Evaluation of contact angle between asphalt binders and aggregates using Molecular Dynamics (MD) method
Abstract not provided
Yao, Hui↗
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Discussion on molecular dynamics (MD) simulations of the asphalt materials
The application of asphalt materials in pavement engineering has been increasingly widespread and sophisticated over the past several decades. Variations in the properties of asphalt binder during mixing, transportation, and paving can affect the performance of asphalt pavement. However, the asphalt material is a non-homogeneous and complex organic substance, consisting of various molecules with widely various molecular weights, elemental compositions, and structures. This complexity leads to difficulties for researchers to clearly and immediately understand the properties of asphalt materials and their variations. The multi-scale research approach combines macroscopic experimental data and microscopic simulation results from a practical engineering perspective. It helps to improve the understanding of asphalt materials. The molecular dynamics (MD) simulation proposes a corresponding molecular model of asphalt material based on experimental data, and the simulation algorithm is able to derive properties similar to those of real asphalt. Here, this paper provides a comprehensive review of the current studies on MD simulation of asphalt materials, including modeling, properties, and multi-scale analysis. As a key part of the computational simulation, this paper discusses the typical asphalt binder and asphalt-aggregate interface models constructed by different groups, and also presents their differences from real samples and their feasibility based on fundamental properties. After the introduction of molecular models, the extensive work made by researchers based on molecular models is categorically reviewed and discussed. The strengths and weaknesses of MD simulation methods in the study of asphalt materials are also summarized in order to provide the reader with a more comprehensive understanding of the relevant contents and to guide subsequent research.
36 MATERIALS SCIENCE↗
Applications of Molecular Dynamics (MD) in Semiconductor Materials Research.
Abstract not provided.
Zhou, Xiaowang↗
Maturity for Molecular Dynamics (MD) Studies of Hydrogen Embrittlement in Fe-C Based Steels.
Abstract not provided.
Zhou, Xiaowang↗
Impact of Molecular Dynamics (MD) Simulations on Materials Science.
Abstract not provided.
Zhou, Xiaowang↗
Large-Scale Molecular Dynamics (MD) Derivation of Length-Insensitive Dislocation Mobility Laws in Fe-Ni-Cr Stainless Steels.
Abstract not provided.
Zhou, Xiaowang↗
Large-Scale Molecular Dynamics (MD) Derivation of Length-Insensitive Dislocation Mobility Laws in Fe-Ni-Cr Stainless Steels.
Abstract not provided.
Zhou, Xiaowang↗
Impact of Molecular Dynamics (MD) in Growth Processing of Materials.
Abstract not provided.
Zhou, Xiaowang↗
Formation of I 1 stacking fault by deformation defect evolution from grain boundaries in Mg
I 1 stacking faults (SFs) in Mg alloys are regarded as the nucleation sites of $\langle \text{c+a} \rangle$ dislocations that are critical for these alloys to achieve high ductility. Previously it was proposed that the formation of I 1 SFs requires the accumulations of a large number of vacancies, which are difficult to achieve at low temperatures. In this study, molecular dynamics (MD) and molecular statics (MS) simulations based on empirical interatomic potentials were applied to investigate the deformation defect evolutions from the symmetric tilt grain boundaries (GBs) in Mg and Mg-Y alloys under external loading along $\langle c \rangle$-axis. The results show the planar faults (PFs) on Pyramidal I planes first appear due to the nucleation and glide of $\langle {\frac {1}{2}}c + p\rangle$ partial dislocations from GBs, where $\langle p\rangle$ = ${\frac {1}{3}} \langle10\bar{1}0\rangle$. These partial dislocations with pyramidal PFs interact with other defects, including pyramidal PFs themselves, GBs, and $\langle p\rangle$ partial dislocations, generating a large amount of I 1 SFs. Detailed analyses show the nucleation and growth of I 1 SFs are achieved by atomic shuffle events and deformation defect reactions without the requirements of vacancy diffusion. Our simulations also suggest the Y clusters at GBs can reduce the critical stress for the formation of pyramidal PFs and I 1 SFs, which provide a possible reason for the experimental observations that Y promotes the $\langle{\text{c+a}}\rangle$ dislocation activities.
36 MATERIALS SCIENCE↗
Local volume effects in the generalized pseudopotential theory
The generalized pseudopotential theory (GPT) is a compelling approach for deriving real-space transferable interatomic potentials. Using a coarse-grained electronic structure, one can explicitly determine the pair ion-ion and multi-ion interactions in simple and transition metals. While successful in determining bulk properties, in central force metals the GPT fails to describe crystal defects for which there is a significant local volume change. A previous paper [J. A. Moriarty and R. Phillips, Phys. Rev. Lett. 66, 3036 (1991)] found that by allowing the GPT total energy to depend upon some spatially averaged local electron density, the energetics of vacancies and surfaces could be calculated within experimental ranges. In this paper, we develop the formalism further by explicitly calculating the forces and stress tensor associated with this total energy. We call this scheme the adaptive GPT (aGPT) and it is capable of both molecular dynamics (MD) and molecular statics. We apply the aGPT to vacancy formation, divacancy binding, and stacking faults in hcp Mg. We also calculate the local electron density corrections to the bulk elastic constants and phonon dispersion for which there is refinement over the baseline GPT treatment. Furthermore, we demonstrate aGPT-MD simulation through the calculation of thermal expansion in magnesium to 700 K.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Solid-state Precipitation in Molecular Dynamics: KMC-MD Hybrid Simulations
A critical drawback of molecular dynamics (MD) simulations when modeling physical properties is the limitation to short timescales. Solid state precipitation is one behavior that is not significantly observable at timescales accessible to MD, and thus has been difficult to simulate at an MD scale. Taking inspiration from Kinetic Monte-Carlo (KMC) methods for simulating diffusion on a rigid lattice, we have developed a hybrid KMC-MD approach to enable simulation of solid-state precipitation during MD simulation implemented in LAMMPS. This method incorporates the atomic neighbor hopping behavior utilized by KMC simulations into a MD simulation with a variable local lattice configuration. By accelerating nearest-neighbor atomic hopping with this technique, solid-state precipitation can be observed at an MD timescale. Utilizing theory based on persistent-embryo methods for liquid-solid precipitation in MD we demonstrate the calculation of critical nucleus size for the gamma’ precipitation out of gamma phase in the Ni-Al system.
Molecular Dynamics↗
Solid-state Precipitation in Molecular Dynamics: kMC-MD Hybrid Simulations
A critical drawback of molecular dynamics (MD) simulations when modeling physical properties is the limitation to short timescales. Solid state precipitation is one behavior that is not significantly observable at timescales accessible to MD, and thus has been difficult to simulate at an MD scale. Taking inspiration from Kinetic Monte-Carlo (KMC) methods for simulating diffusion on a rigid lattice, we have developed a hybrid KMC-MD approach to enable simulation of solid-state precipitation during MD simulation implemented in LAMMPS. This method incorporates the atomic neighbor hopping behavior utilized by KMC simulations into a MD simulation with a variable local lattice configuration. By accelerating nearest-neighbor atomic hopping with this technique, solid-state precipitation can be observed at an MD timescale. Utilizing theory based on persistent-embryo methods for liquid-solid precipitation in MD we demonstrate the calculation of critical nucleus size for the gamma’ precipitation out of gamma phase in the Ni-Al system.
Molecular Dynamics↗
Self-Healing Ceramics and Mechanical Metamaterials
We performed large scale molecular dynamics (MD) simulations and reactive & quantum molecular dynamics simulations to investigate: (1) faceting, grain growth, and crack healing in ceramics; (2) stress-release mechanism and healing in fibers; and (3) mechanical properties of two-dimensional metamaterials. We also designed highly accurate interatomic potentials and forces using artificial neural networks (ANN) and first-principles molecular dynamics simulation data for use in our MD and reactive MD simulations. We demonstrated the efficacy of modeling quantum materials atomistic simulation data using unsupervised deep learning approaches on a quantum computer. These achievements are elaborated in the following sections.
36 MATERIALS SCIENCE↗
Machine Learning‐Augmented Molecular Dynamics Simulations (MD) Reveal Insights Into the Disconnect Between Affinity and Activation of ZTP Riboswitch Ligands
Abstract The challenge of targeting RNA with small molecules necessitates a better understanding of RNA–ligand interaction mechanisms. However, the dynamic nature of nucleic acids, their ligand‐induced stabilization, and how conformational changes influence gene expression pose significant difficulties for experimental investigation. This work employs a combination of computational and experimental methods to address these challenges. By integrating structure‐informed design, crystallography, and machine learning‐augmented all‐atom molecular dynamics simulations (MD), we synthesized, biophysically and biochemically characterized, and studied the dissociation of a library of small molecule activators of the 5‐aminoimidazole–4–carboxamide ribonucleotide triphosphate (ZTP) riboswitch, a ligand‐binding RNA motif that regulates bacterial gene expression. We uncovered key interaction mechanisms, revealing valuable insights into the role of ligand binding kinetics on riboswitch activation. Further, we established that ligand on‐rates determine activation potency as opposed to binding affinity and elucidated RNA structural differences, which provide mechanistic insights into the interplay of RNA structure on riboswitch activation.
Chemistry↗
Allosterically coupled conformational dynamics in solution prepare the sterol transfer protein StarD4 to release its cargo upon interaction with target membranes
Complex mechanisms regulate the cellular distribution of cholesterol, a critical component of eukaryote membranes involved in regulation of membrane protein functions directly and through the physiochemical properties of membranes. StarD4, a member of the steroidogenic acute regulator-related lipid-transfer (StART) domain (StARD)-containing protein family, is a highly efficient sterol-specific transfer protein involved in cholesterol homeostasis. Its mechanism of cargo loading and release remains unknown despite recent insights into the key role of phosphatidylinositol phosphates in modulating its interactions with target membranes. We have used large-scale atomistic Molecular dynamics (MD) simulations to study how the dynamics of cholesterol bound to the StarD4 protein can affect interaction with target membranes, and cargo delivery. We identify the two major cholesterol (CHL) binding modes in the hydrophobic pocket of StarD4, one near S136&S147 (the Ser-mode), and another closer to the putative release gate located near W171, R92&Y117 (the Trp-mode). We show that conformational changes of StarD4 associated directly with the transition between these binding modes facilitate the opening of the gate. To understand the dynamics of this connection we apply a machine-learning algorithm for the detection of rare events in MD trajectories (RED), which reveals the structural motifs involved in the opening of a front gate and a back corridor in the StarD4 structure occurring together with the spontaneous transition of CHL from the Ser-mode of binding to the Trp-mode. Further analysis of MD trajectory data with the information-theory based NbIT method reveals the allosteric network connecting the CHL binding site to the functionally important structural components of the gate and corridor. Mutations of residues in the allosteric network are shown to affect the performance of the allosteric connection. These findings outline an allosteric mechanism which prepares the CHL-bound StarD4 to release and deliver the cargo when it is bound to the target membrane.
59 BASIC BIOLOGICAL SCIENCES↗
25 Mg NMR and Computational Modeling Studies of the Solvation Structures and Molecular Dynamics in Magnesium Based Liquid Electrolytes
There is increasing evidence that the solvation structure of the active components in a liquid electrolyte solution strongly impacts the performance in electrochemical applications. In this work, the nanoscale solvation structures and dynamics of Mg(BH 4 ) 2 and Mg(TFSI) 2 dissolved in diglyme (DGM) at various concentrations and ratios of Mg(BH 4 ) 2 /Mg(TFSI) 2 were investigated using a combination of natural abundance 25 Mg NMR, quantum chemistry calculations of 25 Mg NMR chemical shifts, classical molecular dynamics (MD) calculations, and electrochemical performance tests. By mixing two competing Mg salts, we were able to reduce the strong covalent interactions between Mg 2+ and BH 4 – anions. A small increase is observed in the coordination number of Mg-TFSI and a significant increase in the interaction of Mg 2+ ions with glymes. Through a combination of NMR, DFT and MD simulations, various stable species around 1 nm in size were detected in the mixed salt solution, which play key roles in the enhanced electrochemical performance of the mixed electrolyte. It is established that for the neat Mg(TFSI) 2 in DGM electrolyte at dilute concentrations the TFSI - is fully dissociated from Mg 2+ . At higher concentrations, Mg 2+ and TFSI - are only partially dissociated as contact ion pairs are formed. In contrast, at 0.01 M Mg(BH 4 ) 2 (saturated concentration) in DGM, the first solvation shell of a Mg 2+ ion contains two BH 4 - anions and one DGM molecule, while the second solvation shell consists of five to six DGM molecules. An exchange mechanism between the solvation structures in the combined electrolyte containing both Mg(BH 4 ) 2 and Mg(TFSI) 2 in DGM was found to result in the observation of a single 25 Mg NMR peak. This exchange is responsible for an increase in uncoordinated anions, as well as improved stability and ionic conductivity as compared to single anion solution. Solvent molecule rearrangement and direct Mg-ion exchange between the basic solvation structures are hypothesized as likely reasons for the exchange. We elucidate that the solvent rearrangement is energetically much more favorable than direct Mg-ion hopping and is thus suggested as the dominant exchange mechanism.
Magnesium battery, electrolytes, Mg(BH4)2, Mg(TFSI↗
Transitioning NWChem to the Next Generation of Manycore Machines
The NorthWest chemistry (NWChem) modeling software is a popular molecular chemistry simulation software that was designed from the start to work on massively parallel processing supercomputers [1-3]. It contains an umbrella of modules that today includes self-consistent eld (SCF), second order Møller-Plesset perturbation theory (MP2), coupled cluster (CC), multiconguration self-consistent eld (MCSCF), selected conguration interaction (CI), tensor contraction engine (TCE) many body methods, density functional theory (DFT), time-dependent density functional theory (TDDFT), real-time time-dependent density functional theory, pseudopotential plane-wave density functional theory (PSPW), band structure (BAND), ab initio molecular dynamics (AIMD), Car-Parrinello molecular dynamics (MD), classical MD, hybrid quantum mechanics molecular mechanics (QM/MM), hybrid ab initio molecular dynamics molecular mechanics (AIMD/MM), gauge independent atomic orbital nuclear magnetic resonance (GIAO NMR), conductor like screening solvation model (COSMO), conductor-like screening solvation model based on density (COSMO-SMD), and reference interaction site model (RISM) solvation models, free energy simulations, reaction path optimization, parallel in time, among other capabilities [4]. Moreover, new capabilities continue to be added with each new release.
Bylaska, Eric J.↗
Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19
In this work, we present a supercomputer-driven pipeline for in silico drug discovery using enhanced sampling molecular dynamics (MD) and ensemble docking. Ensemble docking makes use of MD results by docking compound databases into representative protein binding-site conformations, thus taking into account the dynamic properties of the binding sites. We also describe preliminary results obtained for 24 systems involving eight proteins of the proteome of SARS-CoV-2. The MD involves temperature replica exchange enhanced sampling, making use of massively parallel supercomputing to quickly sample the configurational space of protein drug targets. Using the Summit supercomputer at the Oak Ridge National Laboratory, more than 1 ms of enhanced sampling MD can be generated per day. We have ensemble docked repurposing databases to 10 configurations of each of the 24 SARS-CoV-2 systems using AutoDock Vina. Comparison to experiment demonstrates remarkably high hit rates for the top scoring tranches of compounds identified by our ensemble approach. We also demonstrate that, using Autodock-GPU on Summit, it is possible to perform exhaustive docking of one billion compounds in under 24 h. Finally, we discuss preliminary results and planned improvements to the pipeline, including the use of quantum mechanical (QM), machine learning, and artificial intelligence (AI) methods to cluster MD trajectories and rescore docking poses.
60 APPLIED LIFE SCIENCES↗