All Outputs (2)

Optimal construction of a fast and accurate polarisable water potential based on multipole moments trained by machine learning (2009)
Journal Article
Handley, C. M., Hawe, G. I., Kell, D. B., & Popelier, P. L. A. (2009). Optimal construction of a fast and accurate polarisable water potential based on multipole moments trained by machine learning. Physical Chemistry Chemical Physics, 11(30), 6365-6376. https://doi.org/10.1039/b905748j

To model liquid water correctly and to reproduce its structural, dynamic and thermodynamic properties warrants models that account accurately for electronic polarisation. We have previously demonstrated that polarisation can be represented by fluctua... Read More about Optimal construction of a fast and accurate polarisable water potential based on multipole moments trained by machine learning.

Dynamically Polarizable Water Potential Based on Multipole Moments Trained by Machine Learning (2009)
Journal Article
Handley, C. M., & Popelier, P. L. A. (2009). Dynamically Polarizable Water Potential Based on Multipole Moments Trained by Machine Learning. Journal of Chemical Theory and Computation, 5(6), 1474-1489. https://doi.org/10.1021/ct800468h

It is widely accepted that correctly accounting for polarization within simulations involving water is critical if the structural, dynamic, and thermodynamic properties of such systems are to be accurately reproduced. We propose a novel potential for... Read More about Dynamically Polarizable Water Potential Based on Multipole Moments Trained by Machine Learning.