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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 775 records · Page 43

All sources NASA NTRS DOE OSTI

DOE OSTI2023

Active Learning Framework

Machine learning (ML) of interatomic potentials show great promise to accelerate scientific simulation, e.g., by emulating expensive computations at a high accuracy but much reduced computational cost. Training datasets are calculated from computationally expensive ab initio quantum mechanics methods, density functional theory (DFT). Trained on this data, an ML model can be very successful in predicting energy and forces for new atomic configurations. A critical factor is the quality and diversity of the training dataset. Thus, a highly automated approach to dataset construction based on active learning framework is designed suitable for material physics. The active learning scheme begins with fully randomized atomic configurations. Then, many Molecular Dynamics (MD) trajectories are simulated using current ML potentials, where each MD trajectory is initialized to a random disordered configuration. The temperature is varied in order to diversify the sampled configuration during these simulations. The variance of predictions for eight neural networks within an ensemble is analyzed to determine whether the model is operating as expected. This helps in determining whether collecting more data would be helpful to the model by checking the ensemble variance is greater than the threshold. In this case, the MD trajectory is terminated and the final atomic configuration is placed on a queue (SQL database) for DFT calculations and added to training dataset. Periodically, ML model is retrained to the updated training model. This Active Learning loop is iterated until the cost of MD simulations becomes prohibitively expensive. The MD simulations will hopefully be sufficiently robust to support nucleation after many active learning iterations. In this sense, active learning scheme must automatically discover the important low energy and nonequilibrium physics.

Nebgen, Benjamin↗

DOE OSTI2025

Bayesian prior construction for uncertainty quantification in first-principles statistical mechanics

First-principles statistical mechanics enables the prediction of thermodynamic and kinetic properties of materials, but is computationally expensive. Many approaches require surrogate models to calculate energies within Monte Carlo or molecular dynamics simulations. Inexpensive surrogates such as cluster expansions enable otherwise intractable calculations by interpolating data from higher accuracy methods, such as Density Functional Theory (DFT). Surrogate models introduce uncertainty into downstream calculations, in addition to any uncertainty inherent to DFT calculations. Bayesian frameworks address this by quantifying uncertainty and incorporating expert knowledge through priors. However, constructing effective priors remains challenging. This work introduces and describes practical strategies for building Bayesian cluster expansions, focusing on basis truncation, hyperparameter selection, and ground state replication. We analyze multiple basis truncation schemes, compare cross-validation to the evidence-approximation for hyperparameter optimization, and provide methods to find and enforce ground-state-preserving models through priors. Additionally, we compare the uncertainties between different approximations to DFT (LDA, PBE, SCAN) against the uncertainty introduced with the use of cluster expansion surrogate models. These approaches are demonstrated on the BCC Li x Mg 1-x and Li x Al 1-x alloys, which are both of interest for solid-state Li batteries. Our results provide guidelines for constructing and utilizing Bayesian cluster expansions, thereby improving the transparency of materials modeling. Furthermore, the approaches and insights developed in this work can be transferred to a wide range of cluster expansion surrogate models, including the atomic cluster expansion and related machine-learned interatomic potential architectures.

Alloy theory↗

DOE OSTI2023

Atomic Three-Dimensional Investigations of Pd Nanocatalysts for Acetylene Semi-hydrogenation

Deciphering the three-dimensional (3D) insight into nanocatalyst surfaces at the atomic level is crucial to understanding catalytic reaction mechanisms and developing high-performance catalysts. Nevertheless, better understanding the inherent insufficiency of a long-range ordered lattice in nanocatalysts is a big challenge. Here, in this work, we report the local structure of Pd nanocatalysts, which is beneficial for demonstrating the shape-structure-adsorption relationship in acetylene hydrogenation. The 5.27 nm spherical Pd catalyst (Pd sph ) shows an ethylene selectivity of 88% at complete acetylene conversion, which is much higher than those of the Pd octahedron and Pd cube and superior to other reported monometallic Pd nanocatalysts so far. By virtue of the local structure revelation combined with the atomic pair distribution function (PDF) and reverse Monte Carlo (RMC) simulation, the atomic surface distribution of the unique compressed strain of Pd-Pd pairs in Pd sph was revealed. Density functional theory calculations verified the obvious weakening of the ethylene adsorption energy on account of the surface strain of Pd sph . It is the main factor to avoid the over-hydrogenation of acetylene. The present work, entailing shape-induced surface strain manipulation and atomic 3D insight, opens a new path to understand and optimize chemical activity and selectivity in the heterogeneous catalysis process.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

On the liquid–liquid phase transition of dense hydrogen

Until recently the consensus theory/computation interpretation of the challenging liquid liquid phase transition (LLPT) of high-pressure hydrogen was first order. Cheng et al. developed a machine-learnt potential (MLP) that, in larger molecular dynamics (MD) simulations, gives a continuous transition instead. Here, we show that the MLP does not reproduce our still larger MD density-functional theory (MD-DFT) calculations as it should. Since the MLP is not a faithful surrogate for the MD-DFT, the Ref. 6 prediction of a supercritical atomic liquid is unfounded.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2022

Liquid-Liquid Transition in Water from First Principles

A long-standing question in water research is the possibility that supercooled liquid water can undergo a liquid-liquid phase transition (LLT) into high- and low-density liquids. Here, we used several complementary molecular simulation techniques to evaluate the possibility of an LLT in an ab initio neural network model of water trained on density functional theory calculations with the SCAN exchange correlation functional. We conclusively show the existence of a first-order LLT and an associated critical point in the SCAN description of water, representing the first definitive computational evidence for an LLT in water from first principles.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2019

The Effect Stacking Fault Segregation and Phase Transformations have on Creep Strength in Ni-based Superalloys

In this study, two commercially similar polycrystalline Ni-based disk superalloys (LSHR and ME3) were creep tested at 760C and 552MPa to approximately 0.3% plastic strain. LSHR consistently displayed superior creep properties at this stress/temperature regime even though the microstructural characteristics between the two alloys were comparable. High resolution structural and chemical analysis, however, revealed significant differences between the two alloys among active gamma prime shearing modes involving superlattice intrinsic and extrinsic stacking faults. In ME3, Co and Cr segregation and Ni and Al depletion were observed along the intrinsic faults - revealing a gamma prime -to- gamma phase transformation. Conversely in LSHR, an alloy with a higher W content, Co and W segregation was observed along the intrinsic faults. This observation combined with scanning transmission electron microscopy (STEM) simulations confirm a gamma prime-to-D019 phase transformation along the intrinsic faults in LSHR. Using experimental observations and density functional theory calculations, a novel local phase transformation strengthening mechanism is proposed that could be further utilized to improve the high temperature creep capabilities of Ni-base disk alloys.

Superalloy↗

DOE OSTI2024

SPARC-X: Quantum simulations at extreme scale - reactive dynamics from first principles

We have developed the massively parallel electronic structure code SPARC-X: a computational framework for performing Kohn-Sham Density Functional Theory (DFT) calculations that can scale linearly with the number of atoms in the system, while being able to leverage petascale and emerging exascale parallel computers to study chemical phenomena at unprecedented length and time scales. SPARC-X exploits a recent breakthrough in electronic structure methodologies: systematically improvable, strictly local, orthonormal, discontinuous real-space bases that efficiently and systematically capture the local chemistry of the system. With further adaptation using new machine-learning techniques and the use of the massively parallel Spectral Quadrature (SQ) electronic structure method, the algorithmic complexity and prefactor associated with DFT calculations involving semilocal as well as hybrid functionals are dramatically reduced. Using petascale computational resources, SPARC-X enables quantum mechanical simulations at length and time scales previously accessible only by empirical approaches, e.g., 1,000,000 atoms for a few picoseconds using semilocal functionals or 1,000 atoms for a few picoseconds using hybrid functionals. Using exascale resources, the sizes and times targeted are two orders of magnitude larger. Such a capability has applications in a wide variety of chemical sciences, including reactive interfaces where large length- and/or long time-scales are needed and traditional force fields fail. This is particularly important in dynamic catalysis, where bond breaking and formation must be understood in detail. We developed, tested, and employed the SPARC-X framework to understand the photocatalytic properties of TiO 2 nanoparticles, revealing finite size effects that cannot be captured with standard model systems or functionals. This integrated development and application strategy ensures that SPARC-X remains a robust, efficient, and scalable software package for quantum simulations on current petascale and emerging exascale computing resources.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Observation of a highly conductive warm dense state of water with ultrafast pump–probe free-electron-laser measurements

The electrical conductivity of water under extreme temperatures and densities plays a central role in modeling planetary magnetic fields. Experimental data are vital to test theories of high-energy-density water and assess the possible development and presence of extraterrestrial life. These states are also important in biology and chemistry studies when specimens in water are confined and excited using ultrafast optical or free-electron lasers (FELs). Here we utilize femtosecond optical lasers to measure the transient reflection and transmission of ultrathin water sheet samples uniformly heated by a 13.6 nm FEL approaching a highly conducting state at electron temperatures exceeding 20 000 K. The experiment probes the trajectory of water through the high-energy-density phase space and provides insights into changes in the index of refraction, charge carrier densities, and AC electrical conductivity at optical frequencies. At excitation energy densities exceeding 10 MJ/kg, the index of refraction falls to n = 0.7, and the thermally excited free-carrier density reaches ne = 5 × 1027 m−3, which is over an order of magnitude higher than that of the electron carriers produced by direct photoionization. Significant specular reflection is observed owing to critical electron density shielding of electromagnetic waves. The measured optical conductivity reaches 2 × 104 S/m, a value that is one to two orders of magnitude lower than those of simple metals in a liquid state. At electron temperatures below 15 000 K, the experimental results agree well with the theoretical calculations using density-functional theory/molecular-dynamics simulations. With increasing temperature, the electron density increases and the system approaches a Fermi distribution. In this regime, the conductivities agree better with predictions from the Ziman theory of liquid metals.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2022

Dynamic aspects of graphene deformation and fracture from approximate density functional theory

Graphene is one of the most intriguing two-dimensional carbon materials. Its mechanical strength and failure are key concerns for materials engineering and applications. Despite the success of fracture mechanics, the mechanism of how pristine materials fail remains an elusive problem. While many theoretical studies based on molecular dynamics using empirical forcefields have tried to address this question, atomic-scale mechanics are not clearly understood. Especially, a widely employed bond-breaking approach based on the critical bond length has not been rigorously tested. Here, utilizing molecular dynamics simulations with density functional based tight binding, we investigate how the failure of the pristine material initiates. The Wiberg bond order (W BO ) to estimate the change of chemical bonds shows a transition from sp 2 (W BO ~ 1.33) to sp 3 (WBO < 1.0) during the deformation. However, it reveals that a single threshold value for either the WBO or bond length is insufficient to decide failure of pristine material without free edges or defects. Instead, collective behaviors of the local atomic group govern the fracture initiation of pristine graphene. Overall, our study provides dynamic mechanical responses based on quantum mechanics, which have not been captured by empirical forcefields, opening opportunities to design properties by precisely coupling the mechanics and quantum mechanics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Building a DFT+U machine learning interatomic potential for uranium dioxide

Despite uranium dioxide (UO 2 ) being a widely used nuclear fuel, fuel performance models rely extensively on empirical correlations of material behavior, leveraging the historical operating experience of UO 2 . Mechanistic models that consider an atomistic understanding of the processes governing fuel performance (such as fission gas release and creep) will enable a better description of fuel behavior under non-prototypical conditions such as in new reactor concepts or for modified UO 2 fuel compositions. To this end, molecular dynamics simulation is a powerful tool for rapidly predicting physical properties of proposed fuel candidates. However, the reliability of these simulations depends largely on the accuracy of the atomic forces. Traditionally, these forces are computed using either a classical force field (FF) or density functional theory (DFT). While DFT is relatively accurate, the computational cost is burdensome, especially for f-electron elements, such as actinides. By contrast, classical FFs are computationally efficient but are less accurate. For these reasons, we report a new accurate machine learning interatomic potential (MLIP) for UO 2 that provides high-fidelity reproduction of DFT forces at a similar low cost to classical FFs. We employ an active learning approach that autonomously augments the DFT training data set to iteratively refine the MLIP. To further improve the quality of our predictions, we utilize transfer learning to retrain our MLIP to higher-accuracy DFT+U data. We validate our MLIPs by comparing predicted physical properties (e.g., thermal expansion and elastic properties) with those from existing classical FFs and DFT/DFT+U calculations, as well as with experimental data when available.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2026

Modeling the behavior of concentrated aqueous HNO 3 using machine learning interatomic potentials

We develop two multi-defect machine learning interatomic potentials (MLIPs) trained at the BLYP-D2 and PBE-D3 density functional theories using the DeepMD-kit, allowing for the investigation of structural and thermodynamic properties of nitric acid over a wide range of concentrations via molecular dynamics (MD) simulations. We directly compute the degree of dissociation, α, and pK a from MD simulations, revealing that HNO 3 behaves as a weaker acid at higher concentrations, noting that our standard-state pK a value is in excellent agreement with the experimental one. In general, good agreement is observed with experimental results such as α and density outside the training dataset, with only modest deviations at low-to-medium concentrations. We benchmark our custom multi-defect DeepMD MLIPs against foundational models MACE-MP0 and MACE-OFF23. The foundation models capture some aspects of HNO 3 /NO 3 − solvation in concentrated nitric acid but show noticeable density errors and miss subtle structural features relevant to spectroscopy, whereas the bespoke DeepMD MLIPs yield more compact solvation shells, reproduce density-concentration trends, and run ∼12–15× faster than MACE-MP0. Although classical FFs are still more efficient and match experimental densities better, they lack chemical reactivity and thus cannot predict α or pK a , underscoring the need for system-specific reactive MLIPs beyond universal MLIPs.

Dinpajooh, Mohammadhasan [Pacific Northwest Nation↗

DOE OSTI2025

Facet-Dependent Cold Welding of Au Nanorods Revealed by Liquid Cell Transmission Electron Microscopy

Cold welding of metals at the nanoscale has been demonstrated to play a significant role in bottom-up manufacturing and self-healing processes of nanostructures and nanodevices. However, the welding mechanism at the nanoscale is not well understood. In this study, a comprehensive demonstration of the cold welding process of gold nanorods with different modes is presented through in situ liquid cell transmission electron microscopy. The experimental results and molecular dynamics simulations reveal that the nanorods are welded through the facet-dependent atomic surface diffusion and rearrangement along {100} facets. The density functional theory calculations indicate that the preferred coalescence of two {100} surfaces is thermodynamically favorable. Unlike the prevalent “oriented attachment” in the nanoparticle coalescence, the misalignment of nanorod orientations and local stresses can induce grain boundaries and stacking faults in the welded interface.

42 ENGINEERING↗

DOE OSTI2022

Characterization of the atomic-level structure of γ-alumina and (111) Pt/γ-alumina interfaces

The atomic-level structure of platinum/γ-alumina interfaces is characterized in a model system of dense γ-alumina embedded with faceted Pt NPs produced by implantation of platinum ions into sapphire followed by thermal annealing in air at 800 °C. Aberration-corrected scanning transmission electron microscopy (STEM) was used to collect atomic-resolution images, which are compared to STEM image simulations of two experimentally-based bulk models of γ-alumina by Smrčok et al. and Zhou and Snyder. A density functional theory (DFT) based model of (111) interfaces with different chemical terminations (O, Al 1 , Al 2 ) of the γ-alumina developed by Oware Sarfo et al. is also compared to experimental STEM data from Pt/γ-alumina interfaces. Further, the Smrčok γ-alumina model provides a better fit than the Zhou structure to the bulk of the γ-alumina. The oxygen-terminated Oware Sarfo model best fits the experimental data and is a very good model close to the interface. However, the fit of the interface model to the experimental data is poorer beyond the third atomic layer in the γ-alumina. This is attributed to compromises required in the design of the model to limit the cell size and computational time for DFT calculations. Understanding the accuracy and limits of the structural models of γ-alumina and Pt/γ-alumina interfaces is important to further the understanding of the structure/property relationships in this system.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Insights into the Hydrogen Evolution Reaction on 2D Transition-Metal Dichalcogenides

Understanding hydrogen evolution reaction (HER) behaviors over two-dimensional transition-metal dichalcogenides (2D-TMDs) is critical for the development of nonprecious HER electrocatalysts with better activity. Here, in this work, by combining density functional theory calculations with microkinetic modeling, we thoroughly investigated the HER mechanism on 2D-TMDs. We find an important dependence of simulated cell size on the calculated hydrogen adsorption energy and the activation barrier for MoS 2 . Distinct from previous “H migration” mechanisms proposed for the Heyrovsky reaction, the rate-determining step for MoS 2 , we propose that the Mo site only serves as the stabilized transition state rather than H adsorption. In comparison to transition-metal electrocatalysts, we find that the activation barrier of the Heyrovsky reaction on 2D-TMDs scales with the hydrogen adsorption energy exactly as for transition metals except that all activation energies are displaced upward by ca. 0.4 eV. This higher Heyrovsky activation barrier is responsible for the substantially lower activity of 2D-TMDs. We further show that this higher activation barrier stems from the more positively charged adsorbed hydrogen on the chalcogenides interacting repulsively with the incoming proton. Based on these insights, we discuss potential strategies for the design of nonprecious HER catalysts with activity comparable to Pt.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Spin Decoherence in VOPc@graphene Nanoribbon Complexes

Carbon nanoribbons or nanographene qubit arrays can facilitate quantum-to-quantum transduction between light, charge, and spin, making them an excellent testbed for fundamental science in quantum coherent systems and for the construction of higher-level qubit circuits. Here, in this work, we study spin decoherence due to coupling with a surrounding nuclear spin bath of an electronic molecular spin of a vanadyl phthalocyanine (VOPc) molecule integrated onto an armchair-edged graphene nanoribbon (GNR). Density functional theory (DFT) is used to obtain ground-state atomic configurations. Decay of spin coherence in Hahn echo experiments is then simulated using the cluster correlation expansion method with a spin Hamiltonian involving hyperfine and electric field gradient tensors calculated from DFT. We find that the decoherence time T 2 is anisotropic with respect to magnetic field orientation and determined only by the hydrogen nuclear spins on both VOPc and GNR. Large electron spin echo envelope modulation (ESEEM) due to nitrogen and vanadium nuclear spins is present at specific field ranges and can be completely suppressed by tuning the magnetic field. The relation between these field ranges and the hyperfine interactions is analyzed. The effects of interactions with the nuclear quadrupole moments are also studied, validating the applicability and limitations of the spin Hamiltonian when they are disregarded.

Hamiltonians↗

DOE OSTI2020

First-Principles Approach for Assessing Cold Electron Injection Efficiency of Dye-Sensitized Solar Cell: Elucidation of Mechanism of Charge Injection and Recombination

The adequacy of the inclusion of spacer units in the metal-free D-π–A organic dyes pertaining to the augmentation of dye-sensitized solar cell (DSSC) efficiency has been examined through the excited-state simulations of the charge injection and recombination processes at the dye–semiconductor interface. Within the framework of the time-dependent density functional theory, the proposed computational studies focus on the precise evaluation of pivotal factors controlling the rates of photoinduced charge-transfer and energy-transfer processes, including electronic coupling, reorganization energy, and threshold energy barrier in the semiclassical Marcus formalism. The estimation of the fluorescent state appears to be the crucial step while explaining the ultrafast electron injection process and the charge recombination at the Marcus inverted region, as revealed by the obtained results. The retardation of charge recombination is facilitated by the insertion of a thiophene moiety between the π-bridge and the acceptor units. The estimated cold electron injection efficiencies deploying the Onsager–Braun theory, that rely on the computations of cold electron injection lifetime and cold electron lifetime, show a linear correlation with the experimental photovoltaic parameters of the DSSC comprising short-circuit current density, open-circuit voltage, and power conversion efficiency. The outcomes of the present investigation establish a basis for unraveling the mechanism of intricate dynamical processes upon photoexcitation of the sensitizers, as well as devising plausible routes for functional DSSC materials.

14 SOLAR ENERGY↗

DOE OSTI2021

Teaching a neural network to attach and detach electrons from molecules

Abstract Interatomic potentials derived with Machine Learning algorithms such as Deep-Neural Networks (DNNs), achieve the accuracy of high-fidelity quantum mechanical (QM) methods in areas traditionally dominated by empirical force fields and allow performing massive simulations. Most DNN potentials were parametrized for neutral molecules or closed-shell ions due to architectural limitations. In this work, we propose an improved machine learning framework for simulating open-shell anions and cations. We introduce the AIMNet-NSE (Neural Spin Equilibration) architecture, which can predict molecular energies for an arbitrary combination of molecular charge and spin multiplicity with errors of about 2–3 kcal/mol and spin-charges with error errors ~0.01e for small and medium-sized organic molecules, compared to the reference QM simulations. The AIMNet-NSE model allows to fully bypass QM calculations and derive the ionization potential, electron affinity, and conceptual Density Functional Theory quantities like electronegativity, hardness, and condensed Fukui functions. We show that these descriptors, along with learned atomic representations, could be used to model chemical reactivity through an example of regioselectivity in electrophilic aromatic substitution reactions.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Weighted active space protocol for multireference machine-learned potentials

Multireference methods such as multiconfiguration pair-density functional theory accurately capture electronic correlation in systems with strong multiconfigurational character, but their cost precludes direct use in molecular dynamics. Combining these methods with machine-learned interatomic potentials (MLPs) can extend their reach. However, the sensitivity of multireference calculations to the choice of the active space complicates the consistent evaluation of energies and gradients across structurally diverse nuclear configurations. To overcome this limitation, we introduce the weighted active space protocol (WASP), a systematic approach to assign a consistent active space for a given system across uncorrelated configurations. By integrating WASP with MLPs and enhanced sampling techniques, we propose a data-efficient active learning cycle that enables the training of an MLP on multireference data. We demonstrated the approach on the TiC + -catalyzed C–H activation of methane, a reaction that poses challenges for Kohn–Sham density functional theory due to its significant multireference character. This framework enables accurate and efficient modeling of catalytic dynamics, establishing a paradigm for simulating complex reactive processes beyond the limits of conventional electronic-structure methods.

enhanced sampling↗