All-atom computations with irreversible Markov chains

Michael Faulkner (1) , Liang Qin , A C Maggs , Werner Krauth (2)

1 Phys-ENS - Laboratoire de Physique de l'ENS Lyon
2 LPS - Laboratoire de Physique Statistique de l'ENS

We apply the irreversible event-chain Monte Carlo (ECMC) algorithm to the simulation of dense all-atom systems with long-range Coulomb interactions. ECMC is event-driven and exactly samples the Boltzmann distribution. It neither uses time-step approximations nor spatial cutoffs on the range of the interaction potentials. Most importantly, it need not evaluate the total Coulomb potential and thus circumvents the major computational bottleneck of traditional approaches. It only requires the derivatives of the two-particle Coulomb potential, for which we discuss mutually consistent choices. ECMC breaks up the total interaction potential into factors. For particle systems made up of neutral dipolar molecules, we demonstrate the superior performance of dipole-dipole factors that do not decompose the Coulomb potential beyond the two-molecule level. We demonstrate that these long-range factors can nevertheless lead to local lifting schemes, where subsequently moved particles are mostly close to each other. For the simple point-charge water model with flexible molecules (SPC/Fw), which combines the long-ranged intermolecular Coulomb potential with hydrogen-oxygen bond-length vibrations, a flexible hydrogen-oxygen-hydrogen bond angle, and Lennard-Jones oxygen-oxygen potentials, we break up the potential into factors containing between two and six particles. For this all-atom liquid-water model, we demonstrate that the computational complexity of ECMC scales very well with the system size. This is achieved in a pure particle-particle framework, without the interpolating mesh required for the efficient implementation of other modern Coulomb algorithms. Finally, we discuss prospects and challenges for ECMC and outline several future applications. Published by AIP Publishing. https://doi.

Domaines: Physique [physics]; Matière Condensée [cond-mat]; Mécanique statistique [cond-mat.stat-mech]

Michael Faulkner, Liang Qin, A C Maggs, Werner Krauth. All-atom computations with irreversible Markov chains. Journal of Chemical Physics, 2018, 149, ⟨10.1063/1.5036638⟩. ⟨hal-02324312⟩