Parallel Molecular Dynamics Simulations Research Articles

Enhancing surface strength of tungsten by gradient nano-grained structure

A gradient nano-grained (GNG) structure demonstrates satisfactory surface strength. However, the underlying mechanism responsible for its strengthening lacks sufficient research. To explain how gradient nano-grained structures improve surface strength in detail, large-scale parallel molecular dynamics simulations are utilized in this study to investigate the mechanical deformation behavior of BCC tungsten with varying grain sizes during spherical nanoindentation. The findings suggest that a well-designed gradient structure can promote rational plasticity and an appropriate distribution of internal atomic stress. The critical point of maximum stress and hardness is observed when the initial grain size is 4.5 nm, with an average grain size of 7.1 nm. The interaction between grain boundary slip and migration in small grains, along with the enhanced activity of grain boundary dislocations in large grains, collectively contributes to the enhancement of the strength and hardness of the GNG structure. Compared with a homogeneous nano-grained structure, the gradient nano-grained structure exhibits a more rational distribution of dislocations and stress relaxation effects to enhance strength. The present work utilizes the molecular dynamics nanoindentation method to study GNG materials, providing a methodology for investigating the surface strengthening effects of GNG structures at the atomic scale and effectively revealing potential mechanisms for resisting surface deformation in GNG structures.

High energy radiation tolerance of iron phosphate glasses: Molecular dynamics study

We report the results of massive parallel molecular dynamics simulations of high-energy radiation damage in phosphate glasses. This damage is created by overlapping multiple 70 keV collision cascades. We quantify different aspects of radiation-induced structural changes including at different stages of damage development, including coordination numbers, cluster sizes and density. The overall trend is that radiation damage causes polymerisation of the glass network and the loss of small and isolated clusters, however the details of this response vary with different glass compositions.

More with less: Harvesting transition mechanism, free energy, and rates from short parallel molecular dynamics simulations

More with less: Harvesting transition mechanism, free energy, and rates from short parallel molecular dynamics simulations

Top-Down Machine Learning of Coarse-Grained Protein Force Fields.

Developing accurate and efficient coarse-grained representations of proteins is crucial for understanding their folding, function, and interactions over extended time scales. Our methodology involves simulating proteins with molecular dynamics and utilizing the resulting trajectories to train a neural network potential through differentiable trajectory reweighting. Remarkably, this method requires only the native conformation of proteins, eliminating the need for labeled data derived from extensive simulations or memory-intensive end-to-end differentiable simulations. Once trained, the model can be employed to run parallel molecular dynamics simulations and sample folding events for proteins both within and beyond the training distribution, showcasing its extrapolation capabilities. By applying Markov state models, native-like conformations of the simulated proteins can be predicted from the coarse-grained simulations. Owing to its theoretical transferability and ability to use solely experimental static structures as training data, we anticipate that this approach will prove advantageous for developing new protein force fields and further advancing the study of protein dynamics, folding, and interactions.

Charting Nanocluster Structures via Convolutional Neural Networks.

A general method to obtain a representation of the structural landscape of nanoparticles in terms of a limited number of variables is proposed. The method is applied to a large data set of parallel tempering molecular dynamics simulations of gold clusters of 90 and 147 atoms, silver clusters of 147 atoms, and copper clusters of 147 atoms, covering a plethora of structures and temperatures. The method leverages convolutional neural networks to learn the radial distribution functions of the nanoclusters and distills a low-dimensional chart of the structural landscape. This strategy is found to give rise to a physically meaningful and differentiable mapping of the atom positions to a low-dimensional manifold in which the main structural motifs are clearly discriminated and meaningfully ordered. Furthermore, unsupervised clustering on the low-dimensional data proved effective at further splitting the motifs into structural subfamilies characterized by very fine and physically relevant differences such as the presence of specific punctual or planar defects or of atoms with particular coordination features. Owing to these peculiarities, the chart also enabled tracking of the complex structural evolution in a reactive trajectory. In addition to visualization and analysis of complex structural landscapes, the presented approach offers a general, low-dimensional set of differentiable variables that has the potential to be used for exploration and enhanced sampling purposes.

Molecular Insights into Antioxidant Efficiency of Melanin: A Sustainable Antioxidant for Natural Rubber Formulations.

N-(1,3-Dimethyl butyl)-N'-phenyl-p-phenylenediamine (6-PPD) is a worldwide antioxidant commonly added to delay the thermo-oxidative degradation of tire rubbers. Unfortunately, 6PPD and its transformation product 6PPD-quinone are toxic to aquatic organisms (e.g., coho salmon). Herein, we explore the free radical scavenging activity and protective mechanism of melanin (MLN) on natural rubber's (NR's) oxidative resistance using molecular dynamics (MD) and quantum mechanical (QM) calculations. The relationship between the molecular structure and the chemical nature of the antioxidant molecules via transition state calculations is explored to unravel the reaction mechanisms of antioxidants interacting with peroxy radicals (ROO·) of NR with the estimation of reaction barriers. Following this, the radical scavenging activity of antioxidants was quantified via a hydrogen atom transfer mechanism and bond dissociation energy calculations. Parallel MD simulations were considered to study the interfacial interactions of antioxidant molecules with polymer chains and fillers with a quantifiable structure-property correlation. Given these results, the nanocomposite (NR-MLN-SiO2) with natural antioxidant melanin manifested outstanding antioxidant properties by preferentially bagging the ROO· radicals, thus improving NR's thermal-oxidative aging relative to 6-PPD. The MD results revealed that the intermolecular interactions at the NR/antioxidant interface benefited the antioxidant MLN to bind tightly to the NR in NR-MLN-SiO2 composite, thus exhibiting improved dispersion, O2 barrier properties, and thermo-oxidative stability, which could extend the service life of NR products (e.g., tires). In addition, as a sustainable antioxidant, MLN could replace toxic antioxidants like 6-PPD. More importantly, the QM/MD simulations provided a fundamental understanding of the mechanistic pathways of antioxidant molecules in NR composites, which are conducive to designing high-performance and sustainable green elastomers.

Molecular Simulation to Investigate Open-Close Motion of a Flagellar Export Apparatus Protein FlhAC.

Molecular dynamics (MD) simulation and parallel cascade selection molecular dynamics (PaCS-MD) are widely used to investigate large-amplitude motions of proteins. PaCS-MD is an enhanced conformational sampling method consisting of cycles of parallel unbiased MD simulations combined with a selection of MD snapshots as the initial structures for the next cycle. In addition, free energy calculation can be achieved by the combination of PaCS-MD and the Markov state model (MSM). In this chapter, the protocols to investigate the open-close motion of a flagellar export apparatus protein, FlhAC, by MD and the combination of PaCS-MD and MSM are described.

Optimization of Thermal Conductance at Interfaces Using Machine Learning Algorithms.

Optimization of thermal transport across the interface of two different materials is critical to micro-/nanoscale electronic, photonic, and phononic devices. Although several examples of compositional intermixing at the interfaces having a positive effect on interfacial thermal conductance (ITC) have been reported, an optimum arrangement has not yet been determined because of the large number of potential atomic configurations and the significant computational cost of evaluation. On the other hand, computation-driven materials design efforts are rising in popularity and importance. Yet, the scalability and transferability of machine learning models remain as challenges in creating a complete pipeline for the simulation and analysis of large molecular systems. In this work we present a scalable Bayesian optimization framework, which leverages dynamic spawning of jobs through the Message Passing Interface (MPI) to run multiple parallel molecular dynamics simulations within a parent MPI job to optimize heat transfer at the silicon and aluminum (Si/Al) interface. We found a maximum of 50% increase in the ITC when introducing a two-layer intermixed region that consists of a higher percentage of Si. Because of the random nature of the intermixing, the magnitude of increase in the ITC varies. We observed that both homogeneity/heterogeneity of the intermixing and the intrinsic stochastic nature of molecular dynamics simulations account for the variance in ITC.

Efficient d-dimensional molecular dynamics simulations for studies of the glass-jamming transition.

We develop an algorithm suitable for parallel molecular dynamics simulations in d spatial dimensions and describe its implementation in C++. All routines work in arbitrary d; the maximum simulated d is limited only by available computing resources. These routines include several that are particularly useful for studies of the glass-jamming transition, such as SWAP Monte Carlo and FIRE energy minimization. The scalings of simulation runtimes with the number of particles N and number of simulation threads n_{threads} are comparable to popular molecular dynamics codes such as LAMMPS. The efficient parallel implementation allows simulation of systems that are much larger than those employed in previous high-dimensional glass-transition studies. As a demonstration of the code's capabilities, we show that supercooled d=6 liquids can possess dynamics that are substantially more heterogeneous and experience a breakdown of the Stokes-Einstein relation that is substantially stronger than previously reported, owing at least in part to the much smaller system sizes employed in earlier simulations.

Multiple-Allele MHC Class II Epitope Engineering by a Molecular Dynamics-Based Evolution Protocol.

Epitopes that bind simultaneously to all human alleles of Major Histocompatibility Complex class II (MHC II) are considered one of the key factors for the development of improved vaccines and cancer immunotherapies. To engineer MHC II multiple-allele binders, we developed a protocol called PanMHC-PARCE, based on the unsupervised optimization of the epitope sequence by single-point mutations, parallel explicit-solvent molecular dynamics simulations and scoring of the MHC II-epitope complexes. The key idea is accepting mutations that not only improve the affinity but also reduce the affinity gap between the alleles. We applied this methodology to enhance a Plasmodium vivax epitope for multiple-allele binding. In vitro rate-binding assays showed that four engineered peptides were able to bind with improved affinity toward multiple human MHC II alleles. Moreover, we demonstrated that mice immunized with the peptides exhibited interferon-gamma cellular immune response. Overall, the method enables the engineering of peptides with improved binding properties that can be used for the generation of new immunotherapies.

Analysis of hypervelocity impacts: the tungsten case

The atomistic mechanisms of damage initiation during high velocity (v up to 9 km s−1, kinetic energies up to 200 keV) impacts of W projectiles on a W surface have been investigated using parallel molecular-dynamics simulations involving large samples (up to 40 million atoms). Various aspects of the high velocity impacts, where the projectile and part of the target material undergo massive plastic deformation, breakup, melting, and vaporization, are analyzed. Different stages of the penetration process have been identified through a detailed examination of implantation, crater size and volume, sputtered atoms, and dislocations created by the impacts. The crater volume increases linearly with the kinetic energy for a given impactor; and the total dislocation length (TDL) increases with the kinetic energy but depends on the size of the impactor. We found that the TDL does not depend on the used interatomic potential. The results are rationalized based on the physical properties of bcc W.

Lessons Learned from Responsive Molecular Dynamics Studies of the COVID-19 Virus.

Over the past 18 months, the need to perform atomic detail molecular dynamics simulations of the SARS-CoV-2 virion, its spike protein, and other structures related to the viral infection cycle has led biomedical researchers worldwide to urgently seek out all available biomolecular structure information, appropriate molecular modeling and simulation software, and the necessary computing resources to conduct their work. We describe our experiences from several COVID-19 research collaborations and the challenges they presented in terms of our molecular modeling software development and support efforts, our laboratory's local computing environment, and our scientists' use of non-traditional HPC hardware platforms such as public clouds for large scale parallel molecular dynamics simulations.

Fast Surface Dynamics on a Metallic Glass Nanowire.

The dynamics near the surface of glasses can be much faster than in the bulk. We studied the surface dynamics of a Pt-based metallic glass using electron correlation microscopy with sub-nanometer resolution. Our studies show an ∼20 K suppression of the glass transition temperature at the surface. The enhancement in surface dynamics is suppressed by coating the metallic glass with a thin layer of amorphous carbon. Parallel molecular dynamics simulations on Ni80P20 show a similar temperature suppression of the surface glass transition temperature and that the enhanced surface dynamics are arrested by a capping layer that chemically binds to the glass surface. Mobility in the near-surface region occurs via atomic caging and hopping, with a strong correlation between slow dynamics and high cage-breaking barriers and stringlike cooperative motion. Surface and bulk dynamics collapse together as a function of temperature rescaled by their respective glass transition temperatures.

Solution-State Preorganization of Cyclic β-Hairpin Ligands Determines Binding Mechanism and Affinities for MDM2.

Understanding mechanisms of protein folding and binding is crucial to designing their molecular function. Molecular dynamics (MD) simulations and Markov state model (MSM) approaches provide a powerful way to understand complex conformational change that occurs over long time scales. Such dynamics are important for the design of therapeutic peptidomimetic ligands, whose affinity and binding mechanism are dictated by a combination of folding and binding. To examine the role of preorganization in peptide binding to protein targets, we performed massively parallel explicit-solvent MD simulations of cyclic β-hairpin ligands designed to mimic the p53 transactivation domain and competitively bind mouse double minute 2 homologue (MDM2). Disrupting the MDM2-p53 interaction is a therapeutic strategy to prevent degradation of the p53 tumor suppressor in cancer cells. MSM analysis of over 3 ms of aggregate trajectory data enabled us to build a detailed mechanistic model of coupled folding and binding of four cyclic peptides which we compare to experimental binding affinities and rates. The results show a striking relationship between the relative preorganization of each ligand in solution and its affinity for MDM2. Specifically, changes in peptide conformational populations predicted by the MSMs suggest that entropy loss upon binding is the main factor influencing affinity. The MSMs also enable detailed examination of non-native interactions which lead to misfolded states and comparison of structural ensembles with experimental NMR measurements. In contrast to an MSM study of p53 transactivation domain (TAD) binding to MDM2, MSMs of cyclic β-hairpin binding show a conformational selection mechanism. Finally, we make progress toward predicting accurate off rates of cyclic peptides using multiensemble Markov models (MEMMs) constructed from unbiased and biased simulated trajectories.

Deciphering Complex Mechanisms of Resistance and Loss of Potency through Coupled Molecular Dynamics and Machine Learning.

Drug resistance threatens many critical therapeutics through mutations in the drug target. The molecular mechanisms by which combinations of mutations, especially those remote from the active site, alter drug binding to confer resistance are poorly understood and thus difficult to counteract. A machine learning strategy was developed that coupled parallel molecular dynamics simulations with experimental potency to identify specific conserved mechanisms underlying resistance. Physical features were extracted from the simulations, analyzed, and integrated into one consistent and interpretable elastic network model. To rigorously test this strategy, HIV-1 protease variants with diverse mutations were used, with potencies ranging from picomolar to micromolar to the drug darunavir. Feature reduction resulted in a model with four specific features that predicts for both the training and test sets inhibitor binding free energy within 1 kcal/mol of the experimental value over this entire range of potency. These predictive features are physically interpretable, as they vary specifically with affinity and diagonally transverse across the protease homodimer. This physics-based strategy of parallel molecular dynamics and machine learning captures mechanisms by which complex combinations of mutations confer resistance and identify critical features that serve as bellwethers of affinity, which will be critical in future drug design.

Behavior of phase transition of ZnO in nanoscale of time a molecular dynamics computation

The phase transition of Zinc Oxide Wurtzite structure is investigated at the Nanoscale of time and space using Equilibrium Parallel Molecular Dynamics simulation. We extend existing results to higher pressure and temperature. The calculations ran on the RAVEN supercomputer of Cardiff University with DL_POL_4 software. The interatomic interaction modeled by Bukingham-Coulpmb potential for short and long-range interactions. We study the evolution in time of the total energy in isothermal and isobaric ensemble, to find the equilibrium time of the system, under the ranges of pressure 0–200(GPa) and temperature 300–3000(K), This is a new method to investigate the phase transition. Our results are closed to available theoretical and experimental data.

Thermodynamic Barrier for Nanoparticle Penetration into Nanotubes.

It is promising yet challenging to develop efficient methods to separate nanoparticles (NPs) with nanochannel devices. Herein, in order to guide and develop the separation method, the thermodynamic mechanism of NP penetration into solvent-filled nanotubes is investigated by using classical density functional theory. The potential of mean force (PMF) is calculated to evaluate the thermodynamic energy barrier for NP penetration into nanotubes. The accuracy of the theory is validated by comparing it with parallel molecular dynamics simulation. By examining the effects of nanotube size, solvent density, and substrate wettability on the PMF, we find that a large tube, a low bulk solvent density, and a solvophilic substrate can boost the NP penetration into nanotubes. In addition, it is found that an hourglass-shaped entrance can effectively improve the NP penetration efficiency compared with a square-shaped entrance. Furthermore, the minimum separation density of NPs in solution is identified, below which the NP penetration into nanotubes requires an additional driving force. Our findings provide fundamental insights into the thermodynamic barrier for NP penetration into nanotubes, which may provide theoretical guidance for separating two components using microfluidics.

Memory access optimization of molecular dynamics simulation software Crystal-MD on Sunway Taihulight

The radiation damage effect of key structural materials is one of the main research subjects of the numerical reactor. From the perspective of experimental safety and feasibility, Molecular Dynamics (MD) in the materials field is an ideal method for simulating the radiation damage of structural materials. The Crystal-MD represents a massive parallel MD simulation software based on the key material characteristics of reactors. Compared with the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) and ITAP Molecular Dynamics (IMD) software, the Crystal-MD reduces the memory required for software operation to a certain extent, but it is very time-consuming. Moreover, the calculation results of the Crystal-MD have large deviations, and there are also some problems, such as memory limitation and frequent communication during its migration and optimization. In this paper, in order to solve the above problems, the memory access mode of the Crystal-MD software is studied. Based on the memory access mode, a memory access optimization strategy is proposed for a unique architecture of China's supercomputer Sunway TaihuLight. The proposed optimization strategy is verified by the experiments, and experimental results show that the running speed of the Crystal-MD is increased significantly by using the proposed optimization strategy.

86 PFLOPS Deep Potential Molecular Dynamics simulation of 100 million atoms with ab initio accuracy

We present the GPU version of DeePMD-kit, which, upon training a deep neural network model using ab initio data, can drive extremely large-scale molecular dynamics (MD) simulation with ab initio accuracy. Our tests show that for a water system of 12,582,912 atoms, the GPU version can be 7 times faster than the CPU version under the same power consumption. The code can scale up to the entire Summit supercomputer. For a copper system of 113,246,208 atoms, the code can perform one nanosecond MD simulation per day, reaching a peak performance of 86 PFLOPS (43% of the peak). Such unprecedented ability to perform MD simulation with ab initio accuracy opens up the possibility of studying many important issues in materials and molecules, such as heterogeneous catalysis, electrochemical cells, irradiation damage, crack propagation, and biochemical reactions. Program summaryProgram Title: DeePMD-kitCPC Library link to program files:https://doi.org/10.17632/phyn4kgsfx.1Developer’s repository link:https://doi.org/10.5281/zenodo.3961106Licensing provisions: LGPLProgramming language: C++/Python/CUDAJournal reference of previous version: Comput. Phys. Commun. 228 (2018), 178–184.Does the new version supersede the previous version?: Yes.Reasons for the new version: Parallelize and optimize the DeePMD-kit for modern high performance computers.Summary of revisions: The optimized DeePMD-kit is capable of computing 100 million atoms molecular dynamics with ab initio accuracy, achieving 86 PFLOPS in double precision.Nature of problem: Modeling the many-body atomic interactions by deep neural network models. Running molecular dynamics simulations with the models.Solution method: The Deep Potential for Molecular Dynamics (DeePMD) method is implemented based on the deep learning framework TensorFlow. Standard and customized TensorFlow operators are optimized for GPU. Massively parallel molecular dynamics simulations with DeePMD models on high performance computers are supported in the new version.

PathMolD-AB: Spatiotemporal pathways of protein folding using parallel molecular dynamics with a coarse-grained model

Solving the protein folding problem (PFP) is one of the grand challenges still open in computational biophysics. Globular proteins are believed to evolve from initial configurations through folding pathways connecting several thermodynamically accessible states in a free energy landscape until reaching its minimum, inhabited by the stable native structures. Despite its huge computational burden, molecular dynamics (MD) is the leading approach in the PFP studies by preserving the Newtonian temporal evolution in the canonical ensemble. Non-trivial improvements are provided by highly parallel implementations of MD in cost-effective GPUs, concomitant to multiscale descriptions of proteins by coarse-grained minimalist models. In this vein, we present the PathMolD-AB framework, a comprehensive software package for massively parallel MD simulations using the canonical ensemble, structural analysis, and visualization of the folding pathways using the minimalist AB-model. It has, also, a tool to compare the results with proteins re-scaled from the PDB. We simulate and analyze, as case studies, the folding of four proteins: 13FIBO, 2GB1, 1PLC and 5ANZ, with 13, 55, 99 and 223 amino acids, respectively. The datasets generated from simulations correspond to the MD evolution of 3500 folding pathways, encompassing 35×106 states, which contains the spatial amino acid positions, the protein free energies and radii of gyration at each time step. Results indicate that the speedup of our approach grows logarithmically with the protein length and, therefore, it is suited for most of the proteins in the PDB. The predicted structures simulated by PathMolD-AB were similar to the re-scaled biological structures, indicating that it is promising for the study of the PFP study.