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 sc...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.展开更多
基金S.P and L.B.thank the DARPA Grant HR0011-15-2-0038(MATRIX program)K.K.acknowledges a SURF grant from the state of Arkansas,Y.N.and L.B.thank the DARPA Grant No.HR0011727183-D18AP00010(TEE Program)+1 种基金All authors are grateful for support provided by NVIDIA via the NVIDIA GPU Grant.Computations were made possible thanks to the use of the Arkansas High Performance Computing Center and the Arkansas Economic Development Commission.DARPA Grant HR0011-15-2-0038(MATRIX program)DARPA Grant No.HR0011727183-D18AP00010(TEE Program),SURF grant from the state of Arkansas,NVIDIA GPU Grant.
文摘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.
