Accelerated calculation of configurational free energy using a combination of reverse Monte Carlo and neural network models: Adsorption isotherm for 2D square and triangular lattices

We demonstrate the application of artificial neural network (ANN) models to reverse Monte Carlo based thermodynamic calculations. Adsorption isotherms are generated for 2D square and triangular lattices. These lattices considered because their importance catalytic applications. In general, configurational free energy terms that arise from adsorbate arrangements challenging handle typically evaluated using computationally expensive simulations. show a combination (RMC) ANN model can provide an accurate estimate energy. The is trained/constructed data with help RMC accurately obtained range adsorbate-adsorbate interactions, coverages temperatures within few seconds on desktop computer this method. results validated by comparing MC Additionally, H adsorption Ni(100) surface studied ANN/RMC approach.