We present an adaptive algorithm for the optimal phase space sampling in Monte Carlo simulations of 3D Heisenberg spin systems. Based on a golden rule of the Metropolis algorithm which states that an acceptance rate of 50% is ideal to efficiently sample the phase space, the algorithm adaptively modifies a conebased spin update method keeping the acceptance rate close to 50%. We have assessed the efficiency of the adaptive algorithm through four different tests and contrasted its performance with that of other common spin update methods. In systems at low and high temperatures and anisotropies, the adaptive algorithm proved to be the most efficient for magnetization reversal and for the convergence to equilibrium of the thermal averages and the coercivity in hysteresis calculations. Thus, the adaptive algorithm can be used to significantly reduce the computational cost in Monte Carlo simulations of 3D Heisenberg spin systems.

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We present an open-source software package, Vegas, for the atomistic simulation of magnetic materials. Using the classical Heisenberg model and the Monte Carlo Metropolis algorithm, Vegas provides the required tools to simulate and analyze magnetic phenomena of a great variety of systems. Vegas stores the history of the simulation, i.e. the magnetization and energy of the system at every time step, allowing to analyze static and dynamic magnetic phenomena from results obtained in a single simulation. Also, standardized input and output file formats are employed to facilitate the simulation process and the exchange and archiving of data. We include results from simulations performed using Vegas, showing its applicability to study different magnetic phenomena.