Implementation of Wang-Landau Algorithm for Probing Thermodynamic Stable Configuration of Multi-Element Materials and Application to Multinary Alloy Nanoparticles

Abstract

With an increase in an element, the configurational entropy effect stabilizes the multi-element materials. However, the expansion of configuration space and heterogeneous surfaces such as nanoparticles preclude the analytical evaluation of configurational entropy. Then, we implemented the Wang-Landau algorithm, which is one of the Monte-Carlo algorithms, for evaluating the configurational entropy and probing the thermodynamic stable configuration of multi-element materials. The regression equation obtained by density functional theory calculation and multiple regression analysis is used in the energy estimation in the sampling. This method was applied to binary alloys in the bulk and ternary alloy nanoparticles and the obtained features of the stable configuration were discussed.

Machine Learning in Catalysis: Analysis and Prediction of CO Adsorption on Multi-elemental Nanoparticle using Metal Coordination-based Regression Model

“…For each concentration, CO adsorption on 3 NP with randomly generated atomic configurations were used as the train and test for the regression model, and CO adsorption on the computationally determined NP with stable atomic configurations were used as an addition test set (Figure S1). This set of NPs was obtained using supervised learning (SL) method via multiple regression analysis in combination with DFT-based first-principles calculation and multicanonical Monte-Carlo (MCMC) simulation with Wang-Landau sampling as described in ref [7,8]. CO adsorption on the NP was scanned through all possible adsorption sites via the ontop, bridge, 3f hollow and 4f hollow sites.…”