Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “molecular dynamic (MD) simulation”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Multi-scale characterization and simulation of impact welding between immiscible Mg/steel alloys

Vaporizing foil actuator spot welding method is used in this paper to join magnesium alloy AZ31 and uncoated high-strength steel DP590, which are typically considered as un-weldable due to their high physical property disparities, low mutual solubility, and the lack of any intermetallic phases. Characterization results from scanning electron microscopy (SEM) and high-resolution transmission electron microscopy (HRTEM) of the weld interface indicate that the impact creates an Mg nanocrystalline interlayer with abundant Fe particles. The interlayer exhibits intact bonding with both DP590 and AZ31 substrates. To investigate the fundamental bond formation mechanisms at the interface, a finite element (FE)-based process simulation is first performed to calculate the local temperature and deformation at the interface under the given macroscopic experimental condition. Finally, taking the FE results at the interface as inputs, molecular dynamics (MD) simulations are conducted to study the interlayer formation at the Mg/Fe interface during the impact and cooling. The results found a high velocity shearing-induced mechanical mixing mechanism that mixes Mg/Fe atoms at the interface and creates the interlayer, leading to the metallurgical bond between Mg/steel alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Using Computationally-Determined Properties for Machine Learning Prediction of Self-Diffusion Coefficients in Pure Liquids

The ability to predict transport properties of liquids quickly and accurately will greatly improve our understanding of fluid properties both in bulk and complex mixtures, as well as in confined environments. Such information could then be used in the design of materials and processes for applications ranging from energy production and storage to manufacturing processes. As a first step, we consider the use of machine learning (ML) methods to predict the diffusion properties of pure liquids. Recent results have shown that Artificial Neural Networks (ANNs) can effectively predict the diffusion of pure compounds based on the use of experimental properties as the model inputs. In the current study, a similar ANN approach is applied to modeling diffusion of pure liquids using fluid properties obtained exclusively from molecular simulations. A diverse set of 102 pure liquids is considered, ranging from small polar molecules (e.g., water) to large nonpolar molecules (e.g., octane). Self-diffusion coefficients were obtained from classical molecular dynamics (MD) simulations. Since nearly all the molecules are organic compounds, a general set of force field parameters for organic molecules was used. The MD methods are validated by comparing physical and thermodynamic properties with experiment. Computational input features for the ANN include physical properties obtained from the MD simulations as well as molecular properties from quantum calculations of individual molecules. Furthermore, fluid properties describing the local liquid structure were obtained from center of mass radial distribution functions (COM-RDFs). Feature sensitivity analysis revealed that isothermal compressibility, heat of vaporization, and the thermal expansion coefficient were the most impactful properties used as input for the ANN model to predict the MD simulated self-diffusion coefficients. The MD-based ANN successfully predicts the MD self-diffusion coefficients with only a subset (2 to 3) of the available computationally determined input features required. A separate ANN model was developed using literature experimental self-diffusion coefficients as model targets. Although this second ML model was not as successful due to a limited number of data points, a good correlation is still observed between experimental and ML predicted self-diffusion coefficients.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Accelerating amorphous polymer electrolyte screening by learning to reduce errors in molecular dynamics simulated properties

Polymer electrolytes are promising candidates for the next generation lithium-ion battery technology. Large scale screening of polymer electrolytes is hindered by the significant cost of molecular dynamics (MD) simulation in amorphous systems: the amorphous structure of polymers requires multiple, repeated sampling to reduce noise and the slow relaxation requires long simulation time for convergence. Here, we accelerate the screening with a multi-task graph neural network that learns from a large amount of noisy, unconverged, short MD data and a small number of converged, long MD data. We achieve accurate predictions of 4 different converged properties and screen a space of 6247 polymers that is orders of magnitude larger than previous computational studies. Further, we extract several design principles for polymer electrolytes and provide an open dataset for the community. Our approach could be applicable to a broad class of material discovery problems that involve the simulation of complex, amorphous materials.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A molecular dynamics simulation study of thermal conductivity anisotropy in β-octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine (β-HMX)

Molecular dynamics (MD) simulations were used to predict the thermal conductivity of β-octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine (β-HMX) along directions normal to the (011), (110), and (010) crystal planes. These directions were selected based on the measured morphological importance of the corresponding crystal surfaces. A reverse non-equilibrium MD approach was used wherein a constant heat flux is imposed along a prescribed direction and the resulting steady-state temperature gradient determined. The coefficient of thermal conductivity λ is the quotient of heat flux and temperature gradient (i.e. Fourier's law). Finite-size effects and sensitivity to imposed heat flux were investigated. The results reveal a modest dependence of the conductivity on crystal orientation, significant finite-size effects, and low sensitivity to imposed flux so long as the Fourier's law analysis is limited to the spatial interval in the simulation cell for which the temperature gradient is constant. Infinite-length thermal conductivities were estimated by linear regression of λ -1 (L) versus reciprocal cell length L -1 for each direction. The predicted values are systematically larger, but within a factor of two, than most published experimental determinations, the latter of which were obtained for pressed-powder or composite samples rather than oriented single crystals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Energy-efficient, Large-scale Molecular Dynamics Simulations via Hardware- and Algorithm-level Optimization

This work aims to develop a framework for energy-efficient computing that will enable molecular dynamics (MD) simulations of large-scale phenomena with atomic precision and simultaneously remove computational bottlenecks limiting the speed of MD simulations. We seek to implement such an approach through the development of surrogate models for the interatomic force calculation combined with the use of mixed numerical precision formats. For a model system of neutral atoms (only pairwise interactions), significant force calculation efficiency improvements were achieved, without detrimental effects on atomic structures or average energies, using single precision, by developing a surrogate model (deep neural network), and by quantizing this surrogate model. For a model system of charged atoms, the reciprocal-space calculation of electrostatic interactions was identified as the main bottleneck, and the development of a surrogate model should be pursued to achieve an estimated one-order-of-magnitude additional speedup.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Orientation and Conformation of Proteins at the Air–Water Interface Determined from Integrative Molecular Dynamics Simulations and Sum Frequency Generation Spectroscopy

Understanding the assembly of proteins at the air-water interface (AWI) informs the formation of protein films, emulsion properties, and protein aggregation. Determination of protein conformation and orientation at an interface is difficult to resolve with a single experimental or simulation technique alone. To date, the interfacial structure of even one of the most widely studied proteins, lysozyme, at the AWI remains unresolved. Here in this study, molecular dynamics (MD) simulations are used to determine if the protein adopts a side-on, head-on, or axial orientation at the AWI with two different forcefields, GROMOS-53a6 + SPC/E and a99SB-disp + TIP4P-D. Vibrational sum frequency generation (SFG) spectroscopy experiments and spectral SFG calculations validate consistency between the structure determined from MD and experiments. Overall, we show with strong agreement that lysozyme adopts an axial conformation at pH 7. Further, we provide molecular-level insight as to how pH influences the binding domains of lysozyme resulting in side-on adsorption near the isoelectric point of the lysozyme.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

All-atom molecular dynamics simulations of weak polyionic brushes: influence of charge density on the properties of polyelectrolyte chains, brush-supported counterions, and water molecules

All atom molecular dynamics (MD) simulations of planar Na+-counterion-neutralized polyacrylic acid (PAA) brushes are performed for varying degrees of ionization (and thereby varying charge density) and varying grafting density. Variation in the PE charge density (or degree of ionization) and grafting density leads to massive changes of the properties of the PE molecules (quantified by the changes in the height and the mobility of the PE brushes) as well as the local arrangement and distribution of the brush-supported counterions and water molecules within the brushes. The effect on the counterions is manifested by the corresponding variation of the counterion mobility, counterion concentration, extent of counterion binding to the charged site of the PE brushes, water-in-salt-like structure formation, and counterion-water–oxygen radial distribution function within the PE brushes. On the other hand, the effect on water molecules is manifested by the corresponding variation of water–oxygen–water–oxygen RDF, local water density, water–water and water–PE functional group hydrogen bond networks, static dielectric constant of water molecules, orientational tetrahedral order parameter, and water mobility. Enforcing such varying degree of ionization of weak polyelectrolytes is possible by changing the pH of the surrounding medium. Thus, our results provide insights into the changes in microstructure (at the atomistic level) of weak polyionic brushes at varying pH. Finally, we anticipate that this knowledge will prove to be vital for the efficient design of several nano-scale systems employing PE brushes such as nanomechanical gates, current rectifiers, etc.

42 ENGINEERING↗
DOE OSTI2021

Investigations of water/oxide interfaces by molecular dynamics simulations

Water/oxide interfaces are ubiquitous on earth and show significant influence on many chemical processes. For example, understanding water and solute adsorption as well as catalytic water splitting can help build better fuel cells and solar cells to overcome our looming energy crisis; the interaction between biomolecules and water/oxide interfaces is one hypothesis to explain the origin of life. However, knowledge in this area is still limited due to the difficulty of studying water/solid interfaces. As a result, research using increasingly sophisticated experimental techniques and computational simulations has been carried out in recent years. Although it is difficult for experimental techniques to provide detailed microscopic structural information, molecular dynamics (MD) simulations have satisfactory performance. In this report, we discuss classical and ab initio MD simulations of water/oxide interfaces. Generally, we are interested in the following questions: How do solid surfaces perturb interfacial water structure? How do interfacial water molecules and adsorbed solutes affect solid surfaces and how do interfacial environments affect solvent and solute behavior? Finally, we discuss progress in the application of neural network potential based MD simulations, which offer a promising future because this approach has already enabled ab initio level accuracy for very large systems and long trajectories.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Machine Learning Predictions of Simulated Self-Diffusion Coefficients for Bulk and Confined Pure Liquids

Diffusion properties of bulk fluids have been predicted using empirical expressions and machine learning (ML) models, suggesting that predictions of diffusion also should be possible for fluids in confined environments. The ability to quickly and accurately predict diffusion in porous materials would enable new discoveries and spur development in relevant technologies such as separations, catalysis, batteries, and subsurface applications. Here in this work, we apply artificial neural network (ANN) models to predict the simulated self-diffusion coefficients of real liquids in both bulk and pore environments. The training data sets were generated from molecular dynamics (MD) simulations of Lennard-Jones particles representing a diverse set of 14 molecules ranging from ammonia to dodecane over a range of liquid pressures and temperatures. Planar, cylindrical, and hexagonal pore models consisted of walls composed of carbon atoms. Our simple model for these liquids was primarily used to generate ANN training data, but the simulated self-diffusion coefficients of bulk liquids show excellent agreement with experimental diffusion coefficients. ANN models based on simple descriptors accurately reproduced the MD diffusion data for both bulk and confined liquids, including the trend of increased mobility in large pores relative to the corresponding bulk liquid.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

De Novo Design of Proteins That Bind Naphthalenediimides, Powerful Photooxidants with Tunable Photophysical Properties

De novo protein design provides a framework to test our understanding of protein function and build proteins with cofactors and functions not found in nature. Here, we report the design of proteins designed to bind powerful photooxidants and the evaluation of the use of these proteins to generate diffusible small-molecule reactive species. Because excited-state dynamics are influenced by the dynamics and hydration of a photooxidant’s environment, it was important to not only design a binding site but also to evaluate its dynamic properties. Thus, we used computational design in conjunction with molecular dynamics (MD) simulations to design a protein, designated NBP (NDI Binding Protein), that held a naphthalenediimide (NDI), a powerful photooxidant, in a programmable molecular environment. Solution NMR confirmed the structure of the complex. We evaluated two NDI cofactors in this de novo protein using ultrafast pump–probe spectroscopy to evaluate light-triggered intra- and intermolecular electron transfer function. Moreover, we demonstrated the utility of this platform to activate multiple molecular probes for protein labeling.

carbonyls↗
DOE OSTI2020

Application of First-Principles-Based Artificial Neural Network Potentials to Multiscale-Shock Dynamics Simulations on Solid Materials

The use of artificial neural network (ANN) potentials trained with first-principles calculations has emerged as a promising approach for molecular dynamics (MD) simulations encompassing large space and time scales while retaining first-principles accuracy. To date, however, the application of ANN-MD has been limited to near-equilibrium processes. Here we combine first-principles-trained ANN-MD with multiscale shock theory (MSST) to successfully describe far-from-equilibrium shock phenomena. Overall, our ANN-MSST-MD approach describes shock-wave propagation in solids with first-principles accuracy but a 5000 times shorter computing time. Accordingly, ANN-MD-MSST was able to resolve fine, long-time elastic deformation at low shock speed, which was impossible with first-principles MD because of the high computational cost. This work thus lays a foundation of ANN-MD simulation to study a wide range of far-from-equilibrium processes.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Applying molecular dynamics simulations to the study of micro cold spray of yttria

Micro cold spray (MCS) is a novel manufacturing process with proven success depositing dense, thick films of technical ceramics without the need for elevated processing temperatures or chemical precursors. The process transforms a loose sub-micrometer powder into an adherent film with mechanical properties approaching that of the bulk material through a phenomenon known as room temperature impact consolidation. While this phenomenon has been demonstrated for a wide variety of ceramic materials and film substrates, yttria stands out as a material that is both well-characterized in experiments and of significant interest for industrial applications due to its excellent refractory properties. This dissertation seeks to investigate the fundamental mechanisms that enable room temperature impact consolidation by using molecular dynamics (MD) simulations of yttria particle impacts and characterizing the deformation behavior of the system.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Computational Design of Miniproteins as SARS-CoV-2 Therapeutic Inhibitors

A rational therapeutic strategy is urgently needed for combating SARS-CoV-2 infection. Viral infection initiates when the SARS-CoV-2 receptor-binding domain (RBD) binds to the ACE2 receptor, and thus, inhibiting RBD is a promising therapeutic for blocking viral entry. In this study, the structure of lead antiviral candidate binder (LCB1), which has three alpha-helices (H1, H2, and H3), is used as a template to design and simulate several miniprotein RBD inhibitors. LCB1 undergoes two modifications: structural modification by truncation of the H3 to reduce its size, followed by single and double amino acid substitutions to enhance its binding with RBD. We use molecular dynamics (MD) simulations supported by ab initio density functional theory (DFT) calculations. Complete binding profiles of all miniproteins with RBD have been determined. The MD investigations reveal that the H3 truncation results in a small inhibitor with a -1.5 kcal/mol tighter binding to RBD than original LCB1, while the best miniprotein with higher binding affinity involves D17R or E11V + D17R mutation. DFT calculations provide atomic-scale details on the role of hydrogen bonding and partial charge distribution in stabilizing the minibinder:RBD complex. This study provides insights into general principles for designing potential therapeutics for SARS-CoV-2.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Xe Recovery from Nuclear Power Plants Off-Gas Streams: Molecular Simulations of Gas Permeation through DD3R Zeolite Membrane

Recent experimental work has shown zeolite membrane-based separation as a promising potential technology for Kr/Xe gas mixtures due to its much lower energy requirements in comparison to cryogenic distillation, the conventional separation method for such mixtures. Such a separation is also economically rewarding because Xe is in high demand, as a valuable product for many applications/processes. In this work, we have used Molecular Dynamics (MD) simulations to study the effects of different conditions, i.e., temperature, pressure, and gas feed composition, on Kr/Xe separation performance via DD3R zeolite membranes. We provide a comprehensive study of the permeation of the different gas species, density profiles, and diffusion coefficients. Molecular simulations show that if the feed is changed from pure Kr/Xe to an equimolar mixture, the Kr/Xe separation factor increases, which agrees with experiments. In addition, when Ar is introduced as a sweep gas, the adsorption of both Kr and Xe increases, while the permeation of pure Kr increases. A similar behavior is observed with equimolar mixtures of Kr/Xe with Ar as the sweep gas. High-separation Kr/Xe selectivity is observed at 50 atm and 425 K but with low total permeation rates. Changing pressure and temperature are found to have profound effects on optimizing the separation selectivity and the permeation throughput.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Thermal and concentration effects on 1 H NMR relaxation of Gd 3+ -aqua using MD simulations and measurements

We report gadolinium-based contrast agents are key in clinical MRI for enhancing the longitudinal NMR relativity (r 1 ) of hydrogen nuclei ( 1 H) in water and improving the contrast among different tissues. The importance of MRI in clinical practice cannot be gainsaid, yet the interpretation of MRI relies on models with severe assumptions, reflecting a poor understanding of the molecular-scale relaxation processes. In a step towards building a clearer understanding of the relaxation processes, here we investigate thermal and concentration effects on r 1 of the Gd 3+ -aqua complex using both semi-classical molecular dynamics (MD) simulations and measurements. We follow the MD simulation approach recently introduced by [Singer et al., Phys. Chem. Chem. Phys., 2021, 23, 20974], in which no NMR relaxation model or free-parameter is assumed to predict r 1 , thereby bringing new insights into the physics of r 1 on a molecular scale. We expand the autocorrelation function G(t) in terms of molecular modes and determine the thermal activation energies of the two largest modes, both of which are consistent with the range of literature values for rotational diffusion. We also determine the activation energies for translational diffusion and low-field electron-spin relaxation, both of which are consistent with the literature. Furthermore, we validate the MD simulations at human body temperature and concentrations of the paramagnetic ion used in clinical MRI, and we quantify the uncertainties in both simulations and measurements.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Modelling dislocation-graphene interactions in a BCC Fe matrix by molecular dynamics simulations and gradient plasticity theory

We report graphene nanosheets (GNS) can enhance the strength and ductility of metal-based composites as they can obstruct the propagation of dislocations. The present article employs Molecular Dynamics (MD) simulations to investigate dislocation-GNS interaction mechanisms and possible influencing factors, including the number of GNS layers, the thickness of the metallic amorphous layer and the C - C bond strength. The results indicated that the shear strength of the metal/GNS interface and the bending stiffness of GNS determined the ability of GNS to block dislocation transmission. A physically based phenomenological parameter that can capture such dislocation-GNS interactions is the mechanical interface energy that has been put forth within gradient plasticity. By fitting the theoretical expressions to the simulation data, it was possible to obtain estimates for the mechanical interface energy for the GNS. It was found that increasing the GNS layers and adding an amorphous layer resulted in a strengthening in the stress–strain response and increased the value of this interfacial parameter. This indicates that the mechanical interfacial energy can be a unified measure for capturing and tuning the strength of various interfaces such as grain boundaries, GNS, amorphous-crystalline interface and bimetal interfaces.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Molecular-Level Insights into the NMR Relaxivity of Gadobutrol Using Quantum and Classical Molecular Simulations

MRI is an indispensable diagnostic tool in modern medicine; however, understanding the molecular-level processes governing NMR relaxation of water in the presence of MRI contrast agents remains a challenge, hindering the molecular-guided development of more effective contrast agents. By using quantum-based polarizable force fields, the first-of-its-kind molecular dynamics (MD) simulations of Gadobutrol are reported where the 1 H NMR longitudinal relaxivity r 1 of the aqueous phase is determined without any adjustable parameters. The MD simulations of r 1 dispersion (i.e., frequency dependence) show good agreement with measurements at frequencies of interest in clinical MRI. Importantly, the simulations reveal key insights into the molecular level processes leading to r 1 dispersion by decomposing the NMR dipole–dipole autocorrelation function G(t) into a discrete set of molecular modes, analogous to the eigenmodes of a quantum harmonic oscillator. The molecular modes reveal important aspects of the underlying mechanisms governing r 1 , such as its multiexponential nature and the importance of the second eigenmodal decay. By simply analyzing the MD trajectories on a parameter-free approach, the Gadobutrol simulations show that the outer-shell water contributes ∼50% of the total relaxivity r 1 compared to the inner-shell water, in contrast to simulations of (nonchelated) gadolinium-aqua where the outer shell contributes only ∼15% of r 1 . The deviation between simulations and measurements of r 1 below clinical MRI frequencies is used to determine the low-frequency electron-spin relaxation time for Gadobutrol, in good agreement with independent studies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Sensitivity of Dislocation-GB interactions to simulation setups in atomistic models

Dislocation-grain boundary (GB) interactions play an integral role in strengthening of crystalline materials, and are dependent on external loading conditions and atomic arrangements at the grain boundary. While molecular dynamics (MD) simulations can provide critical insights into relationships between these parameters and the exact interaction, the outcomes are sensitive to the computational setup. Here, in this work, we explore the effect of computational setup (system size, GB orientation and boundary conditions) on stress-controlled dislocation-grain boundary (DGB) reactions using MD simulations for a well-studied copper symmetric $\langle$1 1 2$\rangle$ tilt boundary. The study demonstrates that the DGB reactions (pinning, absorption, transmission, etc.) are sensitive to the system size and the orientation of the GB to the applied stress. Additionally, the current work shows that there is a critical system size for the dislocation-grain boundary reaction stresses to converge, one which was found to be much larger than the setups in comparable MD studies. This was attributed to the specific stress states and the influence of surfaces, fixed or free, as these computational setups are modified. Finally, a stress-controlled setup with minimal model artifacts is examined, and the effect of coupled Schmid and non-Schmid stress components on the dislocation-GB reactions are discussed.

36 MATERIALS SCIENCE↗