Search NASA⌕ Search

SEARCH · Search NASA

Results for “Density functional theory simulations”

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 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Microwave-induced selective decomposition of cellulose: Computational and experimental mechanistic study

Understanding microwave-material interactions will help facilitate the utilization of microwave technology in gasification and renewable energy production. In this study, cellulose was used as a model compound to simulate the organic matter in biomass, and its catalytic decomposition under a microwave (MW) field was studied to identify structural changes from the reaction. The study was conducted using a MW source coupled to a fixed-bed gas-flow reactor, mass spectroscopic, and Fourier transform infrared spectroscopy post-reaction analysis. Zeolite 13X was chosen as a microwave absorber to study the catalytic enhancement of the decomposition of cellulose. Density functional theory (DFT) was used to gain insights into the molecular transformations occurring in the presence of a static electric field, which was used to simulate the electric field component of the microwave electromagnetic radiation. Theoretical calculations demonstrated that both the positive and negative portion of the electric field interact with the permanent dipoles of the cellulose effecting the decomposition through the glycosidic bond breaking mechanism. The theoretical result was verified using infrared spectroscopic analysis of the pure cellulose during microwave heating. The theoretical calculations suggest only the positive electric field component may be active for the cellulose decomposition in the presence of Zeolite 13X. Physically mixing Zeolite 13X with the cellulose led to a significant enhancement in the decomposition rate of the glycosidic bond. Zeolite 13X enhanced the glycosidic O-C decomposition at lower MW power (lower temperatures), whereas the O-H functional group required higher MW power (higher temperature) for its decomposition. The DFT study coupled with the reaction studies revealed that the electric field polarizability is dependent upon both the direction and the orientation of the cellulose. Gas products revealed that applying 250 W of MW power led to the production of CO, H 2 , along with some CO 2 , CH 4 , and benzene at 305 °C. Finally, reaction under 500, 750, and 1000 W of power at constant temperature (305 °C) revealed that higher power led to the complete decomposition of cellulose to mostly CO, H 2 , and CO 2 .

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

Role of Dynamic Polarization Interactions in the Electrical Double Layer at Calcite (104) Interfaces with Aqueous Solutions

Reactions at mineral interfaces with aqueous solutions control many geochemical and biogeochemical processes in the Earth’s critical zone. At the molecular level, insights into important properties such as the structure of the electrical double layer (EDL) at specific mineral interfaces continue to improve due to the increasing fidelity of laboratory instrumentation and computational approaches. However, molecular simulation approaches suffer from limited reach into relevant scales of time, length, and system complexity. To span this gap, a novel hybrid approach that couples first principle plane-wave density functional theory (DFT) with classical DFT (cDFT) is demonstrated and applied to calcite (104) interfaces with various electrolytes. In this approach, a region of interest described using DFT interacts with the surrounding medium described using cDFT to arrive at a self-consistent ground state. Benchmarking against experimental observations and entirely first principle DFT simulations demonstrates that this hybrid model efficiently encompasses the key short-range and collective interactions in the EDL. Simulations of calcite (104)/solution interfaces reveal the key static and dynamic polarization interactions that give rise to structuring of ions and water. Ion hydration interactions have the strongest effect on the depth of the first minimum in the density distribution of counterions at the surface, and the position and width of the first density peak is largely determined by the strength of ion-correlation forces. Finer details of ion distributions are controlled by mutual polarization of the calcite surface and interfacial electrolyte. Finally, this new ability to efficiently and rigorously predict EDL structure at mineral surfaces in contact with complex solutions paves the way to accurately modeling sorption, nucleation, dissolution, and growth in realistic systems.

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

Ab Initio Molecular Dynamics Study of the Reduction of Acetone by the Hydrated Electron

We have investigated the reaction dynamics of the reduction of acetone by the solvated electron in water using ab initio molecular dynamics simulations at 298 and 373 K. The rate constants derived from the simulations are consistent with experimental observations that the reaction has a low activation energy. Detailed analyses carried out to shed light on the mechanism of the reaction show that solvent reorganization plays a key role as a reaction coordinate, as expected from the Marcus electron transfer theory. Furthermore, constrained density functional theory calculations indicate that the electronic coupling is large, placing the reaction in the adiabatic limit. Indeed, the activation energies and rate constants of the simulations are in accordance with the predictions of adiabatic Marcus theory.

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

Charge carrier nonadiabatic dynamics in non-metal doped graphitic carbon nitride

Graphitic carbon nitride (GCN) has attracted significant attention due to its excellent performance in photocatalytic applications. Non-metal doping of GCN has been widely used to improve the efficiency of the material as a photocatalyst. Using a combination of time-domain density functional theory with nonadiabatic molecular dynamics, we study the charge carrier dynamics in oxygen and boron doped GCN systems. The reported simulations provide a detailed time-domain mechanistic description of the charge separation and recombination processes that are of fundamental importance while evaluating the photovoltaic and photocatalytic performance of the material. The appearance of smaller energy gaps due to the presence of dopant states improves the visible light absorption range of the doped systems. At the same time, the nonradiative lifetimes are shortened in the doped systems as compared to the pristine GCN. In the case of boron doped at a carbon (B–C–GCN), the charge recombination time is very long as compared to the other two doped systems owing to the smaller electron–phonon coupling strength between the valence band maximum and the trap state. The results suggest B–C–GCN as the most suitable candidate among three doped systems studied in this work for applications in photocatalysis. Finally, this work sheds light into the influence of dopants on quantum dynamics processes that govern GCN performance and, thus, guides toward building high-performance devices in photocatalysis.

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

Electron and Electron-Nuclear Dynamics of Open Quantum Subsystems

Leveraging an open-subsystem formulation of Density Functional Theory (DFT) we aim at describing periodic and molecular systems alike, including their electronic and nuclear dynamics. Subsystem DFT enables first principles simulations to approach realistic time- and length-scales, and most importantly sheds light on the dynamical behavior of complex systems. Taking subsystem DFT to the time domain allows us to inspect the electron dynamics of condensed-phase systems in real time. In liquids and interfaces, we observe all the relevant regimes proper of non-Markovian open quantum system dynamics, such as electronic energy transfer, and screening. In addition, the ab-initio modeling of system-bath interactions brought us to observe and justify the holographic time-dependent electron density theorem. Contrary to interactions between molecular (finite) systems, when molecules interact with metal or semiconductor surfaces the electron dynamics is strongly non-Markovian with dramatic repercussions to the molecule’s response to external perturbations. Metals and semiconductors typically have large polarizabilities, and even in a regime of low coupling their effect on impinging molecular species is significant–line broadening, peak shift, and intensity borrowing are observed, characterized, and explained in terms of inter-subsystem dynamical interactions and a many-body decomposition of the system’s density-density response function in a way that transcends the canons of Fermi Golden Rule.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

$\mathrm{Perturbo}$: A software package for ab initio electron–phonon interactions, charge transport and ultrafast dynamics

We report Perturbo is a software package for first-principles calculations of charge transport and ultrafast carrier dynamics in materials. The current version focuses on electron–phonon interactions and can compute phonon-limited transport properties such as the conductivity, carrier mobility and Seebeck coefficient. It can also simulate the ultrafast nonequilibrium electron dynamics in the presence of electron–phonon scattering. Perturbo uses results from density functional theory and density functional perturbation theory calculations as input, and employs Wannier interpolation to reduce the computational cost. It supports norm-conserving and ultrasoft pseudopotentials, spin–orbit coupling, and polar electron–phonon interactions for bulk and 2D materials. Hybrid MPI plus OpenMP parallelization is implemented to enable efficient calculations on large systems (up to at least 50 atoms) using high-performance computing. Taken together, Perturbo provides efficient and broadly applicable ab initio tools to investigate electron–phonon interactions and carrier dynamics quantitatively in metals, semiconductors, insulators, and 2D materials.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Relative phase stability of L1 2 and DO 22 /DO 23 structures in Al 3 Nb, Al 3 Zr and Al 3 V compounds

The relative stability of the different tri-aluminide (Al 3 M) phases in three binary systems (M = Zr, Nb and V) was assessed for their potential to form fine cubic L1 2 precipitates in additively manufactured alloys. Supersaturated thin films of Al-(8–30) at% M were sputtered and heat treated during in-situ x-ray diffraction (XRD) measurements to observe the temperature ranges of stability for each phase. As-sputtered films were then processed with laser tracks simulating additive manufacturing solidification conditions, and the formation of phases in the laser tracks was correlated with density functional theory (DFT) and nucleation rate calculations. We found that the metastable L1 2 structure is highly competitive with the stable DO 23 structure in the Al-Zr system, but much less stable than the DO 22 structure in the Al-Nb system, and both the DO 22 and Al 8 V 5 structure in the Al-V system. Furthermore, these experimental results were found to be in good agreement with the DFT and kinetic calculations, as we determined that the metastable L1 2 in Al-Zr only requires a small amount of undercooling to favor its nucleation over the stable DO 23 , suggesting additive manufacturing can be a viable pathway to develop Al-Zr alloys strengthened by a high volume fraction of L1 2 Al 3 Zr phase.

Perrin, Alice E. [Oak Ridge National Laboratory (O↗
DOE OSTI2020

Self-assembly of solid nanoclusters in molybdenum under gas ion implantation

The self-assembly of defects in materials could create unique physical and chemical properties. In nuclear fuels, the formation of self-assembled fission products mitigates fission-gas-release and swelling. In this work, we report the formation of a solid nanocluster superlattice in molybdenum, an important constituent in metallic fuels, under krypton gas ion implantation. X-ray absorption near edge structure measurements coupled with density functional theory calculations show that the pressure of krypton nanoclusters in molybdenum is ~2 GPa and molecular dynamic simulations reveal that the krypton atoms are solid in the nanoclusters with a krypton-to-vacancy ratio of ~0.28 at 300 K.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Structure of chalcogen overlayers on Au(111): Density functional theory and lattice-gas modeling

Ordering of different chalcogens, S, Se, and Te, on Au(111) exhibit broad similarities but also some distinct features, which must reflect subtle differences in relative values of the long-range pair and many-body lateral interactions between adatoms. We develop lattice-gas (LG) models within a cluster expansion framework, which includes about 50 interaction parameters. These LG models are developed based on density functional theory (DFT) analysis of the energetics of key adlayer configurations in combination with the Monte Carlo (MC) simulation of the LG models to identify statistically relevant adlayer motifs, i.e., model development is based entirely on theoretical considerations. The MC simulation guides additional DFT analysis and iterative model refinement. Given their complexity, development of optimal models is also aided by strategies from supervised machine learning. The model for S successfully captures ordering motifs over a broader range of coverage than achieved by previous models, and models for Se and Te capture the features of ordering, which are distinct from those for S. More specifically, the modeling for all three chalcogens successfully explains the linear adatom rows (also subtle differences between them) observed at low coverages of ~0.1 monolayer. The model for S also leads to a new possible explanation for the experimentally observed phase with a (5 × 5)-type low energy electron diffraction (LEED) pattern at 0.28 ML and to predictions for LEED patterns that would be observed with Se and Te at this coverage.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1998

Modeling of Branched (L, T and Y) Carbon Nanotubes

Models for connecting two or three carbon nanotubes (CNT) using topological defects (i.e., pentagons and heptagons) are presented for the characterization of experimentally observed L, T and Y CNT junctions. The effects of the separation and orientation of the topological defects on the structures and energetics of these junctions are investigated using the nonlocal density function theory (DFT) and semi-empirical molecular orbital (AM1) calculations, and the Brenner empirical potential molecular mechanics simulations. The potential applications of L, Y and T CNT junctions in nanoelectronic devices are also discussed.

Han, Jie↗
DOE OSTI2021

Ab Initio modeling of Near-Edge EELS spectra for chemisorbed molecules

Abstract Electron energy loss spectroscopy (EELS) has recently been applied to probe chemisorbed molecules on metal nanostructures, but a fundamental understanding of the correlation between these spectra and the electronic structures of the adsorbates has been limited. We report here on the insights afforded by time-dependent density functional theory to decipher the energy loss near edge structure (ELNES) of EELS spectra associated with chemisorption. These first-principles calculations simulate the ELNES-EELS spectra for chemisorbed CO on various facets of Au and Pt. Computational predictions of key signatures such as the ‘red shift’ and reductions in the peak intensity of the 2 π * and 6 σ * peaks, as compared to free CO in the gas phase, are validated in comparison to experimentally collected EELS spectra. These signatures are revealed to arise from changes in the electronic structure in terms of unoccupied density of states associated with the chemisorption process. They are consistent with a Blyholder model that incorporates donation and back-donation of electrons. They are also characteristic of the chemisorption process, such as the choice of metal, site of adsorption and the coverage and distribution of adsorbates. Our simulations thus provide guidelines for the use of ELNES-EELS to characterize the atomic structure and adsorption property of nanostructured surfaces and facilitate the development of advanced nanomaterials for catalytic applications.

Materials Science↗
DOE OSTI2021

Structurally Driven Environmental Degradation of Friction in MoS2 Films

Abstract We report an investigation of the friction mechanisms of MoS 2 thin films under changing environments and contact conditions using a variety of computational and experimental techniques. Molecular dynamics simulations were used to study the effects of water and molecular oxygen on friction and bonding of MoS 2 lamellae during initial sliding. Characterization via photoelectron emission microscopy (PEEM) and Kelvin probe force microscopy (KPFM) were used to determine work function changes in shear modified material within the top few nanometers of MoS 2 wear scars. The work function was shown to change with contact conditions and environment, and shown by density functional theory (DFT) calculations and literature reports to be correlated with lamellae size and thickness of the basally oriented surface layer. Results from nanoscale simulations and macroscale experiments suggest that the evolution of the friction behavior of MoS 2 is linked primarily to the formation or inhibition of a basally oriented, molecularly thin surface film with long-range order.

Curry, John F.↗
DOE OSTI2022

A nearsighted force-training approach to systematically generate training data for the machine learning of large atomic structures

A challenge of atomistic machine-learning (ML) methods is ensuring that the training data are suitable for the system being simulated, which is particularly challenging for systems with large numbers of atoms. Most atomistic ML approaches rely on the nearsightedness principle (“all chemistry is local”), using information about the position of an atom’s neighbors to predict a per-atom energy. Here, in this work, we develop a framework that exploits the nearsighted nature of ML models to systematically produce an appropriate training set for large structures. We use a per-atom uncertainty estimate to identify the most uncertain atoms and extract chunks centered around these atoms. It is crucial that these small chunks are both large enough to satisfy the ML’s nearsighted principle (that is, filling the cutoff radius) and are large enough to be converged with respect to the electronic structure calculation. We present data indicating when the electronic structure calculations are converged with respect to the structure size, which fundamentally limits the accuracy of any nearsighted ML calculator. These new atomic chunks are calculated in electronic structures, and crucially, only a single force—that of the central atom—is added to the growing training set, preventing the noisy and irrelevant information from the piece’s boundary from interfering with ML training. The resulting ML potentials are robust, despite requiring single-point calculations on only small reference structures and never seeing large training structures. We demonstrated our approach via structure optimization of a 260-atom structure and extended the approach to clusters with up to 1415 atoms.

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

Meta-GGA exchange-correlation free energy density functional to increase the accuracy of warm dense matter simulations

We discuss strategies for thermalization of the ground-state meta-generalized gradient approximation (meta-GGA) exchange-correlation (XC) functionals. A simple but accurate scheme is implemented via universal additive thermal correction to XC using a perturbative-like self-consistent approach. The additive correction with explicit temperature dependence is applied to the ground-state deorbitalized, strongly constrained and appropriately normed (SCAN-L) meta-GGA XC leading to thermal XC functional denoted here as T-SCAN-L. Thermal T-SCAN-L meta-GGA functional shows significant improvement in density functional theory calculation accuracy for warm dense matter by a factor of 3 to 10, achieving unprecedented accuracy of total pressure between a few tenths and 1% when compared to traditional XC functionals, as demonstrated by the comparison to pathintegral Monte Carlo simulations for helium equation of state. Furthermore, the T-SCAN-L calculations of dc conductivity of warm dense aluminum also give better agreement with experiments over other XC functionals such as PBE and SCAN-L.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Probing DNA structural heterogeneity by identifying conformational subensembles of a bicovalently bound cyanine dye

DNA is a re-configurable, biological information-storage unit, and much remains to be learned about its heterogeneous structural dynamics. For example, while it is known that molecular dyes templated onto DNA exhibit increased photostability, the mechanism by which the structural dynamics of DNA affect the dye photophysics remains unknown. Here, we use femtosecond, two-dimensional electronic spectroscopy measurements of a cyanine dye, Cy5, to probe local conformations in samples of single-stranded DNA (ssDNA–Cy5), double-stranded DNA (dsDNA–Cy5), and Holliday junction DNA (HJ–DNA–Cy5). A line shape analysis of the 2D spectra reveals a strong excitation–emission correlation present in only the dsDNA–Cy5 complex, which is a signature of inhomogeneous broadening. Molecular dynamics simulations support the conclusion that this inhomogeneous broadening arises from a nearly degenerate conformer found only in the dsDNA–Cy5 complex. These insights will support future studies on DNA’s structural heterogeneity.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2026

Characterizing Defect Dynamics in Silicon Carbide Using Symmetry-Adapted Collective Variables and Machine Learning Interatomic Potentials

Silicon carbide (SiC) divacancies are attractive candidates for spin-defect qubits possessing long coherence times and optical addressability. The high activation barriers associated with SiC defect formation and motion pose challenges for their study by first-principles molecular dynamics. In this work, we develop and deploy machine learning interatomic potentials (MLIPs) to accelerate defect dynamics simulations while retaining ab initio accuracy. We employ an active learning strategy comprising symmetry-adapted collective variable discovery and enhanced sampling to compile configurationally diverse training data, calculation of energies and forces using density functional theory (DFT), and training of an E(3)-equivariant MLIP based on the Allegro model. Here, the trained MLIP reproduces DFT-level accuracy in defect transition activation free energy barriers, enables the efficient and stable simulation of multidefect 216-atom supercells, and permits an analysis of the temperature dependence of defect thermodynamic stability and formation/annihilation kinetics to propose an optimal annealing temperature to maximally stabilize VV divacancies.

Computer simulations↗
DOE OSTI2023

Graph-based quantum response theory and shadow Born–Oppenheimer molecular dynamics

Graph-based linear scaling electronic structure theory for quantum-mechanical molecular dynamics simulations [A. M. N. Niklasson et al., J. Chem. Phys. 144, 234101 (2016)] is adapted to the most recent shadow potential formulations of extended Lagrangian Born–Oppenheimer molecular dynamics, including fractional molecular-orbital occupation numbers [A. M. N. Niklasson, J. Chem. Phys. 152, 104103 (2020) and A. M. N. Niklasson, Eur. Phys. J. B 94, 164 (2021)], which enables stable simulations of sensitive complex chemical systems with unsteady charge solutions. The proposed formulation includes a preconditioned Krylov subspace approximation for the integration of the extended electronic degrees of freedom, which requires quantum response calculations for electronic states with fractional occupation numbers. For the response calculations, we introduce a graph-based canonical quantum perturbation theory that can be performed with the same natural parallelism and linear scaling complexity as the graph-based electronic structure calculations for the unperturbed ground state. Further, the proposed techniques are particularly well-suited for semi-empirical electronic structure theory, and the methods are demonstrated using self-consistent charge density-functional tight-binding theory both for the acceleration of self-consistent field calculations and for quantum-mechanical molecular dynamics simulations. Graph-based techniques combined with the semi-empirical theory enable stable simulations of large, complex chemical systems, including tens-of-thousands of atoms.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2021

A deep learning interatomic potential developed for atomistic simulation of carbon materials

Interatomic potentials based on neural-network machine learning method have attracted considerable attention in recent years owing to their outstanding ability to balance the accuracy and efficiency in atomistic simulations. In this work, a neural-network potential (NNP) for carbon is generated to simulate the structural properties of various carbon structures. The potential is trained using a database consisting of crystalline and liquid structures obtained by the first-principles density functional theory (DFT) calculations. The developed potential accurately predicts the energies and forces in crystalline and liquid carbon structures, the energetic stability of defected graphene, and the structures of amorphous carbon as the function of density. As a result, the excellent accuracy and transferability of the NNP provide a promising tool for accurate atomistic simulations of various carbon materials with faster speed and much lower cost.

36 MATERIALS SCIENCE↗