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Results for “MOLECULAR DYNAMICS METHOD”

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.

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At least 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Ammonia-salt solvent promotes cellulosic biomass deconstruction under ambient pretreatment conditions to enable rapid soluble sugar production at ultra-low enzyme loadings

In this paper, we report a novel ammonia : ammonium salt solvent based pretreatment process that can rapidly dissolve crystalline cellulose into solution and eventually produce highly amorphous cellulose under near-ambient conditions. Pre-activating the cellulose I allomorph to its ammonia–cellulose swollen complex (or cellulose III allomorph) at ambient temperatures facilitated rapid dissolution of the pre-activated cellulose in the ammonia-salt solvent ( i.e. , ammonium thiocyanate salt dissolved in liquid ammonia) at ambient pressures. For the first time in reported literature, we used time-resolved in situ neutron scattering methods to characterize the cellulose polymorphs structural modification and understand the mechanism of crystalline cellulose dissolution into a ‘molecular’ solution in real-time using ammonia-salt solvents. We also used molecular dynamics simulations to provide insight into solvent interactions that non-covalently disrupted the cellulose hydrogen-bonding network and understand how such solvents are able to rapidly and fully dissolve pre-activated cellulose III. Importantly, the regenerated amorphous cellulose recovered after pretreatment was shown to require nearly ~50-fold lesser cellulolytic enzyme usage compared to native crystalline cellulose I allomorph for achieving near-complete hydrolytic conversion into soluble sugars. Lastly, we provide proof-of-concept results to further showcase how such ammonia-salt solvents can pretreat and fractionate lignocellulosic biomass like corn stover under ambient processing conditions, while selectively co-extracting ~80–85% of total lignin, to produce a highly digestible polysaccharide-enriched feedstock for biorefinery applications. Unlike conventional ammonia-based pretreatment processes ( e.g. , Ammonia Fiber Expansion or Extractive Ammonia pretreatments), the proposed ammonia-salt process can operate at near-ambient conditions to greatly reduce the pressure/temperature severity necessary for conducting effective ammonia-based pretreatments on lignocellulose.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Development of a coarse-grained molecular dynamics model for poly(dimethyl- co -diphenyl)siloxane

Polydimethylsiloxane is an important polymeric material with a wide range of applications. However, environmental effects like low temperature can induce crystallization in this material with resulting changes in its structural and dynamic properties. The incorporation of phenyl-siloxane components, e.g., as in a poly(dimethyl-co-diphenyl)siloxane random copolymer, is known to suppress such crystallization. Molecular dynamics (MD) simulations can be a powerful tool to understand such effects in atomistic detail. Unfortunately, all-atomistic molecular dynamics (AAMD) is limited in both spatial dimensions and simulation times it can probe. Here, to overcome such constraints and to extend to more useful length- and time-scales, we systematically develop a coarse-grained molecular dynamics (CGMD) model for the poly(dimethyl-co-diphenyl)siloxane system with bonded and non-bonded interactions determined from all-atomistic simulations by the iterative Boltzmann inversion (IBI) method. Additionally, we propose a lever rule that can be useful to generate non-bonded potentials for such systems without reference to the all-atomistic ground truth. Our model captures the structural and dynamic properties of the copolymer material with quantitative accuracy and is useful to study long-time dynamics of highly-entangled systems, sequence-dependent properties, phase behaviour, etc.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Bayesian force fields from active learning for simulation of inter-dimensional transformation of stanene

Abstract We present a way to dramatically accelerate Gaussian process models for interatomic force fields based on many-body kernels by mapping both forces and uncertainties onto functions of low-dimensional features. This allows for automated active learning of models combining near-quantum accuracy, built-in uncertainty, and constant cost of evaluation that is comparable to classical analytical models, capable of simulating millions of atoms. Using this approach, we perform large-scale molecular dynamics simulations of the stability of the stanene monolayer. We discover an unusual phase transformation mechanism of 2D stanene, where ripples lead to nucleation of bilayer defects, densification into a disordered multilayer structure, followed by formation of bulk liquid at high temperature or nucleation and growth of the 3D bcc crystal at low temperature. The presented method opens possibilities for rapid development of fast accurate uncertainty-aware models for simulating long-time large-scale dynamics of complex materials.

Chemistry↗
DOE OSTI2021

Demonstration of an x-ray Raman spectroscopy setup to study warm dense carbon at the high energy density instrument of European XFEL

We present a proof-of-principle study demonstrating x-ray Raman Spectroscopy (XRS) from carbon samples at ambient conditions in conjunction with other common diagnostics to study warm dense matter, performed at the high energy density scientific instrument of the European x-ray Free Electron Laser (European XFEL). We obtain sufficient spectral resolution to identify the local structure and chemical bonding of diamond and graphite samples, using highly annealed pyrolytic graphite spectrometers. Due to the high crystal reflectivity and XFEL brightness, we obtain signal strengths that will enable accurate XRS measurements in upcoming pump–probe experiments with a high repetition-rate, where the samples will be pumped with high-power lasers. Molecular dynamics simulations based on density functional theory together with XRS simulations demonstrate the potential of this technique and show predictions for high-energy-density conditions. Our setup allows simultaneous implementation of several different diagnostic methods to reduce ambiguities in the analysis of the experimental results, which, for warm dense matter, often relies on simplifying model assumptions. The promising capabilities demonstrated here provide unprecedented insights into chemical and structural dynamics in warm dense matter states of light elements, including conditions similar to the interiors of planets, low-mass stars, and other celestial bodies.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Near-continuum, hypersonic oxygen flow over a double cone simulated by direct simulation Monte Carlo informed from quantum chemistry

A large-scale, fully resolved direct simulation Monte Carlo (DSMC) computation of a non-equilibrium, reactive flow of pure oxygen over a double cone is presented. Under the simulated near-continuum conditions, the computational demands are shown to be significant because of the wide range of length scales that must be resolved. Therefore, robust grid adaption capabilities and efficient parallelization of the Stochastic PArallel Rarefied-gas Time-accurate Analyzer (SPARTA) code that is utilized in this work are essential. The thermochemical and transport collision models were selected for efficiency and simplicity. First-principles data, obtained from the highly accurate direct molecular simulation method, were used to inform the collision models’ parameters. Importantly, because SPARTA implements molecular collision models using collision-specific energies, the resulting macroscopic relaxation rates were evaluated a posteriori via zero-dimensional heat bath simulations. The comparisons of surface properties, namely heat flux and pressure, show very close agreement with previous computational fluid dynamics (CFD) results. Differences with the measurements were found to be similar to the CFD simulations. The unresolved discrepancy with the measurements could be due to inconsistent free stream conditions with the actual experimental data or missing physical phenomena altogether, for example atomic and molecular oxygen electronically excited states, three-dimensional effects, or more complex gas–surface interactions. As shown in this work, the advantages of obtaining a DSMC particle solution for these flows reside in the method's ability to be directly informed from first principles and to seamlessly describe internal energy non-equilibrium for all modes. With the advent of exascale computing and beyond, particle methods will be an increasingly important tool to verify the validity of physical assumptions in reduced-order models via fully resolved, experimental-scale simulations, down to the level of molecular-level distributions.

Mechanics↗
DOE OSTI2021

Ab initio electron dynamics in high electric fields: Accurate prediction of velocity-field curves

Electron dynamics in external electric fields governs the behavior of solid-state electronic devices. First-principles calculations enable precise predictions of charge transport in low electric fields. However, studies of high-field electron dynamics remain elusive due to a lack of accurate and broadly applicable methods. In this work, we develop an efficient approach to solve the real-time Boltzmann transport equation with both the electric field term and ab initio electron-phonon collisions. These simulations provide field-dependent electronic distributions in the time domain, allowing us to investigate both transient and steady-state transport in electric fields ranging from low to high (> 10 kV/cm). The broad capabilities of our approach are shown by computing nonequilibrium electron occupations and velocity-field curves in Si, GaAs, and graphene, obtaining results in quantitative agreement with experiment. Our approach sheds light on microscopic details of transport in high electric fields, including the dominant scattering mechanisms and valley occupation dynamics. Our results demonstrate quantitatively accurate calculations of electron dynamics in low to high electric fields, with broad application to power and micro-electronics, optoelectronics, and sensing.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Computational Prediction of Water Sorption in Facilitated Transport Membranes

Polymeric facilitated transport membranes (FTMs) have emerged as an innovative class of promising carbon capture technology. Studies have shown that the transport properties of an FTM are significantly influenced by its water uptake. In order to better quantify FTM performance, we herein explore the potential of computational techniques to predict the equilibrium water uptake values of FTMs. Two prediction approaches were examined. First, the water sorption was explicitly simulated by iteratively conducting grand canonical Monte Carlo (GCMC) and molecular dynamics (MD) simulations. Second, the water sorption was predicted based on the chemical potential of the adsorbed water, which was calculated using the Widom insertion or continuous fractional component Monte Carlo (CFCMC) method. The chemical potential-based approach with CFCMC demonstrated good prediction of the equilibrium water uptake values of FTMs with poly(N-vinylformamide-co-vinylamine) as fixed-site carrier and 2-(1-piperazinyl)ethylamine sarcosinate as mobile carrier. The predicted water uptake values increased with increasing mobile carrier content and were in good agreement with the experimental values. Furthermore, the higher water uptake promoted the diffusion of CO 2 , N 2 , and mobile carrier as well as slightly stifled the sorption of N 2 . Such an approach significantly contributes to a more comprehensive theoretical evaluation of FTMs.

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

Local structure of zinc–indium–tin oxide films via grazing-incidence x-ray pair-distribution functions and theoretical methods

A detailed experimental and theoretical study on the local (r ≤ 4.5 Å) atomic structure of amorphous and crystalline zinc–indium–tin oxide (ZITO) thin films using grazing-incidence x-ray Pair-Distribution Functions (PDFs), ab initio Molecular Dynamics (MD), and Empirical Potential Structure Refinement (EPSR) Monte Carlo simulations is presented. High-energy synchrotron x rays, a two-dimensional detector, and different incident angles were used to probe the depth uniformity of five (ZnO) 0.15 (In 2 O 3 ) 0.70 (SnO 2 ) 0.15 films that were deposited via pulsed-laser deposition at growth temperatures (T G ) ranging from 25 to 300 °C. Films deposited at T G ≤ 150 °C were amorphous. The partially crystalline (T G = 200 °C) and fully crystalline (T G = 300 °C) films were highly textured. Both crystalline and amorphous structures were investigated using ab initio MD and EPSR Monte Carlo simulations. The density of the amorphous films determined from the experimental data agreed with MD calculations. Coordination numbers, bond lengths, and distortion for metal–oxygen and for both the edge- and corner-shared In–metal shells up to 4.5 Å obtained from PDF analysis closely agreed with MD and EPSR simulations. There is a pronounced decrease in the edge- and corner-shared In–Zn distances arising from the shorter Zn–O bond length, Zn–O tetrahedral coordination, and In–O–Zn angle in amorphous ZITO compared to its crystalline counterpart. A maximum in electrical mobility was observed for the amorphous film just before crystallization occurred. While the peak is broad, consistent with nearly unchanged overall cation–oxygen coordination in the amorphous films, ESPR results indicate that the tetrahedral coordination follows the conductivity trend.

Grazing Incidence X-ray↗
DOE OSTI2025

Thermal disorder and phonon softening in the ferroelectric phase transition of lead titanate

We report a molecular dynamics study of ab initio quality of the ferroelectric phase transition in crystalline PbTi⁢O3. We model anharmonicity accurately in terms of potential energy and polarization surfaces trained on density functional theory data with modern machine learning techniques. Our simulations demonstrate that the transition has a strong order-disorder character, in agreement with diffraction experiments, and provide fresh insight into the approach to equilibrium across the phase transition. We find that the emergence and disappearance of the macroscopic polarization is driven by dipolar switching at the nanometer scale. We also computed the infrared optical absorption spectra in both the ferroelectric and the paraelectric phases, finding good agreement with the experimental Raman frequencies. Often, the almost ideal displacive character of the soft mode detected by Raman scattering in the paraelectric phase has been contrasted with the order-disorder character of the transition suggested by diffraction experiments. We settle this issue by showing that the soft mode coexists with a strong Debye relaxation associated with thermal disordering of the dipoles. The Debye relaxation feature is centered at zero frequency and appears near the transition temperature in both the ferroelectric and the paraelectric phases.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Vanishing dynamic strength measured in a transient high-pressure phase of tin

This work reports two independent experimental estimations of the dynamic strength of the body-centered tetragonal (bct) 𝛾 phase of tin, which occurs at pressures above about 9 GPa and transforms promptly back to the ambient 𝛽 phase upon pressure release. Measuring strength in such a transient high-pressure phase is challenging. One measurement used free-surface Richtmyer-Meshkov instabilities generated with gas gun impact. Another set of measurements used ramp-release loading in a pulsed-power facility. Strength estimations came from comparison to simulations using a comprehensive multiphase modeling framework that includes equation of state, shear moduli, and strength separately for each phase, and treats mixed-phase regions. Both experimental methods found the 𝛾-phase strength to be very low, within experimental uncertainty of zero. The existing literature on other materials, by contrast, almost universally reports higher strength in transient high-pressure phases compared with ambient phases. Recent Molecular-Dynamics simulations on tin in the literature showed almost zero deviatoric strength during a deformation-induced bct → bct transformation in which one of the 𝛾-phase 𝑎 axes “flips” to the 𝑐 axis. This reorientation currently provides the most plausible explanation for the low observed strength in 𝛾-phase tin.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Solid-state NMR of unlabeled plant cell walls: high-resolution structural analysis without isotopic enrichment

Abstract Background Multidimensional solid-state nuclear magnetic resonance (ssNMR) spectroscopy has emerged as an indispensable technique for resolving polymer structure and intermolecular packing in primary and secondary plant cell walls. Isotope ( 13 C) enrichment provides feasible sensitivity for measuring 2D/3D correlation spectra, but this time-consuming procedure and its associated expenses have restricted the application of ssNMR in lignocellulose analysis. Results Here, we present a method that relies on the sensitivity-enhancing technique Dynamic Nuclear Polarization (DNP) to eliminate the need for 13 C-labeling. With a 26-fold sensitivity enhancement, a series of 2D 13 C– 13 C correlation spectra were successfully collected using the unlabeled stems of wild-type Oryza sativa (rice). The atomic resolution allows us to observe a large number of intramolecular cross peaks for fully revealing the polymorphic structure of cellulose and xylan. NMR relaxation and dipolar order parameters further suggest a sophisticated change of molecular motions in a ctl1 ctl2 double mutant: both cellulose and xylan have become more dynamic on the nanosecond and microsecond timescale, but the motional amplitudes are uniformly small for both polysaccharides. Conclusions By skipping isotopic labeling, the DNP strategy demonstrated here is universally extendable to all lignocellulose materials. This time-efficient method has landed the technical foundation for understanding polysaccharide structure and cell wall assembly in a large variety of plant tissues and species.

09 BIOMASS FUELS↗
DOE OSTI2024

XFEL SASE pulses can enhance time-dependent observables

Abstract X-ray free electron lasers (XFELs) have emerged as powerful sources of short and intense x-ray pulses. We propose a simple and robust procedure which takes advantage of the inherent stochasticity of self-amplified stimulated emission (SASE) pulses to enhance the time-resolution and signal strength of the recorded data. Notably, the proposed method is able to enhance the average signal without knowledge of the signal strength of individual shots. Simple metrics for the probe pulses are introduced, such as an effective pulse duration applicable to SASE pulses characterised in the time domain using e.g. an X-band transverse cavity. The approach is evaluated using simulated and real pulse data in the context of ultrafast electron dynamics in a molecule. Utilising H 2 as a model system, we demonstrate the efficacy of the method theoretically, successfully enhancing the predicted nonresonant ultrafast x-ray scattering signal associated with electron dynamics. The method presented is broadly applicable and offers a general strategy for enhancing time-dependent observables at XFELs.

data analysis↗
DOE OSTI2023

Reinforcement learning based hybrid bond-order coarse-grained interatomic potentials for exploring mesoscale aggregation in liquid–liquid mixtures

Exploring mesoscopic physical phenomena has always been a challenge for brute-force all-atom molecular dynamics simulations. Although recent advances in computing hardware have improved the accessible length scales, reaching mesoscopic timescales is still a significant bottleneck. Coarse-graining of all-atom models allows robust investigation of mesoscale physics with a reduced spatial and temporal resolution but preserves desired structural features of molecules, unlike continuum-based methods. Here, we present a hybrid bond-order coarse-grained forcefield (HyCG) for modeling mesoscale aggregation phenomena in liquid–liquid mixtures. The intuitive hybrid functional form of the potential offers interpretability to our model, unlike many machine learning based interatomic potentials. We parameterize the potential with the continuous action Monte Carlo Tree Search (cMCTS) algorithm, a reinforcement learning (RL) based global optimizing scheme, using training data from all-atom simulations. The resulting RL-HyCG correctly describes mesoscale critical fluctuations in binary liquid–liquid extraction systems. cMCTS, the RL algorithm, accurately captures the mean behavior of various geometrical properties of the molecule of interest, which were excluded from the training set. The developed potential model along with the RL-based training workflow could be applied to explore a variety of other mesoscale physical phenomena that are typically inaccessible to all-atom molecular dynamics simulations.

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

First Principles Nonadiabatic Excited-State Molecular Dynamics in NWChem

Computational simulation of non-adiabatic molecular dynamics is an indispensable tool for understanding complex photoinduced processes such as internal conversion, energy transfer, charge separation, and spatial localization of excitons, to name a few. Here, we report an implementation of the fewest-switches surface hopping algorithm in the NWChem computational chemistry program. Here the surface hopping method is combined with linear-response time-dependent density functional theory calculations of adiabatic excited state potential energy surfaces. To treat quantum transitions between arbitrary electronic Born{Oppenheimer states, we have implemented both numerical and analytical differentiation schemes for derivative non-adiabatic couplings. A numerical approach for the time-derivative non-adiabatic couplings together with an analytical method for calculating non-adiabatic coupling vectors is an efficient combination for surface hopping approaches. Additionally, electronic decoherence schemes and a state reassigned unavoided crossings algorithm are also implemented to improve the accuracy of the simulated dynamics and to handle trivial unavoided crossings. We apply our code to study the ultrafast decay of photoexcited benzene, including a detailed analysis of the potential energy surface, population decay time scales, and vibrational coordinates coupled to the excitation dynamics. The development presented in this work is a baseline for future implementations of more sophisticated frameworks for simulating of non-adiabatic molecular dynamics in NWChem.

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

Harness the power of atomistic modeling and deep learning in biofuel separation

Biofuels offer a remarkable, sustainable energy source for a future of clean energy. The development of efficient biofuel separation plays a crucial role in achieving cost-effective utilization of biofuel. In this chapter, we provide an overview of the recent advancements in atomistic-level modeling and deep learning in the rational design of novel, efficient biofuel separation. The fundamental principles of quantum and statistical mechanics are covered in appropriate detail to highlight their underlying differences in theory. The methodologies of several molecular representations and deep learning algorithms applicable to biofuel separation are briefly demonstrated as well. The applications, successes, and risks of employing density functional theory, ab initio molecular dynamics, classical molecular dynamics, and deep learning are provided to showcase their recent accomplishments in biofuel separation as well as potential improvements in both methodology and application. Lastly, a vision for the future growth of these methods is illustrated.

deep learning, artificial intelligence, biofuels, ↗
DOE OSTI2022

Accelerators for Classical Molecular Dynamics Simulations of Biomolecules

Atomistic Molecular Dynamics (MD) simulations provide researchers the ability to model biomolecular structures such as proteins and their interactions with drug-like small molecules with greater spatiotemporal resolution than is otherwise possible using experimental methods. MD simulations are notoriously expensive computational endeavors that have traditionally required massive investment in specialized hardware to access biologically relevant spatiotemporal scales. Our goal is to summarize the fundamental algorithms that are employed in the literature to then highlight the challenges that have affected accelerator implementations in practice. We consider three broad categories of accelerators: Graphics Processing Units (GPUs), Field-Programmable Gate Arrays (FPGAs), and Application Specific Integrated Circuits (ASICs). These categories are comparatively studied to facilitate discussion of their relative trade-offs and to gain context for the current state of the art. We conclude by providing insights into the potential of emerging hardware platforms and algorithms for MD.

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

Machine learning force field model for kinetic Monte Carlo simulations of itinerant Ising magnets

Here, we present a scalable machine learning (ML) framework for large-scale kinetic Monte Carlo (kMC) simulations of itinerant electron Ising systems. As the effective interactions between Ising spins in such itinerant magnets are mediated by conducting electrons, the calculation of energy change due to a local spin update requires solving an electronic structure problem. Such repeated electronic structure calculations could be overwhelmingly prohibitive for large systems. Assuming the locality principle, a convolutional neural network (CNN) model is developed to directly predict the effective local field and the corresponding energy change associated with a given spin update based on Ising configuration in a finite neighborhood. As the kernel size of the CNN is fixed at a constant, the model can be directly scalable to kMC simulations of large lattices. Our approach is reminiscent of the ML force field models widely used in first-principles molecular dynamics simulations. Applying our ML framework to a square-lattice double-exchange Ising model, we uncover unusual coarsening of ferromagnetic domains at low temperatures. Our work highlights the potential of ML methods for large-scale modeling of similar itinerant systems with discrete dynamical variables.

machine learning↗