GAFit: A general-purpose, user-friendly program for fitting potential energy surfaces

Abstract

We have developed a software package based on a genetic algorithm that fits an analytic function to a given set of data points. The code, called GAFit, was also interfaced with the CHARMM and MOPAC programs in order to facilitate force field parameterizations and fittings of specific reaction parameters (SRP) for semiempirical Hamiltonians. The present tool may be applied to a wide range of fitting problems, though it has been especially designed to significantly reduce the hard work involved in the development of potential energy surfaces for complex systems. For this purpose, it has been equipped with several programs to help the user in the preparation of the input files. We showcase the application of the computational tool to several chemical-relevant problems: force-field parameterization, with emphasis on nonbonded energy terms or intermolecular potentials, derivation of SRP for semiempirical Hamiltonians, and fittings of generic analytical functions. Program summaryProgram title : GAFitProgram Files doi : http://dx.doi.org/10.17632/9gy6bjcwk5.1Licensing provisions : GNU General Public License 3 (GPL)Programming language : Fortran 90, C, Perl and JavaNature of problem : Potential energy surfaces (PESs) have a key role in reaction dynamics and molecular dynamics. Chemical dynamics simulations can be performed by using analytical PESs or by direct dynamics, that is, by computing energies and forces “on-the-fly” by molecular structure calculations. The development of analytical PESs is, quite often, a very complex and tedious task. There are several programs in the literature that may help to develop PESs but, as far as we know, none of them show, at the same time, great generality and flexibility.Solution method : GAFit was designed to help users develop analytical potential energy functions. We made special efforts to write a code that exhibits the three features mentioned above. For this reason, we have also interfaced GAFit with the CHARMM and MOPAC programs. The former is one of the popular packages for molecular dynamics and the latter is a semiempirical quantum chemistry program that may be conveniently used for direct dynamics simulations. Because, in general, the fitting of PESs involves a simultaneous optimization of many nonlinear parameters, we have selected a genetic algorithm as the driver for the parameterizations. As shown in the paper, GAFit may be applied to other fitting problems.