Volume 2 Issue 1 | APL Machine Learning

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Volume 2, Issue 1

March 2024

EISSN 2770-9019

PERSPECTIVES

In-memory and in-sensor reservoir computing with memristive devices

Ning Lin; Jia Chen; Ruoyu Zhao; Yangu He; Kwunhang Wong; Qinru Qiu; Zhongrui Wang; J. Joshua Yang

APL Mach. Learn. 2, 010901 (2024) https://doi.org/10.1063/5.0174863

Abstract

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A unifying perspective on non-stationary kernels for deeper Gaussian processes

Marcus M. Noack; Hengrui Luo; Mark D. Risser

APL Mach. Learn. 2, 010902 (2024) https://doi.org/10.1063/5.0176963

Abstract

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ARTICLES

Machine learning based hybrid ensemble models for prediction of organic dyes photophysical properties: Absorption wavelengths, emission wavelengths, and quantum yields

Kapil Dev Mahato; S. S. Gourab Kumar Das; Chandrashekhar Azad; Uday Kumar

APL Mach. Learn. 2, 016101 (2024) https://doi.org/10.1063/5.0181294

Abstract

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Learning thermodynamically constrained equations of state with uncertainty

Himanshu Sharma; Jim A. Gaffney; Dimitrios Tsapetis; Michael D. Shields

APL Mach. Learn. 2, 016102 (2024) https://doi.org/10.1063/5.0165298

Abstract

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Learning the stable and metastable phase diagram to accelerate the discovery of metastable phases of boron

Karthik Balasubramanian; Suvo Banik; Sukriti Manna; Srilok Srinivasan; Subramanian K. R. S. Sankaranarayanan

APL Mach. Learn. 2, 016103 (2024) https://doi.org/10.1063/5.0175994

Abstract

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A deep learning approach for gas sensor data regression: Incorporating surface state model and GRU-based model

In Special Collection: 2023 Papers with Best Practices in Data Sharing and Comprehensive Background Review

Yi Zhuang; Du Yin; Lang Wu; Gaoqiang Niu; Fei Wang

APL Mach. Learn. 2, 016104 (2024) https://doi.org/10.1063/5.0160983

Abstract

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Prediction and control of spatiotemporal chaos by learning conjugate tubular neighborhoods

Nazmi Burak Budanur

APL Mach. Learn. 2, 016105 (2024) https://doi.org/10.1063/5.0181022

Abstract

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M³ICRO: Machine learning-enabled compact photonic tensor core based on programmable multi-operand multimode interference

Jiaqi Gu; Hanqing Zhu; Chenghao Feng; Zixuan Jiang; Ray T. Chen; David Z. Pan

APL Mach. Learn. 2, 016106 (2024) https://doi.org/10.1063/5.0170965

Abstract

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Noise tailoring, noise annealing, and external perturbation injection strategies in memristive Hopfield neural networks

János Gergő Fehérvári; Zoltán Balogh; Tímea Nóra Török; András Halbritter

APL Mach. Learn. 2, 016107 (2024) https://doi.org/10.1063/5.0173662

Abstract

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Machine learning for rapid discovery of laminar flow channel wall modifications that enhance heat transfer

Yuri Koide; Arjun J. Kaithakkal; Matthias Schniewind; Bradley P. Ladewig; Alexander Stroh; Pascal Friederich

APL Mach. Learn. 2, 016108 (2024) https://doi.org/10.1063/5.0187783

Abstract

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Digitizing images of electrical-circuit schematics

Charles R. Kelly; Jacqueline M. Cole

APL Mach. Learn. 2, 016109 (2024) https://doi.org/10.1063/5.0177755

Abstract

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Completeness of atomic structure representations

Jigyasa Nigam; Sergey N. Pozdnyakov; Kevin K. Huguenin-Dumittan; Michele Ceriotti

APL Mach. Learn. 2, 016110 (2024) https://doi.org/10.1063/5.0160740

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Predicting wind farm wake losses with deep convolutional hierarchical encoder–decoder neural networks

David A. Romero; Saeede Hasanpoor; Enrico G. A. Antonini; Cristina H. Amon

APL Mach. Learn. 2, 016111 (2024) https://doi.org/10.1063/5.0168973

Abstract

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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

Abstract

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Contextual beamforming: Exploiting location and AI for enhanced wireless telecommunication performance

Jaspreet Kaur; Satyam Bhatti; Kang Tan; Olaoluwa R. Popoola; Muhammad Ali Imran; Rami Ghannam; Qammer H. Abbasi; Hasan T. Abbas

APL Mach. Learn. 2, 016113 (2024) https://doi.org/10.1063/5.0176422

Abstract

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Training self-learning circuits for power-efficient solutions

Menachem Stern; Sam Dillavou; Dinesh Jayaraman; Douglas J. Durian; Andrea J. Liu

APL Mach. Learn. 2, 016114 (2024) https://doi.org/10.1063/5.0181382

Abstract

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Physics-agnostic inverse design using transfer matrices

Nathaniel Morrison; Shuaiwei Pan; Eric Y. Ma

APL Mach. Learn. 2, 016115 (2024) https://doi.org/10.1063/5.0179457

Abstract

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Measuring thermal profiles in high explosives using neural networks

J. Greenhall; D. K. Zerkle; E. S. Davis; R. Broilo; C. Pantea

APL Mach. Learn. 2, 016116 (2024) https://doi.org/10.1063/5.0183886

Abstract

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Deep learning-enabled probing of irradiation-induced defects in time-series micrographs

Kory Burns; Kayvon Tadj; Tarun Allaparti; Liliana Arias; Nan Li; Assel Aitkaliyeva; Amit Misra; Mary C. Scott; Khalid Hattar

APL Mach. Learn. 2, 016117 (2024) https://doi.org/10.1063/5.0186046

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Random forests for detecting weak signals and extracting physical information: A case study of magnetic navigation

Mohammadamin Moradi; Zheng-Meng Zhai; Aaron Nielsen; Ying-Cheng Lai

APL Mach. Learn. 2, 016118 (2024) https://doi.org/10.1063/5.0189564

Abstract

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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

Abstract

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VERI-D: A new dataset and method for multi-camera vehicle re-identification of damaged cars under varying lighting conditions

Shao Liu; Sos S. Agaian

APL Mach. Learn. 2, 016120 (2024) https://doi.org/10.1063/5.0183408

Abstract

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Autonomous convergence of STM control parameters using Bayesian optimization

Ganesh Narasimha; Saban Hus; Arpan Biswas; Rama Vasudevan; Maxim Ziatdinov

APL Mach. Learn. 2, 016121 (2024) https://doi.org/10.1063/5.0185362

Abstract

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Accelerating defect predictions in semiconductors using graph neural networks

Md Habibur Rahman; Prince Gollapalli; Panayotis Manganaris; Satyesh Kumar Yadav; Ghanshyam Pilania; Brian DeCost; Kamal Choudhary; Arun Mannodi-Kanakkithodi

APL Mach. Learn. 2, 016122 (2024) https://doi.org/10.1063/5.0176333

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ERRATA

Erratum: “Learning the stable and metastable phase diagram to accelerate the discovery of metastable phases of boron” [APL Mach. Learn. 2, 016103 (2024)]

Karthik Balasubramanian; Suvo Banik; Sukriti Manna; Srilok Srinivasan; Subramanian K. R. S. Sankaranarayanan

APL Mach. Learn. 2, 019901 (2024) https://doi.org/10.1063/5.0198511

Extract

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PERSPECTIVES

In-memory and in-sensor reservoir computing with memristive devices

A unifying perspective on non-stationary kernels for deeper Gaussian processes

ARTICLES

Machine learning based hybrid ensemble models for prediction of organic dyes photophysical properties: Absorption wavelengths, emission wavelengths, and quantum yields

Learning thermodynamically constrained equations of state with uncertainty

Learning the stable and metastable phase diagram to accelerate the discovery of metastable phases of boron

A deep learning approach for gas sensor data regression: Incorporating surface state model and GRU-based model

Prediction and control of spatiotemporal chaos by learning conjugate tubular neighborhoods

M³ICRO: Machine learning-enabled compact photonic tensor core based on programmable multi-operand multimode interference

Noise tailoring, noise annealing, and external perturbation injection strategies in memristive Hopfield neural networks

Machine learning for rapid discovery of laminar flow channel wall modifications that enhance heat transfer

Digitizing images of electrical-circuit schematics

Completeness of atomic structure representations

Predicting wind farm wake losses with deep convolutional hierarchical encoder–decoder neural networks

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

Contextual beamforming: Exploiting location and AI for enhanced wireless telecommunication performance

Training self-learning circuits for power-efficient solutions

Physics-agnostic inverse design using transfer matrices

Measuring thermal profiles in high explosives using neural networks

Deep learning-enabled probing of irradiation-induced defects in time-series micrographs

Random forests for detecting weak signals and extracting physical information: A case study of magnetic navigation

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

VERI-D: A new dataset and method for multi-camera vehicle re-identification of damaged cars under varying lighting conditions

Autonomous convergence of STM control parameters using Bayesian optimization

Accelerating defect predictions in semiconductors using graph neural networks

ERRATA

Erratum: “Learning the stable and metastable phase diagram to accelerate the discovery of metastable phases of boron” [APL Mach. Learn. 2, 016103 (2024)]

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