Building an affordable self-driving lab: Practical machine learning experiments for physics education using Internet-of-Things
Date
2025-10-29
Journal Title
Journal ISSN
Volume Title
Publisher
APL Machine Learning
Abstract
Machine learning (ML) is transforming modern physics research, but practical, hands-on experience with ML techniques remains limited
due to cost and complexity barriers. To address this gap, we introduce an affordable, autonomous, Internet-of-Things (IoT)-enabled experimental
platform designed specifically for applied physics education. Utilizing an Arduino microcontroller, a customizable multi-wavelength
light emitting diode array, and photosensors, our setup generates diverse, real-time optical datasets ideal for training and evaluating foundational
ML algorithms, including traversal methods, Bayesian inference, and deep learning. The platform facilitates a closed-loop, self-driving
experimental workflow, encompassing automated data collection, preprocessing, model training, and validation. Through systematic performance
comparisons, we demonstrate the superior ability of deep learning to capture complex nonlinear relationships compared to traversal
and Bayesian methods. At ∼$60, this open-source IoT platform provides an accessible, practical pathway for students to master advanced
ML concepts, promoting deeper conceptual insights and essential technical skills required for the next generation of physicists and engineers.
Description
This article was originally published in APL Machine Learning . The version of record is available at: https://doi.org/10.1063/5.0283529
© 2025 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Keywords
Light emitting diodes, Photodetectors, Microcontroller, Internet of things, Data acquisition, Convolutional neural network, Deep learning, Machine learning, Bayesian inference, Statistical analysis
Citation
Yang Liu, Qianjie Lei, Xiaolong He, Yizhe Xue, Kexin He, Haitao Yang, Yong Wang, Xian Zhang, Li Yang, Yichun Zhou, Ruiqi Hu, Yong Xie; Building an affordable self-driving lab: Practical machine learning experiments for physics education using Internet-of-Things. APL Mach. Learn. 1 December 2025; 3 (4): 046105. https://doi.org/10.1063/5.0283529