Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Fast parallel algorithms for short-range molecular dynamics

CP2K is an open source electronic structure and molecular dynamics software package to perform atomistic simulations of solid-state, liquid, molecular, and biological systems. It is especially aimed at massively parallel and linear-scaling electronic structure methods and state-of-the-art ab initio molecular dynamics simulations. Excellent performance for electronic structure calculations is achieved using novel algorithms implemented for modern high-performance computing systems. This review revisits the main capabilities of CP2K to perform efficient and accurate electronic structure simulations. The emphasis is put on density functional theory and multiple post–Hartree–Fock methods using the Gaussian and plane wave approach and its augmented all-electron extension.

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CP2K: An electronic structure and molecular dynamics software package - Quickstep: Efficient and accurate electronic structure calculations

https://onlinelibrary.wiley.com/doi/pdf/10.1002/adts.202000237

Transition Path Sampling as Markov Chain Monte Carlo of Trajectories: Recent Algorithms, Software, Applications, and Future Outlook

An emulsion is a thermodynamically unstable system consisting of at least two immiscible liquid phases, one of which is dispersed in the other in the form of droplets of varying size. Most studies on emulsions have focused on the behaviour of emulsion droplets with diameter from ∼50 μm and upwards. However, the properties of smaller droplets may be highly relevant in order to understand the behaviour of emulsions, including their performance in numerous applications within the fields of food, industry, and medical science. The relatively long life-time and small size of these droplets compared to other emulsion droplets, make them suited for optical trapping and micromanipulation technologies. Optical tweezers have previously shown potential in the study of stabilized emulsions. Here we employ optical tweezers to examine unstable oil-in-water emulsions to determine the effects of system parameters on depletion force and coalescence times.

/pdf/exploring-the-effects-of-approach-velocity-on-depletion-53x8g101gq.pdf

Exploring the effects of approach velocity on depletion force and coalescence in oil-in-water emulsions

Several simulations strategies have emerged to predict the permeability of solutes across membranes, which is important for many biological or industrial processes such as drug design. The widespread inhomogeneous solubility-diffusion (ISD) model is based on the Smoluchowski equation and describes permeation as purely diffusive. The counting method, which counts membrane transitions in a long molecular dynamics (MD) trajectory, is free of this diffusive assumption, but it lacks sufficient statistics when the permeation involves high free energy barriers. Metadynamics and variations thereof can overcome such barriers, but they generally lack the kinetics information. The milestoning framework has been used to describe permeation as a rare event, but it still relies on the Markovian assumption between the milestones. Replica Exchange Transition Interface Sampling (RETIS) has been shown to be an effective method for sampling rare events while simultaneously describing the kinetics without assumptions. This paper is the first permeation application of RETIS on an all-atom lipid bilayer consisting of 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) to compute the entrance, escape and complete transition of molecular oxygen. Conventional MD was performed as a benchmark, and the MD rates from counting were converted to rate constants, giving good agreement with the RETIS values. Moreover, a correction factor was derived to convert the collective order parameter in RETIS, which was aimed to improve efficiency, to a single-particle order parameter. With this work, we showed how the exact kinetics of drug molecules permeation can be assessed with RETIS even if the permeation is truly a rare event or if the permeation is non-Markovian. RETIS will therefore be a valuable tool for future permeation studies.

Permeation Rates of Oxygen through a Lipid Bilayer Using Replica Exchange Transition Interface Sampling.

The algorithmic development in the field of path sampling has made tremendous progress in recent years. Although the original transition path sampling method was mostly used as a qualitative tool to sample reaction paths, the more recent family of interface-based path sampling methods has paved the way for more quantitative rate calculation studies. Of the exact methods, the replica exchange transition interface sampling (RETIS) method is the most efficient, but rather difficult to implement. This has been the main motivation to develop the open-source Python-based computer library PyRETIS that was released in 2017. PyRETIS is designed to be easily interfaced with any molecular dynamics (MD) package using either classical or ab initio MD. In this study, we report on the principles and the software enhancements that are now included in PyRETIS 2, as well as the recent developments on the user interface, improvements of the efficiency via the implementation of new shooting moves, easier initialization procedures, analysis methods, and supported interfaced software. © 2019 The Authors. Journal of Computational Chemistry published by Wiley Periodicals, Inc.

PyRETIS 2: An improbability drive for rare events.

Covalent inhibition has become more accepted in the past two decades, as illustrated by the clinical approval of several irreversible inhibitors designed to covalently modify their target. Elucidation of the structure‐activity relationship and potency of such inhibitors requires a detailed kinetic evaluation. Here, we elucidate the relationship between the experimental read‐out and the underlying inhibitor binding kinetics. Interactive kinetic simulation scripts are employed to highlight the effects of in vitro enzyme activity assay conditions and inhibitor binding mode, thereby showcasing which assumptions and corrections are crucial. Four stepwise protocols to assess the biochemical potency of (ir)reversible covalent enzyme inhibitors targeting a nucleophilic active site residue are included, with accompanying data analysis tailored to the covalent binding mode. Together, this will serve as a guide to make an educated decision regarding the most suitable method to assess covalent inhibition potency. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC.

A Comprehensive Guide for Assessing Covalent Inhibition in Enzymatic Assays Illustrated with Kinetic Simulations

Permeation of compounds through membranes is important in biological and engineering processes, e.g., drug delivery through lipid bilayers, anesthetics, or chemical reactor design. Simulations at the atomic scale can provide insight in the diffusive pathways and they give estimates of the membrane permeability based on counting membrane transitions or on the inhomogeneous solubility-diffusivity model described by the Smoluchowski equation. For many permeants, permeation through a membrane is too slow to gather sufficient statistics with conventional molecular dynamics simulations, i.e., permeation is a rare event. Recent attempts to improve the description of the dynamics of such rare permeation events have been based on milestoning, which allows the study of processes at timescales beyond those achievable by straightforward molecular dynamics. The approach is not relying on an overdamped description, but, still, it uses a Markovian approximation which is only valid for small permeants that are not disruptive to the membrane structure. To overcome this fundamental limitation, we show here how replica exchange transition interface sampling (RETIS) can effectively be used on this problem by deriving an effective set of equations that relate the outcome of RETIS simulations and the permeability coefficient. In addition, we introduce two new path Monte Carlo (MC) moves specifically for permeation dynamics, that are used in combination with the ordinary path generating moves, which considerably increase the efficiency. The advantage of our method is that it gives exact results, identical to brute force molecular dynamics, but orders of magnitude faster.

https://biblio.ugent.be/publication/8716551/file/8716553.pdf

Exact non-Markovian permeability from rare event simulations

We propose to analyze molecular dynamics (MD) output via a supervised machine learning (ML) algorithm, the decision tree. The approach aims to identify the predominant geometric features which correlate with trajectories that transition between two arbitrarily defined states. The data-driven algorithm aims to identify these features without the bias of human "chemical intuition". We demonstrate the method by analyzing the proton exchange reactions in formic acid solvated in small water clusters. The simulations were performed with ab initio MD combined with a method to efficiently sample the rare event, path sampling. Our ML analysis identified relevant geometric variables involved in the proton transfer reaction and how they may change as the number of solvating water molecules changes.

Chemistrees: Data-Driven Identification of Reaction Pathways via Machine Learning

We developed a replica exchange method that is effectively parallelizable even if the computational cost of the Monte Carlo moves in the parallel replicas are considerably different, for instance, because the replicas run on different types of processor units or because of the algorithmic complexity. To prove detailed-balance, we make a paradigm shift from the common conceptual viewpoint in which the set of parallel replicas represents a high-dimensional superstate, to an ensemble-based criterion in which the other ensembles represent an environment that might or might not participate in the Monte Carlo move. In addition, based on a recent algorithm for computing permanents, we effectively increase the exchange rate to infinite without the steep factorial scaling as a function of the number of replicas. We illustrate the effectiveness of this replica exchange methodology by combining it with a quantitative path sampling method, replica exchange transition interface sampling (RETIS), in which the costs for a Monte Carlo move can vary enormously as paths in a RETIS algorithm do not have the same length and the average path lengths tend to vary considerably for the different path ensembles that run in parallel. This combination, coined ∞RETIS, was tested on three model systems.

Sander Roet

Papers

PyRETIS 2: An improbability drive for rare events.

A Comprehensive Guide for Assessing Covalent Inhibition in Enzymatic Assays Illustrated with Kinetic Simulations

Exact non-Markovian permeability from rare event simulations

Chemistrees: Data-Driven Identification of Reaction Pathways via Machine Learning

Exchanging Replicas with Unequal Cost, Infinitely and Permanently