Large scale hybrid Monte Carlo simulations for structure and property prediction

S Prokhorenko and K Kalke and Y Nahas and L Bellaiche, NPJ COMPUTATIONAL MATERIALS, 4, 80 (2018).

DOI: 10.1038/s41524-018-0137-0

The Monte Carlo method is one of the first and most widely used algorithms in modern computational physics. In condensed matter physics, the particularly popular flavor of this technique is the Metropolis Monte Carlo scheme. While being incredibly robust and easy to implement, the Metropolis sampling is not well-suited for situations where energy and force evaluations are computationally demanding. In search for a more efficient technique, we here explore the performance of Hybrid Monte Carlo sampling, an algorithm widely used in quantum electrodynamics, as a structure prediction scheme for systems with long- range interactions. Our results show that the Hybrid Monte Carlo algorithm stands out as an excellent computational scheme that can not only significantly outperform the Metropolis sampling but also complement molecular dynamics in materials science applications, while allowing ultra-large-scale simulations of systems containing millions of particles.

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