Publication: Machine Learning-Assisted Discovery of Hidden States in Expanded Free Energy Space
Machine Learning-Assisted Discovery of Hidden States in Expanded Free Energy Space
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Ketkaew, R., Creazzo, F., & Luber, S. (2022). Machine Learning-Assisted Discovery of Hidden States in Expanded Free Energy Space. Journal of Physical Chemistry Letters, 13(7), 1797–1805. https://doi.org/10.1021/acs.jpclett.1c04004
Abstract
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Collective variables (CVs) are crucial parameters in enhanced sampling calculations and strongly impact the quality of the obtained free energy surface. However, many existing CVs are unique to and dependent on the system they are constructed with, making the developed CV non-transferable to other systems. Herein, we develop a non-instructor-led deep autoencoder neural network (DAENN) for discovering general-purpose CVs. The DAENN is used to train a model by learning molecular representations upon unbiased trajectories that contain on
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- Creazzo, Fabrizio
- Luber, Sandra
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Citations
Ketkaew, R., Creazzo, F., & Luber, S. (2022). Machine Learning-Assisted Discovery of Hidden States in Expanded Free Energy Space. Journal of Physical Chemistry Letters, 13(7), 1797–1805. https://doi.org/10.1021/acs.jpclett.1c04004
