Neural networks to fit potential energy curves from asphaltene-asphaltene interaction data

Abstract

Neural networks methodology is a tool that allows to get the potential energy curve in cases where the data dispersion does not fit a discrete distribution; hence, a binding energy fitting can be found with this methodology. A data distribution of the intermolecular pair interaction potential in vacuum has been previously accomplished between asphaltene-asphaltene (UAA) systems by using compass classical force field. In the latter, all possible interaction geometries are taken into account between the species: random, face-to-face, t-shape and edge to edge. In one of these cases, a potential energy curve is gotten when the geometry of interaction is face-to-face using a statistical fit. Focusing in these data distribution, neural networks have been applied on the following cases: i) face-to-face distribution of asphaltene-asphaltene interactions; ii) the complete asphaltene-asphaltene discrete distribution of energy vs contact distance (the minimum distance at which the interacting species is not equal to zero) where all-geometries were used, and iii) the random distribution of geometries of asphaltene-asphaltene interactions. In addition, using an asphaltene model molecule reported by Speight and taking into account two possible asphaltene interactions (face-to-face and random), firstly the data distribution of energy as a function of distance is obtained, and secondly neural networks are applied to fit the corresponding potential energy curve.