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1 August 2021 Enhanced force-field calibration via machine learning
Aykut Argun, Tobias Thalheim, Stefano Bo, Frank Cichos, Giovanni Volpe
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Proceedings Volume 11798, Optical Trapping and Optical Micromanipulation XVIII; 117981S (2021) https://doi.org/10.1117/12.2593655
Event: SPIE Nanoscience + Engineering, 2021, San Diego, California, United States
Abstract
We introduce a toolbox to calibrate microscopic force fields by analyzing the trajectories of a Brownian particle using machine learning, namely, recurrent neural networks. We demonstrate that this machine-learning approach outperforms standard methods when characterizing the force fields generated by harmonic potentials if the available data are limited. More importantly, it provides a tool to calibrate force fields in situations for which there are no standard methods, such as non-conservative and time-varying force fields. In order to make this method readily available for other users, we provide a Python software package named DeepCalib, which can be easily personalized and optimized for specific force fields and applications.
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Aykut Argun, Tobias Thalheim, Stefano Bo, Frank Cichos, and Giovanni Volpe "Enhanced force-field calibration via machine learning", Proc. SPIE 11798, Optical Trapping and Optical Micromanipulation XVIII, 117981S (1 August 2021); https://doi.org/10.1117/12.2593655
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KEYWORDS
Calibration
Machine learning
Particles
Biophysics
Neural networks
Thermodynamics
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