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JavaGenes: Evolving molecular force field parameters with genetic algorithm. (English) Zbl 1019.92048

Summary: A genetic algorithm procedure has been developed for fitting parameters for many-body interatomic force field functions. Given a physics or chemistry based analytic form for the force field function, parameters are typically chosen to fit a range of structural and physical properties given either by experiments and/or by higher accuracy tight-binding or ab-initio simulations. The method involves using both near equilibrium and far from equilibrium configurations in the fitting procedure, and is unlikely to be trapped in local minima in the complex many-dimensional parameter space. As a proof of the concept, we demonstrate the procedure for the Stillinger-Weber (S-W) potential by (a) reproducing the published parameters for Si by using S-W energetics in the fitness function, and (b) evolving a “new” set of parameters, with a fitness function based on a non-orthogonal tight-binding method, which are better suited for Si cluster energetics as compared to the published S-W potential. Evolution is driven by a fitness function based on the energies and forces calculated for Si\(_n\) clusters \((n< 7)\), and is able to predict accurate energies for minimum energy and deformed configurations of Si\(_n\) \((n= 7, 8, 33)\) clusters, which were not used in the fitness function.

MSC:

92E20 Classical flows, reactions, etc. in chemistry
65C20 Probabilistic models, generic numerical methods in probability and statistics
90C99 Mathematical programming
82D99 Applications of statistical mechanics to specific types of physical systems

Software:

JavaGenes
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