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Results for “Reactive force field”

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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Surface structure of linear nanopores in amorphous silica: Comparison of properties for different pore generation algorithms

In this work, we compare the surface structure of linear nanopores in amorphous silica (a-SiO 2 ) for different versions of “pore drilling” algorithms (where the pores are generated by the removal of atoms from the preformed bulk a-SiO 2 ) and for “cylindrical resist” algorithms (where a-SiO 2 is formed around a cylindrical exclusion region). After adding H to non-bridging O, the former often results in a moderate to high density of surface silanol groups, whereas the latter produces a low density. The silanol surface density for pore drilling can be lowered by a final dehydroxylation step, and that for the cylindrical resist approach can be increased by a final hydroxylation step. In this respect, the two classes of algorithms are complementary. We focus on the characterization of the chemical structure of the pore surface, decomposing the total silanol density into components corresponding to isolated and vicinal mono silanols and geminal silanols. The final dehyroxylation and hydroxylation steps can also be tuned to better align some of these populations with the target experimental values.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

First principles reactive simulation for equation of state prediction

The high cost of density functional theory (DFT) has hitherto limited the ab initio prediction of the equation of state (EOS). In this article, we employ a combination of large scale computing, advanced simulation techniques, and smart data science strategies to provide an unprecedented ab initio performance analysis of the high explosive pentaerythritol tetranitrate (PETN). Comparison to both experiment and thermochemical predictions reveals important quantitative limitations of DFT for EOS prediction and thus the assessment of high explosives. In particular, we find that DFT predicts the energy of PETN detonation products to be systematically too high relative to the unreacted neat crystalline material, resulting in an underprediction of the detonation velocity, pressure, and temperature at the Chapman–Jouguet state. The energetic bias can be partially accounted for by high-level electronic structure calculations of the product molecules. Furthermore we demonstrate a modeling strategy for mapping chemical composition across a wide parameter space with limited numerical data, the results of which suggest additional molecular species to consider in thermochemical modeling.

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

Can a deep-learning model make fast predictions of vacancy formation in diverse materials?

The presence of point defects, such as vacancies, plays an important role in materials design. Here, we explore the extrapolative power of a graph neural network (GNN) to predict vacancy formation energies. We show that a model trained only on perfect materials can also be used to predict vacancy formation energies (E vac ) of defect structures without the need for additional training data. Such GNN-based predictions are considerably faster than density functional theory (DFT) calculations and show potential as a quick pre-screening tool for defect systems. To test this strategy, we developed a DFT dataset of 530 E vac consisting of 3D elemental solids, alloys, oxides, semiconductors, and 2D monolayer materials. We analyzed and discussed the applicability of such direct and fast predictions. We applied the model to predict 192 494 E vac for 55 723 materials in the JARVIS-DFT database. Our work demonstrates how a GNN-model performs on unseen data.

2D materials↗
DOE OSTI2023

Detailed studies of the processes in low energy H irradiation of Li and Li-compound surfaces

We have used a combination of pico-to-nano temporal/spatial scale computational physics and chemistry modeling of plasma–material interfaces in the tokamak fusion plasma edges to unravel the evolving characteristics, not readily accessible by empirical means, of lithium-, oxygen-, and hydrogen-containing materials of plasma-facing components under irradiation by hydrogen and its isotopes. In the present calculation, amorphous lithium compound surfaces containing oxygen, Li 2 O, and LiOH were irradiated by 1-100 eV particles at incident angles on the surface ranging from perpendicular to almost grazing angles. Consequential surface processes, reflection, retention, and sputtering were studied at “the same footing” and compared to earlier results from amorphous Li and LiH surfaces. In conclusion, the critical role of charging dynamics of lithium, oxygen, and hydrogen atoms in the surface chemistry during hydrogen-fuel irradiation was found to drive the kinetics and dynamics of these surfaces in unexpected ways that ultimately could have profound effects on fusion plasma confinement behavior and surface erosion.

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

Chemical evolution in nitrogen shocked beyond the molecular stability limit

Evolution of nitrogen under shock compression up to 100 GPa is revisited via molecular dynamics simulations using a machine-learned interatomic potential. The model is shown to be capable of recovering the structure, dynamics, speciation, and kinetics in hot compressed liquid nitrogen predicted by first-principles molecular dynamics, as well as the measured principal shock Hugoniot and double shock experimental data, albeit without shock cooling. Our results indicate that a purely molecular dissociation description of nitrogen chemistry under shock compression provides an incomplete picture and that short oligomers form in non-negligible quantities. Finally, this suggests that classical models representing the shock dissociation of nitrogen as a transition to an atomic fluid need to be revised to include reversible polymerization effects.

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

Accuracy, transferability, and computational efficiency of interatomic potentials for simulations of carbon under extreme conditions

Large-scale atomistic molecular dynamics (MD) simulations provide an exceptional opportunity to advance the fundamental understanding of carbon under extreme conditions of high pressures and temperatures. However, the fidelity of these simulations depends heavily on the accuracy of classical interatomic potentials governing the dynamics of many-atom systems. Here, this study critically assesses several popular empirical potentials for carbon, as well as machine learning interatomic potentials (MLIPs), in their ability to simulate a range of physical properties at high pressures and temperatures, including the diamond equation of state, its melting line, shock Hugoniot, uniaxial compressions, and the structure of liquid carbon. Empirical potentials fail to accurately predict the behavior of carbon under high pressure–temperature conditions. In contrast, MLIPs demonstrate quantum accuracy, with Spectral Neighbor Analysis Potential (SNAP) and atomic cluster expansion (ACE) being the most accurate in reproducing the density functional theory results. ACE displays remarkable transferability despite not being specifically trained for extreme conditions. Furthermore, ACE and SNAP exhibit superior computational performance on graphics processing unit-based systems in billion atom MD simulations, with SNAP emerging as the fastest. In addition to offering practical guidance in selecting an interatomic potential with a fine balance of accuracy, transferability, and computational efficiency, this work also highlights transformative opportunities for groundbreaking scientific discoveries facilitated by quantum-accurate MD simulations with MLIPs on emerging exascale supercomputers.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Dynamics of iodine geminate recombination in supercritical xenon solvent: Caging effect

Understanding the dynamics of chemical reactions in solutions is vital, as their rates and kinetics are significantly affected by the solvent environment. Supercritical solvents offer extensive applications in chemical reactions by enabling the manipulation of the solution environment. Here, in this study, we investigate the geminate recombination of iodine in a supercritical xenon solvent by using ReaxFF-based molecular dynamics simulations. Our findings reveal that the highest iodine recombination rate occurs near supercritical conditions, while lower-pressure conditions lead to reduced collision rates and unstable recombination, and higher-pressure conditions hinder iodine diffusion, resulting in a lower recombination rate. Our analysis shows that the xenon local density at the time of recombination is at least 2.5 times higher than the global density, confirming the presence of xenon clusters surrounding the Iodine atoms. This observation is further supported by coordination number analysis, which confirms an elevated xenon local density during recombination. In addition, the correlation between the total energy of xenon atoms within a cluster and recombined iodine atoms underscores the kinetic energy transfer process, validating the occurrence of geminate recombination. The excess kinetic energy from the recombining iodine atoms is transferred to the surrounding xenon atoms. Our examination of geminate recombination demonstrates that iodine atoms confined within xenon clusters—whether through manual insertion of atoms or the fast dissociation of an iodine molecule within xenon clusters—are more likely to recombine as primary geminate recombination. However, extending the iodine molecule dissociation time allows iodine atoms to diffuse out of the cluster, and the recombination to shift toward secondary geminate recombination.

Cage effect↗
DOE OSTI2025

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.↗
DOE OSTI2021

Molecular dynamics of the early stages of high-temperature corrosion

In this work, we performed molecular dynamics simulations of the high-temperature oxidation of metal alloys composed of Al, Cr, and Fe and compared their behavior with that of pure Fe. The metal alloy elements (Al and Cr) segregated to the surface during oxidation, producing a lower stress gradient at the metal/environment interface compared to pure Fe. We have found that the lowered stress gradients produced in the alloy material appear to play a key role in the development of corrosion. Interfaces with lower stress gradients have reduced rates of H 2 O adsorption, especially for the ferritic (bcc) alloys. The diffusivity of oxygen and hydrogen drops more rapidly for the interfaces with reduced stress gradients. The stress gradient is also diminished when the gas pressure is increased, indicating that the Fe-Cr-Al alloy system is more resistant to oxidation than pure Fe at higher pressures. Therefore, we conclude that the lower stress gradients at the alloy/environment interface reduce the stress concentration and can slow down the rate of the initial oxide scale growth. We also compared bcc and fcc alloys with pure Fe based on our three evaluation criteria (peak stress, stress gradient, and summation of stress in the oxide scale). We found that the alloys have lower values under the three criteria compared to pure Fe. The bcc alloy has the best score under a water rich environment and the fcc alloy is proven to be better for peak stress and summation of stress in the oxide scale under an oxygen rich environment. For surface segregation to occur, we find that a minimum content of Al or Cr in the near-surface region must be achieved. We also learned that the role of Al is more important than that of Cr in terms of corrosion resistant behavior at relatively higher temperatures for the Fe-Cr-Al ternary alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Deep potential molecular dynamics simulations of ion-enhanced etching of silicon by atomic chlorine

The continued development of plasma-assisted processing techniques requires a fundamental understanding of plasma-surface interactions. Molecular dynamics (MD) simulations have been employed to complement experimental studies and better understand the properties of such systems. Recently, machine learning (ML) methods have enabled the development of ab initio-based interatomic potentials, which can be generalized to complex combinations of multiple atom types. In this work, we use ML potentials developed using the Deep Potential Molecular Dynamics (DeepMD) framework to provide a model of ion-enhanced etching of Si by Cl atoms. We demonstrate the importance of proper selection of the training data set to the accuracy of the DeepMD model and compare our results to MD results using empirical potentials, as well as to experimental measurements. Exposure of undoped Si at 300 K to thermal Cl atoms yields a steady-state Cl coverage of 1.25 monolayers, which is slightly lower than the value obtained in previous experimental studies. Predictions of Si etch yields by simultaneous Cl atom and Ar + ion impacts as a function of ion energy, neutral to ion flux ratio, and angle of incidence of the ions are in reasonably good agreement with classical MD results and experimental measurements. Finally, etch yields and SiCl x mixed layer thicknesses during simultaneous bombardment of the Si(100) surface by Cl atoms and Cl + ions are in good agreement with experimental data. In conclusion, the present work is a necessary condition for the extension of the DeepMD procedure to more complex systems of interest in plasma-surface interactions.

Artificial neural networks↗
DOE OSTI2024

Computational Advances in Ionic Liquid Applications for Green Chemistry: A Critical Review of Lignin Processing and Machine Learning Approaches

The valorization and dissolution of lignin using ionic liquids (ILs) is critical for developing sustainable biorefineries and a circular bioeconomy. This review aims to critically assess the current state of computational and machine learning methods for understanding and optimizing IL-based lignin dissolution and valorization processes reported since 2022. The paper examines various computational approaches, from quantum chemistry to machine learning, highlighting their strengths, limitations, and recent advances in predicting and optimizing lignin-IL interactions. Key themes include the challenges in accurately modeling lignin’s complex structure, the development of efficient screening methodologies for ionic liquids to enhance lignin dissolution and valorization processes, and the integration of machine learning with quantum calculations. These computational advances will drive progress in IL-based lignin valorization by providing deeper molecular-level insights and facilitating the rapid screening of novel IL-lignin systems.

09 BIOMASS FUELS↗
DOE OSTI2022

Energy, angle, and temperature dependencies of the sticking of D atoms on Li surfaces

Detailed experimental and computational information on the response of lithium surfaces to irradiation by slow hydrogenic particles (ions, atoms, molecules) is sparse and mainly speculative. In this work, we present a computational study of the reflection and retention of deuterium (D) atoms at crystalline and amorphous lithium surfaces at 300 and 500 K, where the D atoms have an impact energy in the range of 0.025–5 eV and incident angles of 0° (perpendicular incidence) or 85° (near-grazing incidence). Classical molecular dynamics simulations are performed with the reactive bond-order force field (ReaxFF) potentials. This study provides quantitative information on the deuterium sticking probability and recycling coefficient for lithium surfaces. Further, our results support the ongoing work at the Lithium Tokamak eXperiment-β fusion experiment as well as relevant experiments in the laboratory setting.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2021

Semi-Automated Creation of Density Functional Tight Binding Models through Leveraging Chebyshev Polynomial-Based Force Fields

Density functional tight binding (DFTB) is an attractive method for accelerated quantum simulations of condensed matter due to its enhanced computational efficiency over standard density functional theory (DFT) approaches. However, DFTB models can be challenging to determine for individual systems of interest, especially for metallic and interfacial systems where different bonding arrangements can lead to significant changes in electronic states. In this regard, we have created a rapid-screening approach for determining systematically improvable DFTB interaction potentials that can yield transferable models for a variety of conditions. Our method leverages a recent reactive molecular dynamics force field where many-body interactions are represented by linear combinations of Chebyshev polynomials. This allows for the efficient creation of multi-center representations with relative ease, requiring only a small investment in initial DFT calculations. Here, we have focused our workflow on TiH 2 as a model system and show that a relatively small training set based on unit-cell-sized calculations yields a model accurate for both bulk and surface properties. Our approach is easy to implement and can yield reliable DFTB models over a broad range of thermodynamic conditions, where physical and chemical properties can be difficult to interrogate directly and there is historically a significant reliance on theoretical approaches for interpretation and validation of experimental results.

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

Processes at lithium-hydride/deuteride surfaces upon low energy impact of H/D

Sputtering, reflection, and retention processes at amorphous and crystalline lithium hydride surfaces due to impact of low energy (1–100 eV) hydrogen and deuterium atoms over the range of 0 o −85 o angle of incidence at 300 K surface temperature were investigated by atomistic computational methods. Classical molecular dynamics simulations were performed with improved reactive bond-order force field (ReaxFF) potentials that include long-range polarization effects. In addition to probabilities of surface processes, the energy and angular spectra of ejected particles were obtained. Comparison of these results with those previously obtained on pristine lithium surfaces indicates the importance of saturation of the Li surface and near-surface region with hydrogen. We show that such saturation, which is typical in both laboratory and fusion device experiments with lithium coating of the plasma-facing surfaces, significantly changes the surface processes with hydrogen irradiation in the understudied low-energy region of impact energies.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2022

Quantum machine learning for chemistry and physics

Machine learning (ML) has emerged as a formidable force for identifying hidden but pertinent patterns within a given data set with the objective of subsequent generation of automated predictive behavior. In recent years, it is safe to conclude that ML and its close cousin, deep learning (DL), have ushered in unprecedented developments in all areas of physical sciences, especially chemistry. Not only classical variants of ML, even those trainable on near-term quantum hardwares have been developed with promising outcomes. Such algorithms have revolutionized materials design and performance of photovoltaics, electronic structure calculations of ground and excited states of correlated matter, computation of force-fields and potential energy surfaces informing chemical reaction dynamics, reactivity inspired rational strategies of drug designing and even classification of phases of matter with accurate identification of emergent criticality. In this review we shall explicate a subset of such topics and delineate the contributions made by both classical and quantum computing enhanced machine learning algorithms over the past few years. We shall not only present a brief overview of the well-known techniques but also highlight their learning strategies using statistical physical insight. The objective of the review is not only to foster exposition of the aforesaid techniques but also to empower and promote cross-pollination among future research in all areas of chemistry which can benefit from ML and in turn can potentially accelerate the growth of such algorithms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

AFM-IR and s-SNOM-IR measurements of chemically addressable monolayers on Au nanoparticles

The performance of catalysts depends on their nanoscale properties, and local variations in structure and composition can have a dramatic impact on the catalytic reactivity. Therefore, probing the localized reactivity of catalytic surfaces using high spatial resolution vibrational spectroscopy, such as infrared (IR) nanospectroscopy and tip-enhanced Raman spectroscopy, is essential for mapping their reactivity pattern. Two fundamentally different scanning probe IR nanospectroscopy techniques, namely, scattering-type scanning near-field optical microscopy (sSNOM) and atomic force microscopy-infrared spectroscopy (AFM-IR), provide the capabilities for mapping the reactivity pattern of catalytic surfaces with a spatial resolution of ~20 nm. Herein, we compare these two techniques with regard to their applicability for probing the vibrational signature of reactive molecules on catalytic nanoparticles. For this purpose, we use chemically addressable self-assembled molecules on Au nanoparticles as model systems. We identified significant spectral differences depending on the measurement technique, which originate from the fundamentally different working principles of the applied methods. While AFM-IR spectra provided information from all the molecules that were positioned underneath the tip, the s-SNOM spectra were more orientation-sensitive. Due to its field-enhancement factor, the s-SNOM spectra showed higher vibrational signals for dipoles that were perpendicularly oriented to the surface. The s-SNOM sensitivity to the molecular orientation influenced the amplitude, position, and signal-to-noise ratio of the collected spectra. Ensemble-based IR measurements verified that differences in the localized IR spectra stem from the enhanced sensitivity of s-SNOM measurements to the adsorption geometry of the probed molecules.

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

Data-Driven Transition Path Analysis Yields a Statistical Understanding of Sudden Stratospheric Warming Events in an Idealized Model

Abstract Atmospheric regime transitions are highly impactful as drivers of extreme weather events, but pose two formidable modeling challenges: predicting the next event (weather forecasting) and characterizing the statistics of events of a given severity (the risk climatology). Each event has a different duration and spatial structure, making it hard to define an objective “average event.” We argue here that transition path theory (TPT), a stochastic process framework, is an appropriate tool for the task. We demonstrate TPT’s capacities on a wave–mean flow model of sudden stratospheric warmings (SSWs) developed by Holton and Mass, which is idealized enough for transparent TPT analysis but complex enough to demonstrate computational scalability. Whereas a recent article (Finkel et al. 2021) studied near-term SSW predictability, the present article uses TPT to link predictability to long-term SSW frequency. This requires not only forecasting forward in time from an initial condition, but also backward in time to assess the probability of the initial conditions themselves. TPT enables one to condition the dynamics on the regime transition occurring, and thus visualize its physical drivers with a vector field called the reactive current . The reactive current shows that before an SSW, dissipation and stochastic forcing drive a slow decay of vortex strength at lower altitudes. The response of upper-level winds is late and sudden, occurring only after the transition is almost complete from a probabilistic point of view. This case study demonstrates that TPT quantities, visualized in a space of physically meaningful variables, can help one understand the dynamics of regime transitions.

Meteorology & Atmospheric Sciences↗