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Journal Articles

In silico design and prediction of metastable quaternary phases in Cu–Ni–Si–Cr alloys

Ángel Díaz Carral, Simon Gravelle, Maria Fyta

APL Mach. Learn. 2, 046109 (2024)

https://doi.org/10.1063/5.0228936

Published: November 2024

View article titled, In silico design and prediction of metastable quaternary phases in Cu–Ni–Si–Cr alloys

Journal Articles

Elastic constants from charge density distribution in FCC high-entropy alloys using CNN and DFT

Hossein Mirzaee, Ramin Soltanmohammadi, Nathan Linton, Jacob Fischer, Serveh Kamrava, Pejman Tahmasebi, Dilpuneet Aidhy

APL Mach. Learn. 2, 046104 (2024)

https://doi.org/10.1063/5.0229105

Published: October 2024

View article titled, Elastic constants from charge density distribution in FCC high-entropy alloys using CNN and DFT

Journal Articles

Enhancing high-fidelity neural network potentials through low-fidelity sampling

Gang Seob Jung

APL Mach. Learn. 2, 046102 (2024)

https://doi.org/10.1063/5.0222779

Published: October 2024

View article titled, Enhancing high-fidelity neural network potentials through low-fidelity sampling

Journal Articles

Machine-learning-derived thermal conductivity of two-dimensional TiS₂/MoS₂ van der Waals heterostructures

A. K. Nair, C. M. Da Silva, C. H. Amon

APL Mach. Learn. 2, 036115 (2024)

https://doi.org/10.1063/5.0205702

Published: September 2024

View article titled, Machine-learning-derived thermal conductivity of two-dimensional TiS2/MoS2 van der Waals heterostructures

Journal Articles

Ionic species representations for materials informatics

Anthony Onwuli, Keith T. Butler, Aron Walsh

APL Mach. Learn. 2, 036112 (2024)

https://doi.org/10.1063/5.0227009

Published: September 2024

View article titled, Ionic species representations for materials informatics

Journal Articles

Constructing coarse-grained models with physics-guided Gaussian process regression

Yating Fang, Qian Qian Zhao, Ryan B. Sills, Ahmed Aziz Ezzat

APL Mach. Learn. 2, 026123 (2024)

https://doi.org/10.1063/5.0190357

Published: June 2024

View article titled, Constructing coarse-grained models with physics-guided Gaussian process regression

Journal Articles

AlGaN/GaN MOS-HEMT enabled optoelectronic artificial synaptic devices for neuromorphic computing

Jiaxiang Chen, Haitao Du, Haolan Qu, Han Gao, Yitian Gu, Yitai Zhu, Wenbo Ye, Jun Zou, Hongzhi Wang, Xinbo Zou

APL Mach. Learn. 2, 026113 (2024)

https://doi.org/10.1063/5.0194083

Published: April 2024

View article titled, AlGaN/GaN MOS-HEMT enabled optoelectronic artificial synaptic devices for neuromorphic computing

Includes: Supplementary data

Journal Articles

Computational synthesis of a new generation of 2D-based perovskite quantum materials

Chinedu E. Ekuma

APL Mach. Learn. 2, 026102 (2024)

https://doi.org/10.1063/5.0189497

Published: April 2024

View article titled, Computational synthesis of a new generation of 2D-based perovskite quantum materials

Journal Articles

Improving the mechanical properties of Cantor-like alloys with Bayesian optimization

Valtteri Torsti, Tero Mäkinen, Silvia Bonfanti, Juha Koivisto, Mikko J. Alava

APL Mach. Learn. 2, 016119 (2024)

https://doi.org/10.1063/5.0179844

Published: March 2024

View article titled, Improving the mechanical properties of Cantor-like alloys with Bayesian optimization

Journal Articles

Integrating uncertainty into deep learning models for enhanced prediction of nanocomposite materials’ mechanical properties

Yuheng Wang, Guang Lin, Shengfeng Yang

APL Mach. Learn. 2, 016112 (2024)

https://doi.org/10.1063/5.0177062

Published: February 2024

View article titled, Integrating uncertainty into deep learning models for enhanced prediction of nanocomposite materials’ mechanical properties

Journal Articles

Multi-objective generative design of three-dimensional material structures

Zhengyang Zhang, Han Fang, Zhao Xu, Jiajie Lv, Yao Shen, Yanming Wang

APL Mach. Learn. 1, 046120 (2023)

https://doi.org/10.1063/5.0169432

Published: December 2023

View article titled, Multi-objective generative design of three-dimensional material structures

Includes: Supplementary data

Journal Articles

Unsupervised machine learning to analyze corneal tissue surfaces

Carolin A. Rickert, Fabio Henkel, Oliver Lieleg

APL Mach. Learn. 1, 046107 (2023)

https://doi.org/10.1063/5.0159502

Published: November 2023

View article titled, Unsupervised machine learning to analyze corneal tissue surfaces

Includes: Supplementary data

Journal Articles

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

Published: September 2023

View article titled, Accelerated and interpretable prediction of local properties in composites

Journal Articles

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

Published: July 2023

View article titled, Deep ensemble inverse model for image-based estimation of solar cell parameters

Journal Articles

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

Published: July 2023

View article titled, 3D CNN and grad-CAM based visualization for predicting generation of dislocation clusters in multicrystalline silicon

Journal Articles

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

Published: July 2023

View article titled, Machine learning guided optimal composition selection of niobium alloys for high temperature applications

Includes: Supplementary data

Journal Articles

Designing composition ratio of magnetic alloy multilayer for transverse thermoelectric conversion by Bayesian optimization

Naoki Chiba, Keisuke Masuda, Ken-ichi Uchida, Yoshio Miura

APL Mach. Learn. 1, 026114 (2023)

https://doi.org/10.1063/5.0140332

Published: May 2023

View article titled, Designing composition ratio of magnetic alloy multilayer for transverse thermoelectric conversion by Bayesian optimization

Includes: Supplementary data

Journal Articles

Glass transition of amorphous polymeric materials informed by machine learning

Anwen Hu, Yongdi Huang, Qionghai Chen, Wanhui Huang, Xiaohui Wu, Lihong Cui, Yining Dong, Jun Liu

APL Mach. Learn. 1, 026111 (2023)

https://doi.org/10.1063/5.0137357

Published: May 2023

View article titled, Glass transition of amorphous polymeric materials informed by machine learning

Includes: Supplementary data

Journal Articles

DyFraNet: Forecasting and backcasting dynamic fracture mechanics in space and time using a 2D-to-3D deep neural network

Yu-Chuan Hsu, Markus J. Buehler

APL Mach. Learn. 1, 026105 (2023)

https://doi.org/10.1063/5.0135015

Published: April 2023

View article titled, DyFraNet: Forecasting and backcasting dynamic fracture mechanics in space and time using a 2D-to-3D deep neural network

Journal Articles

A machine learning framework for elastic constants predictions in multi-principal element alloys

Nathan Linton, Dilpuneet S. Aidhy

APL Mach. Learn. 1, 016109 (2023)

https://doi.org/10.1063/5.0129928

Published: March 2023

View article titled, A machine learning framework for elastic constants predictions in multi-principal element alloys

Includes: Supplementary data

NARROW

Topics

In silico design and prediction of metastable quaternary phases in Cu–Ni–Si–Cr alloys

Elastic constants from charge density distribution in FCC high-entropy alloys using CNN and DFT

Enhancing high-fidelity neural network potentials through low-fidelity sampling

Machine-learning-derived thermal conductivity of two-dimensional TiS₂/MoS₂ van der Waals heterostructures

Ionic species representations for materials informatics

Constructing coarse-grained models with physics-guided Gaussian process regression

AlGaN/GaN MOS-HEMT enabled optoelectronic artificial synaptic devices for neuromorphic computing

Computational synthesis of a new generation of 2D-based perovskite quantum materials

Improving the mechanical properties of Cantor-like alloys with Bayesian optimization

Integrating uncertainty into deep learning models for enhanced prediction of nanocomposite materials’ mechanical properties

Multi-objective generative design of three-dimensional material structures

Unsupervised machine learning to analyze corneal tissue surfaces

Accelerated and interpretable prediction of local properties in composites

Deep ensemble inverse model for image-based estimation of solar cell parameters

3D CNN and grad-CAM based visualization for predicting generation of dislocation clusters in multicrystalline silicon

Machine learning guided optimal composition selection of niobium alloys for high temperature applications

Designing composition ratio of magnetic alloy multilayer for transverse thermoelectric conversion by Bayesian optimization

Glass transition of amorphous polymeric materials informed by machine learning

DyFraNet: Forecasting and backcasting dynamic fracture mechanics in space and time using a 2D-to-3D deep neural network

A machine learning framework for elastic constants predictions in multi-principal element alloys

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