Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “forcing fields”

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 577 records · Page 32

Predicting Hugoniot equation of state in erythritol with ab initio and reactive molecular dynamics

Erythritol has been proposed as an inert surrogate for developing theoretical and computational models to study aging in energetic materials. In this work, we present a comparison of mechanical and shock properties of erythritol computed using the ReaxFF reactive force field and from ab initio calculations employing density functional theory (DFT). We screened eight different ReaxFF parameterizations, of which the CHO parameters developed for hydrocarbon oxidation provide the most accurate predictions of mechanical properties and the crystal structure of erythritol. Further validation of the applicability of this ReaxFF parameterization for modeling erythritol is demonstrated by comparing predictions of the elastic constants, crystal structure, vibrational density of states, and Hugoniot curves against DFT calculations. The ReaxFF predictions are in close agreement with the DFT simulations for the elastic constants and shock Hugoniot when the crystal is loaded along its c axis but show as much as 30% disagreement in the elastic constants in the ab plane and 12% difference in shock pressures when shocked along the a or b crystal axes. Last, we compare thermomechanical properties predicted from classical molecular dynamics with those calculated using the quasi-harmonic approximation and show that quantum mechanical effects produce large discrepancies in the computed values of heat capacity and thermal expansion coefficients compared with classical assumptions. Combining classical molecular dynamics predictions of mechanical behavior with phonon-based calculations of thermal behaviors, we show that predicted shock-induced temperatures for pressures up to 6.5 GPa do not exceed the pressure-dependent melting point of erythritol.

Hu, Jing↗

A classical model for three-body interactions in aqueous ionic systems

We present a classical induction model to evaluate the three-body ion–water–water (I–W–W) and water–water–water (W–W–W) interactions in aqueous ionic systems. The classical description of the induction energy is based on electrostatic distributed multipoles up to hexadecapole and distributed polarizabilities up to quadrupole–quadrupole on the O and H atoms of water. The monatomic ions were described by a point charge and a dipole–dipole polarizability, while for the polyatomic ions, distributed multipoles up to hexadecapole and distributed polarizabilities up to quadrupole–quadrupole were used. The accuracy of the classical model is benchmarked against an accurate dataset of 936 (I–W–W) and 2184 (W–W–W) three-body terms for 13 different monatomic and polyatomic cation and anion systems. The classical model shows excellent agreement with the reference second order Moller–Plesset and coupled-cluster single double and perturbative triple [CCSD(T)] three-body energies. The Root-Mean-Square-Errors (RMSEs) for monatomic cations, monatomic anions, and polyatomic ions were 0.29, 0.25, and 0.12 kcal/mol, respectively. The corresponding RMSE for 1744 CCSD(T)/aVTZ three-body (W–W–W) energies, used to train MB-pol, was 0.12 kcal/mol. The accuracy of the proposed classical model demonstrates that the three-body term for aqueous ionic systems can be accurately modeled classically. This approach provides a fast, efficient, and as-accurate path toward modeling the three-body term in aqueous ionic systems that is fully transferable across systems with different ions without the need to fit to tens of thousands of ab initio calculations for each ion to extend existing many-body force fields to interactions between water and ions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Helium droplet infrared spectroscopy of the butyl radicals

Butyl radicals (n-, s-, i-, and tert-butyl) are formed from the pyrolysis of stable precursors (1-pentyl nitrite, 2-methyl-1-butyl nitrite, isopentyl nitrite, and azo-tert-butane, respectively). The radicals are doped into a beam of liquid helium droplets and probed with infrared action spectroscopy from 2700 to 3125 cm -1 , allowing for a low temperature measurement of the CH stretching region. The presence of anharmonic resonance polyads in the 2800–3000 cm -1 region complicates its interpretation. To facilitate spectral assignment, the anharmonic resonances are modeled with two model Hamiltonian approaches that explicitly couple CH stretch fundamentals to HCH bend overtones and combinations: a VPT2+K normal mode model based on coupled-cluster with single, double, and perturbative triple excitations [CCSD(T)] quartic force fields and a semi-empirical local mode model. Both of these computational methods provide generally good agreement with the experimental spectra.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Thermal conductivity tensor of γ and ε -hexanitrohexaazaisowurtzitane as a function of pressure and temperature

Using reverse non-equilibrium molecular dynamics simulations, we have determined the dependences on temperature and pressure of the thermal conductivity tensors for the monoclinic γ and ε polymorphs of hexanitrohexaazaisowurtzitane (HNIW or CL20). In this work, a recently developed non-reactive force field [X. Bidault and S. Chaudhuri, RSC Adv. 9, 39649–39661 (2019)], designed to study polymorphism and phase transitions in CL20, is employed. The effects of temperature and pressure are investigated between 200 and 500 K and up to 0.5 GPa for γ-CL20 and 2 GPa for ε-CL20. In order to obtain the full thermal conductivity tensor, $κ_{ij}$, for the monoclinic crystals, four distinct heat propagation directions are used. We find that $κ_{ij}$ for both polymorphs is more isotropic than for other energetic molecular crystals, including α- and γ-RDX, β-HMX, and PETN, with a maximum difference of 9.8% between orientations observed at 300 K and 0 GPa for γ-CL20 and a maximum difference of 4.8% for ε-CL20. The average thermal conductivity, $\bar{κ}$, of ε-CL20 is 6.4% larger than that of γ-CL20 at 300 K and 0 GPa. Analytic linear functions of the inverse temperature and the pressure are provided, which fit the data well and can be used to predict the thermal conductivity of both polymorphs for any orientation, pressure, and temperature in and around the fitting range. Our predictions agree reasonably well with the limited available experimental data, for which the polymorph type is unknown.

36 MATERIALS SCIENCE↗

Electronic structure theory on modeling short-range noncovalent interactions between amino acids

While short-range noncovalent interactions (NCIs) are proving to be of importance in many chemical and biological systems, these atypical bindings happen within the so-called van der Waals envelope and pose an enormous challenge for current computational methods. We introduce SNCIAA, a database of 723 benchmark interaction energies of short-range noncovalent interactions between neutral/charged amino acids originated from protein x-ray crystal structures at the “gold standard” coupled-cluster with singles, doubles, and perturbative triples/complete basis set [CCSD(T)/CBS] level of theory with a mean absolute binding uncertainty less than 0.1 kcal/mol. Subsequently, a systematic assessment of commonly used computational methods, such as the second-order Møller−Plesset theory (MP2), density functional theory (DFT), symmetry-adapted perturbation theory (SAPT), composite electronic-structure methods, semiempirical approaches, and the physical-based potentials with machine learning (IPML) on SNCIAA is carried out. It is shown that the inclusion of dispersion corrections is essential even though these dimers are dominated by electrostatics, such as hydrogen bondings and salt bridges. Overall, MP2, ωB97M-V, and B3LYP+D4 turned out to be the most reliable methods for the description of short-range NCIs even in strongly attractive/repulsive complexes. SAPT is also recommended in describing short-range NCIs only if the δMP2 correction has been included. The good performance of IPML for dimers at close-equilibrium and long-range conditions is not transferable to the short-range. We expect that SNCIAA will assist the development/improvement/validation of computational methods, such as DFT, force-fields, and ML models, in describing NCIs across entire potential energy surfaces (short-, intermediate-, and long-range NCIs) on the same footing.

Chemistry↗

Nanoscale structure of a hybrid aqueous–nonaqueous electrolyte

A new class of electrolytes have been reported, hybridizing aqueous with non-aqueous solvents, which combines non-flammability and non-toxicity characteristics of aqueous electrolytes with the superior electrochemical stability of non-aqueous systems. Here, we report measurements of the structure of an electrolyte composed of an equal-mass mixture of 21 m LiTFSI-water and 9 m LiTFSI-dimethyl carbonate using high-energy x-ray diffraction and polarized neutron diffraction with isotope substitution. Neutron structure factors from partially and fully deuterated samples exhibit peaks at low scattering vector Q that we ascribe to long-range correlations involving both solvent molecules and TFSI − anions. Here we compare both sets of measurements with results of molecular dynamics simulations based on a polarizable force field. The structures derived from simulations are generally in agreement with those measured, except that neutron structure factors predicted for two partially deuterated samples show very intense scattering increasing up to the low- Q limit of simulation, indicating a partial segregation between the two solvents not observed in experimental measurements.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

MBX: A many-body energy and force calculator for data-driven many-body simulations

Many-Body eXpansion (MBX) is a C++ library that implements many-body potential energy functions (PEFs) within the “many-body energy” (MB-nrg) formalism. MB-nrg PEFs integrate an underlying polarizable model with explicit machine-learned representations of many-body interactions to achieve chemical accuracy from the gas to the condensed phases. MBX can be employed either as a stand-alone package or as an energy/force engine that can be integrated with generic software for molecular dynamics and Monte Carlo simulations. MBX is parallelized internally using Open Multi-Processing and can utilize Message Passing Interface when available in interfaced molecular simulation software. In this study, MBX enables classical and quantum molecular simulations with MB-nrg PEFs, as well as hybrid simulations that combine conventional force fields and MB-nrg PEFs, for diverse systems ranging from small gas-phase clusters to aqueous solutions and molecular fluids to biomolecular systems and metal-organic frameworks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

First-principles molten salt phase diagrams through thermodynamic integration

Precise prediction of phase diagrams in molecular dynamics simulations is challenging due to the simultaneous need for long time and large length scales and accurate interatomic potentials. Here, we show that thermodynamic integration from low-cost force fields to neural network potentials trained using density-functional theory (DFT) enables rapid first-principles prediction of the solid–liquid phase boundary in the model salt NaCl. We use this technique to compare the accuracy of several DFT exchange–correlation functionals for predicting the NaCl phase boundary and find that the inclusion of dispersion interactions is critical to obtain good agreement with experiment. Importantly, our approach introduces a method to predict solid–liquid phase boundaries for any material at an ab initio level of accuracy, with the majority of the computational cost at the level of classical potentials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Quantum bath augmented stochastic nonequilibrium atomistic simulations for molecular heat conduction

Classical molecular dynamics (MD) has been shown to be effective in simulating heat conduction in certain molecular junctions since it inherently takes into account some essential methodological components which are lacking in the quantum Landauer-type transport model, such as many-body full force-field interactions, anharmonicity effects and nonlinear responses for large temperature biases. However, the classical MD reaches its limit in the environments where the quantum effects are significant (e.g. with low-temperatures substrates, presence of extremely high frequency molecular modes). Here, we present an atomistic simulation methodology for molecular heat conduction that incorporates the quantum Bose–Einstein statistics into an “effective temperature” in the form of a modified Langevin equation. We show that the results from such a quasi-classical effective temperature MD method deviates drastically when the baths temperature approaches zero from classical MD simulations and the results converge to the classical ones when the bath approaches the high-temperature limit, which makes the method suitable for full temperature range. In addition, we show that our quasi-classical thermal transport method can be used to model the conducting substrate layout and molecular composition (e.g. anharmonicities, high-frequency modes). Anharmonic models are explicitly simulated via the Morse potential and compared to pure harmonic interactions to show the effects of anharmonicities under quantum colored bath setups. Finally, the chain length dependence of heat conduction is examined for one-dimensional polymer chains placed in between quantum augmented baths.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Unusual dynamics of tetrahedral liquids caused by the competition between dynamic heterogeneity and structural heterogeneity

Tetrahedral liquids exhibit intriguing thermodynamic and transport properties because of the various ways tetrahedra can be packed and connected. Recently, an unusual temperature dependence of the stretching exponent β in a model tetrahedral liquid ZnCl 2 from T m + 85 K to T m + 35 K has been reported using neutron-spin echo spectroscopy. This discovery stands in sharp contrast to other glass-forming liquids. In this study, we conducted neural network force field driven molecular dynamic simulations of ZnCl 2 . We found a non-monotonic temperature dependence of β from liquid to supercooled liquid temperatures. Further structural decomposition and dynamic analysis suggest that this unusual dynamic behavior is a result of the competition between the decrease in the diversity of tetrahedra motifs (structural heterogeneity) and the increase in glassy dynamic heterogeneity. Furthermore, this result may contribute to new understandings of the structural relaxation of other network liquids.

36 MATERIALS SCIENCE↗

Energy conversion and transport in molecular-scale junctions

Molecular-scale junctions (MSJs) have been considered the ideal testbed for probing physical and chemical processes at the molecular scale. Due to nanometric confinement, charge and energy transport in MSJs are governed by quantum mechanically dictated energy profiles, which can be tuned chemically or physically with atomic precision, offering rich possibilities beyond conventional semiconductor devices. While charge transport in MSJs has been extensively studied over the past two decades, understanding energy conversion and transport in MSJs has only become experimentally attainable in recent years. As demonstrated recently, by tuning the quantum interplay between the electrodes, the molecular core, and the contact interfaces, energy processes can be manipulated to achieve desired functionalities, opening new avenues for molecular electronics, energy harvesting, and sensing applications. This Review provides a comprehensive overview and critical analysis of various forms of energy conversion and transport processes in MSJs and their associated applications. We elaborate on energy-related processes mediated by the interaction between the core molecular structure in MSJs and different external stimuli, such as light, heat, electric field, magnetic field, force, and other environmental cues. Key topics covered include photovoltaics, electroluminescence, thermoelectricity, heat conduction, catalysis, spin-mediated phenomena, and vibrational effects. Furthermore, the review concludes with a discussion of existing challenges and future opportunities, aiming to facilitate in-depth future investigation of promising experimental platforms, molecular design principles, control strategies, and new application scenarios.

Charge transport↗

Accuracy of Kohn–Sham density functional theory for warm- and hot-dense matter equation of state

We study the accuracy of Kohn–Sham density functional theory (DFT) for warm- and hot-dense matter (WDM and HDM). Specifically, considering a wide range of systems, we perform accurate ab initio molecular dynamics simulations with temperature-independent local/semilocal density functionals to determine the equations of state at compression ratios of 3x–7x and temperatures near 1 MK. We find very good agreement with path integral Monte Carlo benchmarks, while having significantly smaller error bars and smoother data, demonstrating the accuracy of DFT for the study of WDM and HDM at such conditions. In addition, using a Δ-machine learned force field scheme, we confirm that the DFT results are insensitive to the choice of exchange-correlation functional, whether local, semilocal, or nonlocal.

Suryanarayana, Phanish (ORCID:0000000151720049)↗

Determining the hydronium pKα at platinum surfaces and the effect on pH-dependent hydrogen evolution reaction kinetics

Electrocatalytic hydrogen evolution reaction (HER) is critical for green hydrogen generation and exhibits distinct pH-dependent kinetics that have been elusive to understand. A molecular-level understanding of the electrochemical interfaces is essential for developing more efficient electrochemical processes. Here we exploit an exclusively surface-specific electrical transport spectroscopy (ETS) approach to probe the Pt-surface water protonation status and experimentally determine the surface hydronium pK a = 4.3. Quantum mechanics (QM) and reactive dynamics using a reactive force field (ReaxFF) molecular dynamics (RMD) calculations confirm the enrichment of hydroniums (H 3 O + * ) near Pt surface and predict a surface hydronium pK a of 2.5 to 4.4, corroborating the experimental results. Importantly, the observed Pt-surface hydronium pK a correlates well with the pH-dependent HER kinetics, with the protonated surface state at lower pH favoring fast Tafel kinetics with a Tafel slope of 30 mV per decade and the deprotonated surface state at higher pH following Volmer-step limited kinetics with a much higher Tafel slope of 120 mV per decade, offering a robust and precise interpretation of the pH-dependent HER kinetics. These insights may help design improved electrocatalysts for renewable energy conversion.

Zhong, Guangyan↗

Ab initio generalized Langevin equation

We introduce a machine learning–based approach called ab initio generalized Langevin equation (AIGLE) to model the dynamics of slow collective variables (CVs) in materials and molecules. In this scheme, the parameters are learned from atomistic simulations based on ab initio quantum mechanical models. Force field, memory kernel, and noise generator are constructed in the context of the Mori–Zwanzig formalism, under the constraint of the fluctuation–dissipation theorem. Combined with deep potential molecular dynamics and electronic density functional theory, this approach opens the way to multiscale modeling in a variety of situations. Here, we demonstrate this capability with a study of two mesoscale processes in crystalline lead titanate, namely the field-driven dynamics of a planar ferroelectric domain wall, and the dynamics of an extensive lattice of coarse-grained electric dipoles. In the first case, AIGLE extends the reach of ab initio simulations to a regime of noise-driven motions not accessible to molecular dynamics. In the second case, AIGLE deals with an extensive set of CVs by adopting a local approximation for the memory kernel and retaining only short-range noise correlations. The scheme is computationally more efficient than molecular dynamics by several orders of magnitude and mimics the microscopic dynamics at low frequencies where it reproduces accurately the dominant far-infrared absorption frequency.

97 MATHEMATICS AND COMPUTING↗

Prediction of carbon nanostructure mechanical properties and the role of defects using machine learning

Graphene-based nanostructures hold immense potential as strong and lightweight materials, however, their mechanical properties such as modulus and strength are difficult to fully exploit due to challenges in atomic-scale engineering. This study presents a database of over 2,000 pristine and defective nanoscale CNT bundles and other graphitic assemblies, inspired by microscopy, with associated stress–strain curves from reactive molecular dynamics (MD) simulations using the reactive INTERFACE force field (IFF-R). These 3D structures, containing up to 80,000 atoms, enable detailed analyses of structure-stiffness-failure relationships. By leveraging the database and physics- and chemistry-informed machine learning (ML), accurate predictions of elastic moduli and tensile strength are demonstrated at speeds 1,000 to 10,000 times faster than efficient MD simulations. Hierarchical Graph Neural Networks with Spatial Information (HS-GNNs) are introduced, which integrate chemistry knowledge. HS-GNNs as well as extreme gradient boosted trees (XGBoost) achieve forecasts of mechanical properties of arbitrary carbon nanostructures with only 3 to 6% mean relative error. The reliability equals experimental accuracy and is up to 20 times higher than other ML methods. Predictions maintain 8 to 18% accuracy for large CNT bundles, CNT junctions, and carbon fiber cross-sections outside the training distribution. The physics- and chemistry-informed HS-GNN works remarkably well for data outside the training range while XGBoost works well with limited training data inside the training range. The carbon nanostructure database is designed for integration with multimodal experimental and simulation data, scalable beyond 100 nm size, and extendable to chemically similar compounds and broader property ranges. The ML approaches have potential for applications in structural materials, nanoelectronics, and carbon-based catalysts.

Winetrout, Jordan J.↗

Finite-temperature vibrational full configuration interaction

Thermodynamic functions of an ideal molecular gas due to its anharmonic vibrations are evaluated in a wide range of temperature (T) by the vibrational full-configuration-interaction (FCI) method using a quartic force field and a finite number (N) of harmonic-oscillator basis functions along each normal mode. The thermodynamic functions considered are the grand potential (Ω), internal energy (U), and entropy (S). They are compared with those obtained from the Bose–Einstein theory with or without truncation of the harmonic-oscillator basis functions after quantum number N–1. The comparison reveals that the finite-basis-set errors in Ω and U are, respectively, k B Tln(k B T/Nℏω) and k B T per mode in the high-T limit, obscuring anharmonic effects when k B T > ℏω, where ω is the lowest mode frequency. Here, the benchmark data for several low-order perturbation corrections to Ω, U, and S are also obtained as the numerical derivatives of their FCI values with respect to dimensionless perturbation strength, and the domain of T and N in which these data are reliable (for the N → ∞ limits) is discussed.

74 ATOMIC AND MOLECULAR PHYSICS↗

Flat-histogram extrapolation as a useful tool in the age of big data

In this work, we review recent work by the authors to revisit the concept of extrapolating thermodynamic properties of classical systems using statistical mechanical principles. Specifically, we discuss how the combination of these principles with biased sampling techniques enables the prediction of free energy landscapes and other detailed information, such as structural properties, of the system in question. Remarkably accurate estimates of physical properties across a broad range of conditions have been achieved using this approach, greatly reducing the number of simulations needed to explore a given system's behaviour. While approximate, these extrapolations significantly amplify the amount of reasonably accurate information that can be extracted from simulations enabling a small set of them to feed data-intensive regression algorithms such as neural networks. Thus, this extrapolation methodology represents a useful tool for performing tasks such as high-throughput screening of physical properties, optimising force field parameters, exploring equilibrium phase behaviour, and enabling theory-guided data science for these systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Simulating water adsorption in metal–organic frameworks with open metal sites using the 12-6-4 Lennard–Jones potential

Metal-organic frameworks (MOFs) with coordinatively unsaturated open metal sites (OMSs) are promising sorbent materials in atmospheric water harvesting (AWH) systems at low relative humidity (RH) due to their strong interactions with water molecules. However, accurate computational modelling of water adsorption in those materials are challenging, as standard force fields (FFs) based on the 12-6 Lennard-Jones (L-J) potential cannot properly describe the water-OMS interactions. In biomolecular simulations, the 12-6-4 L-J potential has been successfully used to model metal ion-water interactions in which the extra 1/r 4 -term is for charge-induced dipole electrostatics. In this work, we adopted this strategy and used the 12-6-4 L-J potential to model water-OMS interactions in MOFs for low RH AWH applications. Notably, without any modifications, the parameters used for aqueous metal ions were able to greatly improve the accuracy in predicting water adsorption isotherms in MOF-74, which highlights the simplicity and transferability of this method.

74 ATOMIC AND MOLECULAR PHYSICS↗