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Identifying Physico-Chemical Laws from the Robotically Collected Data

preprint
submitted on 03.07.2019 and posted on 03.07.2019 by Liwei Cao, Danilo Russo, Vassilios S. Vassiliadis, Alexei Lapkin

A mixed-integer nonlinear programming (MINLP) formulation for symbolic regression was proposed to identify physical models from noisy experimental data. The formulation was tested using numerical models and was found to be more efficient than the previous literature example with respect to the number of predictor variables and training data points. The globally optimal search was extended to identify physical models and to cope with noise in the experimental data predictor variable. The methodology was coupled with the collection of experimental data in an automated fashion, and was proven to be successful in identifying the correct physical models describing the relationship between the shear stress and shear rate for both Newtonian and non-Newtonian fluids, and simple kinetic laws of reactions. Future work will focus on addressing the limitations of the formulation presented in this work, by extending it to be able to address larger complex physical models.


Funding

This project is also co-funded by the UKRI project “Combining Chemical Robotics and Statistical Methods to Discover Complex Functional Products” (EP/R009902/1), and co-funded by the National Research Foundation (NRF), Prime Minister’s Office, Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) program as a part of the Cambridge Centre for Advanced Research and Education in Singapore Ltd (CARES).

History

Email Address of Submitting Author

aal35@cam.ac.uk

Institution

University of Cambridge

Country

United Kingdom

ORCID For Submitting Author

0000-0001-7621-0889

Declaration of Conflict of Interest

The authors declare no conflict of interests

Version Notes

Submitted for peer review

Licence

Exports