Molecular simulation and design tools based on neural networks

Research on complex fluids physicochemical properties, based on both micro and mesoscopic computing models. Typical examples are the prediction of competitive adsorption isotherms of two or more types of polymer dispersions in an aqueous medium, using molecular dynamics simulations applying Monte Carlo, in different thermodynamic conditions. We also predict stable complex fluid thermodynamics based on criteria extracted from computer simulations, among other topics.

Application of artificial intelligence models such as artificial neural networks, in order to undertake research process of paint and coatings formulation. Optical-properties and formulation performance prediction based on these models. Utilization of genetic algorithms and neural networks approach for predicting optimal formulations based on required properties.

Research Interests

  • Experimental determination, theoretical and molecular simulation of thermodynamic properties of adsorption and association or interaction of polymers-surfactants, pigment-polymers, among others.

  • Theoretical and molecular simulation of complex fluids (prediction of physicochemical properties, adsorption phenomena and stabilization) and the design of new materials.

  • Rheological properties of paints and dispersions in solid or liquid media (dynamic mechanical analysis and rheometry).

  • Optics and nonlinear optics.

  • Radiative transfer models and also tools for brightness prediction.

  • Thermal and dielectric properties of materials.

  • Online monitoring of processes.