10.1021/acs.jctc.7b00779
https://zenodo.org/records/1044183
oai:zenodo.org:1044183
Francesco Fracchia
Francesco Fracchia
Scuola Normale Superiore
Gianluca Del Frate
Gianluca Del Frate
Scuola Normale Superiore
Giordano Mancini
Giordano Mancini
Scuola Normale Superiore
Walter Rocchia
Walter Rocchia
Istituto Italiano di Tecnologia
Vincenzo Barone
Vincenzo Barone
Scuola Normale Superiore
Force Field Parametrization of Metal Ions From Statistical Learning Techniques
Zenodo
2017
2017-11-07
https://zenodo.org/communities/e-cam
Creative Commons Attribution 4.0 International
A novel statistical procedure has been developed to optimize the parameters of non-bonded force fields of metal ions in soft matter. The criterion for the optimization is the minimization of the deviations from ab initio forces and energies calculated for model systems. The method exploits the combination of the linear ridge regression and the cross-validation techniques with the di˙erential evolution algorithm. Wide freedom in the choice of the functional form of the force fields is allowed since both linear and non-linear parameters can be optimized. In order to maximize the information content of the data employed in the fitting procedure, the composition of the training set is entrusted to a combinatorial optimization algorithm which maximizes the dissimilarity of the included instances. The methodology has been validated using the force field parametrization of five metal ions (Zn2+, Ni2+, Mg2+, Ca2+, and Na+) in water as test cases.