Database-driven semigrand canonical Monte Carlo method: Application to segregation isotherm on defects in alloys

RP Campos and S Shinzato and A Ishii and S Nakamura and S Ogata, PHYSICAL REVIEW E, 104, 025310 (2021).

DOI: 10.1103/PhysRevE.104.025310

The application of existing semigrand canonical ensemble Monte Carlo algorithms to alloys requires the chemical potential difference values between pairs of atomic species in the alloys as inputs. However, finding the appropriate values for a target system at a desired temperature and bulk composition is a time-consuming task consisting of multiple test runs to determine the chemical potential differences. This problem becomes more serious when dealing with systems containing three or more atomic species, such as medium- and high-entropy alloys, due to the increase of the number of chemical potential differences that need to be calculated. Here we propose a method for sampling from the semigrand canonical ensemble that relies on energy databases acting as an external atomic reservoir at the desired temperature and composition. Given these energy databases, the desired bulk composition and corresponding chemical potential differences can be satisfied in a "single" Monte Carlo simulation. Moreover, the energy databases shed light on the underlying energetics of alloys, reflecting their local chemical ordering. We demonstrate the validity of this method using analyses of segregation isotherms at grain boundaries and dislocations in two alloy systems: Fe-1-at.-%-Si and NiCoCr medium-entropy alloy. We also discuss the possibly relevant information contained in such energy databases.

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