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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.

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At least 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
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↗
DOE OSTI2023

Shadow Molecular Dynamics and Atomic Cluster Expansions for Flexible Charge Models

Here, a shadow molecular dynamics scheme for flexible charge models is presented where the shadow Born–Oppenheimer potential is derived from a coarse-grained approximation of range-separated density functional theory. The interatomic potential, including the atomic electronegativities and the charge-independent short-range part of the potential and force terms, is modeled by the linear atomic cluster expansion (ACE), which provides a computationally efficient alternative to many machine learning methods. The shadow molecular dynamics scheme is based on extended Lagrangian (XL) Born–Oppenheimer molecular dynamics (BOMD). XL-BOMD provides stable dynamics while avoiding the costly computational overhead associated with solving an all-to-all system of equations, which normally is required to determine the relaxed electronic ground state prior to each force evaluation. To demonstrate the proposed shadow molecular dynamics scheme for flexible charge models using atomic cluster expansion, we emulate the dynamics generated from self-consistent charge density functional tight-binding (SCC-DFTB) theory using a second-order charge equilibration (QEq) model. The charge-independent potentials and electronegativities of the QEq model are trained for a supercell of uranium oxide (UO 2 ) and a molecular system of liquid water. The combined ACE+XL-QEq molecular dynamics simulations are stable over a wide range of temperatures both for the oxide and for the molecular systems and provide a precise sampling of the Born–Oppenheimer potential energy surfaces. Accurate ground Coulomb energies are produced by the ACE-based electronegativity model during an NVE simulation of UO 2 , predicted to be within 1 meV of those from SCC-DFTB on average during comparable simulations.

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

Advances in relaxation and memory effects of magnetic nanoparticles for biomedical applications

Functionalized magnetic nanoparticles are pivotal in magnetic resonance imaging, computed tomography, controlled drug delivery, and hyperthermia treatments due to their exceptional magnetic relaxation and functional properties. The magnetic core composition and structure significantly affects the complex magnetic properties of these nanoparticles necessitating a thorough examination of magnetism fundamentals related to these systems. One important aspect is the ability of magnetic nanoparticles to retain previous magnetic state configurations known as memory effect, primarily governed by domain structure and magnetic anisotropy. Despite its relevance to advanced applications, comprehensive studies on magnetic relaxation and memory effects remain limited. Here, the present review aims to bridge this gap by investigating relaxation mechanisms, synthesis strategies, and applications, fostering further innovation. It investigates the memory effects and their dependence on particle composition and morphology along with key synthesis techniques for large-scale production in industrial adoption. Structured into focused sections on magnetic properties and their influence on biomedical and technological applications, this review provides essential insights into memory effects, magneto-relaxation mechanisms, influencing factors, and both experimental and theoretical methodologies. It also delves into computational modelling and AI-driven design, which are revolutionizing the prediction, discovery, and optimization of materials with tailored properties.

36 MATERIALS SCIENCE↗
DOE OSTI2022

DeePKS + ABACUS as a Bridge between Expensive Quantum Mechanical Models and Machine Learning Potentials

Recently, the development of machine learning (ML) potentials has made it possible to perform large-scale and long-time molecular simulations with the accuracy of quantum mechanical (QM) models. However, for different levels of QM methods, such as density functional theory (DFT) at the meta-GGA level and/or with exact exchange, quantum Monte Carlo, etc., generating a sufficient amount of data for training an ML potential has remained computationally challenging due to their high cost. In this work, we demonstrate that this issue can be largely alleviated with Deep Kohn–Sham (DeePKS), an ML-based DFT model. DeePKS employs a computationally efficient neural network-based functional model to construct a correction term added upon a cheap DFT model. Upon training, DeePKS offers closely matched energies and forces compared with high-level QM method, but the number of training data required is orders of magnitude less than that required for training a reliable ML potential. As such, DeePKS can serve as a bridge between expensive QM models and ML potentials: one can generate a decent amount of high-accuracy QM data to train a DeePKS model and then use the DeePKS model to label a much larger amount of configurations to train an ML potential. Further, this scheme for periodic systems is implemented in a DFT package ABACUS, which is open source and ready for use in various applications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Curated Collection of More than 20,000 Experimentally Reported One-Dimensional Metal–Organic Frameworks

Herein, a collection of more than 20,000 experimentally derived crystal structures for metal–organic frameworks (MOFs) that do not have two- or three-dimensional covalently bonded networks has been developed from the materials available at the Cambridge Crystallographic Data Centre. Of these 20,000 1D MOFs, more than 12,000 structures have been verified to be solvent-free and in exact agreement with the stoichiometry of the synthesized materials. More than 10% of the complete data set comprise materials including two or more distinct metals. The band gaps of more than 12,000 1D MOFs have been computed at the density functional theory-generalized gradient approximation level, finding more than 2000 materials that have a zero band gap. Molecular simulations of CH4 adsorption in a small number of 1D MOFs indicated that adsorbate-induced deformation plays a significant role in determining adsorption isotherms in these materials. As a result, methods that have been used previously for high-throughput predictions of molecular adsorption in 3D MOFs are not suitable for 1D MOFs.

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

Ir(hkl) Surface Electrochemistry in a Nonadsorbing Acidic Medium

The fundamental properties of electrochemical materials depend on the multiple and often complex interactions between electrode surface sites and electrolyte species at the electrochemical interface. Despite Iridium use in electrolyzer systems, much of its surface electrochemistry remains underexplored. This study investigates the surface electrochemistry of Ir(111), Ir(100), and Ir(110) surfaces in acidic media. Using cyclic voltammetry and CO charge displacement experiments, we establish the charge states and adsorbate coverages as a function of the electrode potential, revealing the presence of hydrogen and hydroxyl co-adsorption at low potentials on (111), and almost no coverage of H ad on (110) facet. In situ Shell Isolated Nanoparticle Enhanced Raman Spectroscopy experiments provide direct evidence of the formation of key adsorbate species, such as hydrogen, hydroxyl, and oxygen, but most importantly, their interactions with interfacial water, confirmed by Density Functional Theory calculations. Our findings highlight the role of co-adsorption and interspecies interactions, with microkinetic adsorption voltammetry simulations corroborating the influence of lateral interactions on adsorption dynamics, particularly for Ir(100) where the OHad formation occurs as a sharp adsorption/desorption current. Our results underscores the importance of interfacial water and hydrogen bonding networks in shaping the electrochemical behavior on Ir surfaces, refining our baseline understanding of the Ir surface electrochemistry necessary for the development of advanced Ir-based electrochemical materials.

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

Final Technical Report for project entitled Highly Active, Durable, and Ultra-Low PGM NSTF Thin Film ORR Catalysts and Support

In this project, the objective was to develop new oxygen reduction reaction (ORR) electrocatalysts for proton exchange membrane fuel cells (PEMFCs) which could exceed all of the Department of Energy (DOE) 2020 targets listed in DE-FOA-0001224, Subtopic 1b, Table I. The expected outcome was development of one or more electrocatalysts which are substantially improved over the current state-of-the-art in terms of overall activity, durability, and cost, and are suitable for automotive traction and stationary fuel cell applications. This report summarizes this project’s progress towards meeting the stated objectives and includes a summary of key findings and conclusions. The development activities towards new highly active and durable thin film electrocatalysts has led to the discovery of several electrocatalysts which approached or exceeded several DOE 2020 targets for activity, durability, and performance in proton exchange membrane fuel cell (membrane electrode assemblies (MEAs). In this project, electrocatalyst development focused on systematic physical and electrochemical characterization of electrocatalyst activity, durability, and performance in MEAs as a function of electrocatalyst fabrication, compositional and structural variables. The project approach was to develop relationships between the catalyst functional responses (activity, durability and performance) with catalyst physical properties and catalyst fabrication methods. Additionally, the thin film catalysts were integrated onto the unique and durable 3M Nanostructured Thin Film (NSTF) support, consisting of arrays of self-assembled organic crystalline whiskers. The catalysts were evaluated against the project targets via extensive electrochemical characterization in MEA format at 3M, advanced structural and compositional microscopy at Oak Ridge National Laboratory (ORNL), and atomic structure analysis via XAFS at Argonne National Laboratory (ANL). The extensive electrochemical and physical characterization resulted in development of several trends which correlate the catalysts’ electrochemical properties to their physical properties, including composition, structure, and method of fabrication. The development was guided by density functional theory (DFT) modeling at Purdue University and kinetic Monte Carlo (kMC) modeling at Johns Hopkins University. The catalyst simulations provided key insights into the observed experimental activity and durability trends, and additionally were utilized to assess new electrocatalyst concepts prior to or coincident with physical catalyst development. This development has led to several electrocatalyst candidates with activity and durability which approach or exceed DOE targets. One class of catalysts, based on nm-scale thin layers of Pt on Ir, met or exceeded 6 of the 6 DOE targets the project addressed.

08 HYDROGEN↗
DOE OSTI2024

Multiscale Modeling of Vinyl-Addition Polynorbornenes: The Effect of Stereochemistry

Vinyl-addition polynorbornenes are candidates for designing high-performance polymers due to unique characteristics, which include a high glass transition temperature associated with a rigid backbone. Recent studies have established that the processability and properties of these polymers can be fine-tuned by using targeted substitutions. However, synthesis with different catalysts results in materials with distinct properties, potentially due to the presence of various stereoisomers that are difficult to quantify experimentally. Herein, we develop all-atom models of polynorbornene oligomers based on classical force fields and density functional theory. To establish the relationship between chemical architecture, chain conformations, and melt structure, we perform detailed molecular dynamics simulations with the fine-tuned atomistic force field and propose simpler coarse-grained descriptions to address the high molecular weight limit. All-atom simulations of oligomers suggest high glass transition temperatures in the range of 550–600 K. In the melt state (800 K), meso chains form highly rigid extended coils (C∞≈11) with amorphous structural characteristics similar to the X-ray diffraction data observed in the literature. In contrast, simulations with racemo chains predict highly helical tubular chain conformations that could promote assembly into crystalline structures.

Polymer Science↗
NASA NTRS1972

Acoustic spectral analysis and testing techniques

Subjects covered in four reports are described including: (1) mathematical techniques for combining decibel levels of octaves or constant bandwidth: (2) techniques for determining equation for power spectral density function; (3) computer program to analyze acoustical test data; and (4) computer simulation of horn responses utilizing hyperbolic horn theory.

Hayes, C. D.↗
DOE OSTI2024

Temperature and density dependent pair potential for deuterium under shock

Large-scale classical molecular dynamics (CMD) simulations naturally include the microscopic physics necessary for atomistic modeling of shock release at the ablator-fuel interface in an inertial confinement fusion (ICF) capsule. Here, the multi-megabar shocks utilized in ICF experiments can drive the deuterium fuel from ambient to electron volt temperatures (T) and multi-fold compression. Modeling interatomic interactions over such an extreme range of conditions is challenging for empirical bond order potentials. We generate a pair potential for deuterium with explicit temperature and mass density dependence from ab initio density functional theory molecular dynamics using the iterative Boltzmann inversion method. This potential accurately reproduces the radial distribution functions and pressures from DFT in CMD equilibrium simulations across a wide range of thermodynamic conditions, yet fails to return the expected Hugoniot relations when used in direct CMD shock simulations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Extended Lagrangian Born–Oppenheimer molecular dynamics for orbital-free density-functional theory and polarizable charge equilibration models

We report extended Lagrangian Born–Oppenheimer molecular dynamics (XL-BOMD) is formulated for orbital-free Hohenberg–Kohn density-functional theory and for charge equilibration and polarizable force-field models that can be derived from the same orbital-free framework. The purpose is to introduce the most recent features of orbital-based XL-BOMD to molecular dynamics simulations based on charge equilibration and polarizable force-field models. These features include a metric tensor generalization of the extended harmonic potential, preconditioners, and the ability to use only a single Coulomb summation to determine the fully equilibrated charges and the interatomic forces in each time step for the shadow Born–Oppenheimer potential energy surface. The orbital-free formulation has a charge-dependent, short-range energy term that is separate from long-range Coulomb interactions. This enables local parameterizations of the short-range energy term, while the long-range electrostatic interactions can be treated separately. The theory is illustrated for molecular dynamics simulations of an atomistic system described by a charge equilibration model with periodic boundary conditions. The system of linear equations that determines the equilibrated charges and the forces is diagonal, and only a single Ewald summation is needed in each time step. The simulations exhibit the same features in accuracy, convergence, and stability as are expected from orbital-based XL-BOMD.

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

Intricate short-range order in GeSn alloys revealed by atomistic simulations with highly accurate and efficient machine-learning potentials

GeSn alloys hold promise for silicon-compatible integrated applications in electronics, photonics, and topological quantum devices. However, understanding their intricate structures using density functional theory (DFT) calculations is hindered by spatiotemporal constraints. To overcome this limitation, we develop highly accurate and efficient machine-learning interatomic potentials based on a neuroevolution potential approach with farthest point sampling on a comprehensive DFT data set. The application of the developed machine-learning potential in large-scale atomistic simulations bridges the spatiotemporal gap between modeling and advanced characterizations, and facilitates the discovery of structural intricacies in GeSn alloys. Through extensive statistical sampling, we identify a type of short-range order (SRO) that is distinguished by both its structural signature and electronic band gap from the SRO structure previously predicted. Modeling based on a large simulation cell reveals the coexistence of nano SRO domains with various degrees of ordering, demonstrating a complex spatial heterogeneity of SRO structure. Finally, our study not only reinforces the significance of fine-level structural information in alloys, but it also constitutes an effective framework for exploring SRO in a broad range of complex alloys based on highly accurate and effective machine-learning potentials.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Machine learning dielectric screening for the simulation of excited state properties of molecules and materials

Accurate and efficient calculations of absorption spectra of molecules and materials are essential for the understanding and rational design of broad classes of systems. Solving the Bethe–Salpeter equation (BSE) for electron–hole pairs usually yields accurate predictions of absorption spectra, but it is computationally expensive, especially if thermal averages of spectra computed for multiple configurations are required. We present a method based on machine learning to evaluate a key quantity entering the definition of absorption spectra: the dielectric screening. We show that our approach yields a model for the screening that is transferable between multiple configurations sampled during first principles molecular dynamics simulations; hence it leads to a substantial improvement in the efficiency of calculations of finite temperature spectra. We obtained computational gains of one to two orders of magnitude for systems with 50 to 500 atoms, including liquids, solids, nanostructures, and solid/liquid interfaces. Importantly, the models of dielectric screening derived here may be used not only in the solution of the BSE but also in developing functionals for time-dependent density functional theory (TDDFT) calculations of homogeneous and heterogeneous systems. Overall, our work provides a strategy to combine machine learning with electronic structure calculations to accelerate first principles simulations of excited-state properties.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Nonadiabatic dynamics of photoexcited thiopyridone isomers: An interplay between El-Sayed’s conditions and energy gap law

We investigated the non-adiabatic dynamics of photoexcited thiopyridone systems across their ortho-, meta-, and para-isomeric forms. The relaxation pathways of the three isomers in both gas phase and solvent environments are mapped using surface hopping dynamics based on time-dependent density functional theory. Our analysis highlights the influence of isomeric structures on photophysical behavior, offering insights into design principles to control photochemical phenomena. The simulations suggest a systematic reduction in the rate of intersystem crossing (ISC) from ortho- to meta- to para-isomer. Comparisons with multiconfigurational wave function methods in the gas phase further demonstrate how electronic structure influences the predicted dynamical pathways. The simulated dynamics demonstrates that the spin–orbit coupling strength alone does not determine the rate of ISC, as both state energetics and underlying electronic and structural features play decisive roles. These aspects explain the much slower ISC in the para-isomer, as well as the non-negligible role of the El-Sayed forbidden pathway in the computed ISC dynamics.

Complete-active space self-consistent field↗
DOE OSTI2024

High pressure melt line of nickel using a generalized embedded atomic method potential

As the second most abundant metal in the Earth's core, nickel plays an important role in determining the structure and temperature of the Earth's core. Yet, the melt line of Ni at pressures corresponding to the Earth's core has not been explored in the literature. Many previous experimental and simulation efforts have reported the melting point of Ni at pressures below 100 GPa, but there exist large discrepancies, most of which have persisted due to various experimental and simulation bottlenecks in handling extreme pressure and temperature conditions. We adopted the generalized embedded atom method, which overcomes the limitations of existing interatomic potentials, to probe phase stability and phase boundaries of Ni at pressures between 50 and 500 GPa. Further, the potential was validated by comparing the cold curves, phonon dispersion curves, and enthalpies of fusion with ab initio density functional theory calculations. Our analysis shows that face centered cubic (FCC) is stable, and the hexagonal close packed (HCP) and body centered cubic (BCC) phases are metastable close to the melt line. Melting temperatures at different pressures were obtained from two-phase co-existence simulations and take the following functional form: $T$ m = $1969.23+19.15P-0.012P$ 2 . In contrast to iron, differences between the melting points of the stable and metastable phases of Ni are less than 250 K at 300 GPa, and the difference in melting points of the metastable BCC and HCP phases changes sign at 500 GPa, which implies that the phase transition mechanisms during solidification can be very complex.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Design of Graphene/Ionic Liquid Composites for Carbon Capture

Pore size is a crucial factor impacting gas separation in porous separation materials, but how to control the pore size to optimize the separation performance remains a challenge. Here, we propose a design of graphene/ionic liquid composites with tunable slit pore sizes, where cations and anions of ionic liquids are intercalated between graphene layers. By varying the sizes of the ions, we show from first-principles density functional theory calculations that the accessible pore size can be tuned from 3.4 to 6.0 Å. Grand canonical Monte Carlo simulations of gas sorption find that the composite materials possess high CO 2 uptake at room temperature and 1 bar (up to ~8.5 mmol/g). Further simulations of the sorption of gas mixtures reveal that high CO 2 /N 2 and CO 2 /CH 4 adsorption selectivities can be obtained when the accessible pore size is <5 Å. This work suggests a new strategy to achieve tunable pore sizes via the graphene/IL composites for highly selective CO 2 /N 2 and CO 2 /CH 4 adsorption.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗