
A tutorial on the Bayesian statistical approach to inverse problems
Faaiq G. Waqar, Swati Patel, et al.