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Journal Articles

Combining machine learning and Mie theory to simplify particle characterization inside microchannels

Amirmohammad Taei, Rouhollah Karimzadeh, Mohammadmehdi Jahanbakhshian

Appl. Phys. Lett. 123, 124101 (2023)

https://doi.org/10.1063/5.0157486

Published: September 2023

View article titled, Combining machine learning and Mie theory to simplify particle characterization inside microchannels

Includes: Supplementary data

Journal Articles

High-throughput computation and machine learning of refractive index of polymers

Ankit Mishra, Pankaj Rajak, Ayu Irie, Shogo Fukushima, Rajiv K. Kalia, Aiichiro Nakano, Ken-ichi Nomura, Fuyuki Shimojo, Priya Vashishta

Appl. Phys. Lett. 123, 121901 (2023)

https://doi.org/10.1063/5.0161198

Published: September 2023

View article titled, High-throughput computation and machine learning of refractive index of polymers

Includes: Supplementary data

Journal Articles

Modeling the relationship between mechanical yield stress and material geometry using convolutional neural networks

Fahimeh Najafi, Henrik Andersen Sveinsson, Christer Dreierstad, Hans Erlend Bakken Glad, Anders Malthe-Sørenssen

Appl. Phys. Lett. 123, 111601 (2023)

https://doi.org/10.1063/5.0160338

Published: September 2023

View article titled, Modeling the relationship between mechanical yield stress and material geometry using convolutional neural networks

Journal Articles

Universal and interpretable classification of atomistic structural transitions via unsupervised graph learning

Bamidele Aroboto, Shaohua Chen, Tim Hsu, Brandon C. Wood, Yang Jiao, James Chapman

Appl. Phys. Lett. 123, 094103 (2023)

https://doi.org/10.1063/5.0156682

Published: September 2023

View article titled, Universal and interpretable classification of atomistic structural transitions via unsupervised graph learning

Journal Articles

Coherence modulation for anti-turbulence deep learning recognition of vortex beam

Junan Zhu, Hao Zhang, Zhiquan Hu, Xingyuan Lu, Qiwen Zhan, Yangjian Cai, Chengliang Zhao

Appl. Phys. Lett. 123, 091105 (2023)

https://doi.org/10.1063/5.0163922

Published: September 2023

View article titled, Coherence modulation for anti-turbulence deep learning recognition of vortex beam

Journal Articles

Bandgap structure in elastic metamaterials with curvy Bezier beams

Viacheslav Slesarenko

Appl. Phys. Lett. 123, 081702 (2023)

https://doi.org/10.1063/5.0156529

Published: August 2023

View article titled, Bandgap structure in elastic metamaterials with curvy Bezier beams

Journal Articles

Omnidirectional strain sensing using single flexible acoustic wave device with machine-learning algorithm

Zhangbin Ji, Jian Zhou, Yihao Guo, Yahui He, Huigao Duan, Yongqing Fu

Appl. Phys. Lett. 123, 054104 (2023)

https://doi.org/10.1063/5.0158874

Published: August 2023

View article titled, Omnidirectional strain sensing using single flexible acoustic wave device with machine-learning algorithm

Includes: Supplementary data

Journal Articles

Revisiting four-phonon scattering in WS₂ monolayer with machine learning potential

Guangwu Zhang, Shilin Dong, Chao Yang, Dan Han, Gongming Xin, Xinyu Wang

Appl. Phys. Lett. 123, 052205 (2023)

https://doi.org/10.1063/5.0159517

Published: August 2023

View article titled, Revisiting four-phonon scattering in WS2 monolayer with machine learning potential

Includes: Supplementary data

Journal Articles

Rapid acquisition of liquid thermophysical properties from pure metals to quaternary alloys by proposing a machine learning strategy

R. L. Xiao, K. L. Liu, Y. Ruan, B. Wei

Appl. Phys. Lett. 123, 052204 (2023)

https://doi.org/10.1063/5.0160046

Published: August 2023

View article titled, Rapid acquisition of liquid thermophysical properties from pure metals to quaternary alloys by proposing a machine learning strategy

Journal Articles

Machine learning predictions of high-Curie-temperature materials

Joshua F. Belot, Valentin Taufour, Stefano Sanvito, Gus L. W. Hart

Appl. Phys. Lett. 123, 042405 (2023)

https://doi.org/10.1063/5.0156377

Published: July 2023

View article titled, Machine learning predictions of high-Curie-temperature materials

Includes: Supplementary data

Journal Articles

Machine learning-assisted investigation of the impact of lithium-ion de-embedding on the thermal conductivity of LiFePO₄

Shi-Yi Li, Cheng-Wei Wu, Long-Ting Liu, Hui-Ling Kuang, Yu-Jia Zeng, Dan Wu, Guofeng Xie, Wu-Xing Zhou

Appl. Phys. Lett. 122, 262201 (2023)

https://doi.org/10.1063/5.0157078

Published: June 2023

View article titled, Machine learning-assisted investigation of the impact of lithium-ion de-embedding on the thermal conductivity of LiFePO4

Includes: Supplementary data

Journal Articles

Advancing flame retardant prediction: A self-enforcing machine learning approach for small datasets

Cheng Yan, Xiang Lin, Xiaming Feng, Hongyu Yang, Patrick Mensah, Guoqiang Li

Appl. Phys. Lett. 122, 251902 (2023)

https://doi.org/10.1063/5.0152195

Published: June 2023

View article titled, Advancing flame retardant prediction: A self-enforcing machine learning approach for small datasets

Includes: Supplementary data

Journal Articles

Predicting the motion of a high-Q pendulum subject to seismic perturbations using machine learning

Nicolas Heimann, Jan Petermann, Daniel Hartwig, Roman Schnabel, Ludwig Mathey

Appl. Phys. Lett. 122, 254101 (2023)

https://doi.org/10.1063/5.0144593

Published: June 2023

View article titled, Predicting the motion of a high-Q pendulum subject to seismic perturbations using machine learning

Includes: Supplementary data

Journal Articles

Graphic contrastive learning analyses of discontinuous molecular dynamics simulations: Study of protein folding upon adsorption

Size Zheng, Yong Wei, Yuewei Lin, Tao Wei

Appl. Phys. Lett. 122, 253701 (2023)

https://doi.org/10.1063/5.0157933

Published: June 2023

View article titled, Graphic contrastive learning analyses of discontinuous molecular dynamics simulations: Study of protein folding upon adsorption

Includes: Supplementary data

Journal Articles

Machine learning techniques for β / γ discrimination in phoswich detectors

Chengqian Li, Jingbin Lu, Huan Qu, Haodi Wang, Ruopu Li, Tianjiao Gao, Yuehui Zhang, Zhen Ren, Xinxu Yuan

Appl. Phys. Lett. 122, 244101 (2023)

https://doi.org/10.1063/5.0156554

Published: June 2023

View article titled, Machine learning techniques for β / γ discrimination in phoswich detectors

Journal Articles

Generative discovery of de novo chemical designs using diffusion modeling and transformer deep neural networks with application to deep eutectic solvents

Rachel K. Luu, Marcin Wysokowski, Markus J. Buehler

Appl. Phys. Lett. 122, 234103 (2023)

https://doi.org/10.1063/5.0155890

Published: June 2023

View article titled, Generative discovery of de novo chemical designs using diffusion modeling and transformer deep neural networks with application to deep eutectic solvents

Includes: Supplementary data

Journal Articles

Machine learning quantum-chemical bond scission in thermosets under extreme deformation

Zheng Yu, Nicholas E. Jackson

Appl. Phys. Lett. 122, 211906 (2023)

https://doi.org/10.1063/5.0150085

Published: May 2023

View article titled, Machine learning quantum-chemical bond scission in thermosets under extreme deformation

Includes: Supplementary data

Journal Articles

Discovery of sparse hysteresis models for piezoelectric materials

Abhishek Chandra, Bram Daniels, Mitrofan Curti, Koen Tiels, Elena A. Lomonova, Daniel M. Tartakovsky

Appl. Phys. Lett. 122, 214101 (2023)

https://doi.org/10.1063/5.0146134

Published: May 2023

View article titled, Discovery of sparse hysteresis models for piezoelectric materials

Journal Articles

Thermal switch based on ferroelasticity V^A-N binary compounds

Yuwen Zhang, Chunfeng Cui, Tao Ouyang, Chaoyu He, Jin Li, Mingxing Chen, Chao Tang

Appl. Phys. Lett. 122, 182204 (2023)

https://doi.org/10.1063/5.0152863

Published: May 2023

View article titled, Thermal switch based on ferroelasticity VA-N binary compounds

Includes: Supplementary data

Journal Articles

Predicting lattice thermal conductivity of semiconductors from atomic-information-enhanced CGCNN combined with transfer learning

Zeyu Wang, Jinlong Ma, Run Hu, Xiaobing Luo

Appl. Phys. Lett. 122, 152106 (2023)

https://doi.org/10.1063/5.0142150

Published: April 2023

View article titled, Predicting lattice thermal conductivity of semiconductors from atomic-information-enhanced CGCNN combined with transfer learning

Includes: Supplementary data

NARROW

Topics

Combining machine learning and Mie theory to simplify particle characterization inside microchannels

High-throughput computation and machine learning of refractive index of polymers

Modeling the relationship between mechanical yield stress and material geometry using convolutional neural networks

Universal and interpretable classification of atomistic structural transitions via unsupervised graph learning

Coherence modulation for anti-turbulence deep learning recognition of vortex beam

Bandgap structure in elastic metamaterials with curvy Bezier beams

Omnidirectional strain sensing using single flexible acoustic wave device with machine-learning algorithm

Revisiting four-phonon scattering in WS₂ monolayer with machine learning potential

Rapid acquisition of liquid thermophysical properties from pure metals to quaternary alloys by proposing a machine learning strategy

Machine learning predictions of high-Curie-temperature materials

Machine learning-assisted investigation of the impact of lithium-ion de-embedding on the thermal conductivity of LiFePO₄

Advancing flame retardant prediction: A self-enforcing machine learning approach for small datasets

Predicting the motion of a high-Q pendulum subject to seismic perturbations using machine learning

Graphic contrastive learning analyses of discontinuous molecular dynamics simulations: Study of protein folding upon adsorption

Machine learning techniques for β / γ discrimination in phoswich detectors

Generative discovery of de novo chemical designs using diffusion modeling and transformer deep neural networks with application to deep eutectic solvents

Machine learning quantum-chemical bond scission in thermosets under extreme deformation

Discovery of sparse hysteresis models for piezoelectric materials

Thermal switch based on ferroelasticity V^A-N binary compounds

Predicting lattice thermal conductivity of semiconductors from atomic-information-enhanced CGCNN combined with transfer learning

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