CONVERGENT TECHNOLOGIES FOR CLUSTERS' EQUILIBRIUM PROPERTIES COGNITION
The paper reflects the real gases properties convergent analysis utilization for clusters' properties and structure cognition. Experimental data are from the NIST, USA, Webbook on Thermophysical Properties of Fluid Systems. An explosion of interest to clusters now is stimulated by their utilization as seeds for the nanoparticles nucleation. Some scientists even name clusters as a new state of matter. But a small interparticle bond energy as compared to the thermal agitation energy makes the equilibrium clusters' nature cognition difficult. The author has developed the interactive computerized method for thermophysical data analysis. A high precision of the NIST data, up to 12 digits, provides the inverse problem solution for hidden clusters' properties cognition. The convergent researcher-computer interaction permits hypotheses generation about the equilibrium cluster structure and to receive a quick response from the information system about their correctness. The multi-window mode for intermediate results analysis permits changing easily the algorithm and program providing the investigation convergence to the final theoretical conclusion about the clusters' nature. Unlike the Artificial Intellect technology, which relies mostly on computers' power, in our case we consider the Convergent Intellect, which requires the researcher's active work and an effective support from the information system for his/her creative and cognitive activity. The breakthrough in the clusters' properties cognition relies on new, more informative, variables: the monomer fraction density and the gas potential energy density. It has opened the way to a number of fundamental discoveries, such as: the clusters in gases bond energy growth on approaching the bulk substance melting point; soft structural transitions in cluster fractions; the chain clusters existence at moderate densities and magic particles numbers in large clusters at densities approaching the critical one.