Enhanced force-field calibration via machine learning

Aykut Argun; Tobias Thalheim; Stefano Bo; Frank Cichos; Giovanni Volpe

doi:10.1117/12.2593655

1 August 2021 Enhanced force-field calibration via machine learning

Aykut Argun, Tobias Thalheim, Stefano Bo, Frank Cichos, Giovanni Volpe

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.

Conference Presentation

Citation Download Citation

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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