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

We present calculations of nucleon-deuteron scattering as well as ground and low-lying excited states of light nuclei in the mass range $A=3--16$ up through next-to-next-to-leading order in chiral effective field theory using semilocal coordinate-space regularized two- and three-nucleon forces. It is shown that both of the low-energy constants entering the three-nucleon force at this order can be determined from the triton binding energy and the differential cross section minimum in elastic nucleon-deuteron scattering. From all considered nucleon-deuteron scattering observables, the strongest constraint on these low-energy constants emerges from the precisely measured cross section minimum at ${E}_{N}=70$ MeV. The inclusion of the three-nucleon force is found to improve the agreement with the data for most of the considered observables.

Few- and many-nucleon systems with semilocal coordinate-space regularized chiral two- and three-body forces

A simultaneously accurate and computationally efficient parametrization of the potential energy surface of molecules and materials is a long-standing goal in the natural sciences. While atom-centered message passing neural networks (MPNNs) have shown remarkable accuracy, their information propagation has limited the accessible length-scales. Local methods, conversely, scale to large simulations but have suffered from inferior accuracy. This work introduces Allegro, a strictly local equivariant deep neural network interatomic potential architecture that simultaneously exhibits excellent accuracy and scalability. Allegro represents a many-body potential using iterated tensor products of learned equivariant representations without atom-centered message passing. Allegro obtains improvements over state-of-the-art methods on QM9 and revMD17. A single tensor product layer outperforms existing deep MPNNs and transformers on QM9. Furthermore, Allegro displays remarkable generalization to out-of-distribution data. Molecular simulations using Allegro recover structural and kinetic properties of an amorphous electrolyte in excellent agreement with ab-initio simulations. Finally, we demonstrate parallelization with a simulation of 100 million atoms. 

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Learning local equivariant representations for large-scale atomistic dynamics

Enhanced sampling methods for molecular dynamics simulations [Article v1.0] Jérôme Hénin1,2*, Tony Lelièvre3*, Michael R. Shirts4*, Omar Valsson5,6*, Lucie Delemotte7* 1Laboratoire de Biochimie Théorique UPR 9080, CNRS, Paris, France; 2Institut de Biologie Physico-Chimique–Fondation Edmond de Rothschild, Paris, France; 3CERMICS, Ecole des Ponts, INRIA, Marne-la-Vallée, France; 4Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA, 80309; 5University of North Texas, Department of Chemistry, Denton, TX, USA; 6Max Planck Institute for Polymer Research, Mainz, Germany; 7KTH Royal Institute of Technology, Science for Life Laboratory, Stockholm, Sweden

Enhanced Sampling Methods for Molecular Dynamics Simulations [Article v1.0]

Electrocatalytic reactions occur in the nanoscale space at the electrified electrode-electrolyte interface. It is well known that the electrode-electrolyte interface, also called as interfacial microenvironment, is difficult to investigate due to the interference of bulk electrolytes and its dynamic evolution in response to applied bias potential. Here, we employ electrochemical co-reduction of CO2 and H2O on commercial Ag electrodes as a model system, in conjunction with quaternary ammonium cationic surfactants as electrolyte additives. We probe bias-potential-driven dynamic response of the interfacial microenvironment as well as the mechanistic origin of catalytic selectivity. By virtue of comprehensive in situ vibrational spectroscopy, electrochemical impedance spectroscopy, and molecular dynamics simulations, it is revealed that the structure of surfactants is dynamically changed from a random distribution to a nearly ordered assembly with increasing bias potential. The nearly ordered surfactant assembly regulates the interfacial water environment by repelling isolated water and suppressing water orientation into an ordered structure as well as promotes CO2 enrichment at the electrified interface. Eventually, the formed hydrophobic-aerophilic interface microenvironment reduces the activity of water dissociation and increases the selectivity of CO2 electroreduction to CO. These results highlight the importance of regulating the interfacial microenvironment by organic additives as a means of boosting the electrochemical performance in electrosynthesis and beyond.

Dynamically Formed Surfactant Assembly at the Electrified Electrode-Electrolyte Interface Boosting CO2 Electroreduction.

LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales

Accelerating nuclear configuration interaction calculations through a preconditioned block iterative eigensolver

Studies using molecular dynamics (MD) have long struggled to simulate the failure modes of materials, predicting unrealistically high ductility and failing to capture brittle fracture. The primary cause of this shortcoming is an inadequate description of bond breaking. While reactive force fields such as ReaxFF show improvements compared to traditional force fields, the charge models used yield unphysical partial charges, especially during dissociation of ionic bonds. This flaw may be remedied by using the atom-condensed Kohn-Sham density functional theory (DFT) approximated to a second order (ACKS2) charge model for determining partial charges. In this work, we present a new ACKS2-enabled Reax force field for fracture simulations of lithium oxide systems, which was obtained by training against an extensive set of DFT, multireference configuration interaction (MRCI), and MRCI+Q reference data using genetic optimization techniques. This new force field significantly improves the bond breaking behavior, but still cannot fully capture the brittle fracture in MD simulations, suggesting more research is needed to improve simulation of brittle fracture.

H. Metin Aktulga

Papers

LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales

Accelerating nuclear configuration interaction calculations through a preconditioned block iterative eigensolver

Optimization of the Reax force field for the lithium–oxygen system using a high fidelity charge model