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Results for “Density functional theory development”

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 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Many-body reactive force field development for carbon condensation in C/O systems under extreme conditions

In this paper, we describe the development of a reactive force field for C/O systems under extreme temperatures and pressures, based on the many-body Chebyshev Interaction Model for Efficient Simulation (ChIMES). The resulting model, which targets carbon condensation under thermodynamic conditions of 6500 K and 2.5 g cm –3 , affords a balance between model accuracy, complexity, and training set generation expense. We show that the model recovers much of the accuracy of density functional theory for the prediction of structure, dynamics, and chemistry when applied to dissociative condensed phase systems at 1:1 and 1:2 C:O ratios, as well as molten carbon. Our C/O modeling approach exhibits a 104 increase in efficiency for the same system size (i.e., 128 atoms) and a linear system size scalability over standard quantum molecular dynamics methods, allowing the simulation of significantly larger systems than previously possible. We find that the model captures the condensed-phase reaction-coupled formation of carbon clusters implied by recent experiments, and that this process is susceptible to strong finite size effects. Overall, we find the present ChIMES model to be well suited for studying chemical processes and cluster formation at pressures and temperatures typical of shock waves. We expect that the present C/O modeling paradigm can serve as a template for the development of a broader high pressure–high temperature force-field for condensed phase chemistry in organic materials.

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

Rapid Design of Cost-Effective Refractory High Entropy Alloys Strengthened by Precipitation

Funded by the ARPA-E ULTIMATE program, this project aims to develop cost-effective refractory high entropy alloys (RHEAs) for gas turbine blades applications at 1300 degree Celsius and above. The alloy design focuses on carbides precipitation strengthening in a strong and ductile solid solution BCC matrix. The goal is to achieve balanced mechanical properties including room temperature ductility & fracture toughness and high temperature strength and creep resistance, while maintaining comparable oxidation resistance and densities to Ni-base superalloys. To accelerate alloy design, multiscale modeling including CALPHAD and first-principles density functional theory (DFT) calculations and machine learning are performed. Specifically, high throughput phase diagram calculations and screening are carried out using CALPHAD; DFT calculations are performed to predict intrinsic ductility, grain boundary strengthening potential, coefficient of thermal expansion (CTE) and temperature-dependent elastic constants. Temperature dependent yield stress, creep strain and CTE are also predicted using machine learning. Down-selected alloys are synthesized in small buttons of about 250g using arc melting for rapid evaluation on microstructure and mechanical properties before employing plasma arc melting and additive manufacturing for producing large ingots. Preliminary computational and experimental results of this project will be presented and future direction in RHEA development will be discussed.

high temperature materials↗
DOE OSTI2023

Explosion Mechanism of Core-collapse Supernovae: Role of the Si/Si–O Interface

Abstract We present a simple criterion to predict the explodability of massive stars based on the density and entropy profiles before collapse. If a pronounced density jump is present near the Si/Si–O interface, the star will likely explode. We develop a quantitative criterion by using ∼1300 1D simulations where ν -driven turbulence is included via time-dependent mixing-length theory. This criterion correctly identifies the outcome of the supernova more than 90% of the time. We also find no difference in how this criterion performs on two different sets of progenitors, evolved using two different stellar evolution codes: FRANEC and KEPLER. The explodability as a function of mass of the two sets of progenitors is very different, showing: (i) that uncertainties in the stellar evolution prescriptions influence the predictions of supernova explosions; (ii) the most important properties of the pre-collapse progenitor that influence the explodability are its density and entropy profiles. We highlight the importance that ν -driven turbulence plays in the explosion by comparing our results to previous works.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Computational assessment of smooth and rough parameter dependence of statistics in chaotic dynamical systems

An assumption of smooth response to small parameter changes, of statistics or long-time averages of a chaotic system, is generally made in the field of sensitivity analysis, and the parametric derivatives of statistical quantities are critically used in science and engineering. In this paper, we propose a numerical procedure to assess the differentiability of statistics with respect to parameters in chaotic systems. Here, we numerically show that the existence of the derivative depends on the Lebesgue integrability of a certain density gradient function, which we define as the derivative of logarithmic SRB density along the unstable manifold. We develop a recursive formula for the density gradient that can be efficiently computed along trajectories, and demonstrate its use in determining the differentiability of statistics. Our numerical procedure is illustrated on low-dimensional chaotic systems whose statistics exhibit both smooth and rough regions in parameter space.

42 ENGINEERING↗
DOE OSTI2022

Creating Frustrated Lewis Pairs in Defective Boron Carbon Nitride for Electrocatalytic Nitrogen Reduction to Ammonia

Abstract The electrocatalytic nitrogen reduction reaction (NRR) on metal‐free catalysts is an attractive alternative to the industrial Haber–Bosch process. However, the state‐of‐the‐art metal‐free electrocatalysts still suffer from low Faraday efficiencies and low ammonia yields. Herein, we present a molecular design strategy to develop a defective boron carbon nitride (BCN) catalyst with the abundant unsaturated B and N atoms as Lewis acid and base sites, which upgrades the catalyst from a single “Lewis acid catalysis” to “frustrated Lewis pairs (FLPs) catalysis.” 14 N 2 / 15 N 2 exchange experiments and density functional theory (DFT) calculations reveal that FLPs can adsorb an N 2 molecule to form a six‐membered ring intermediate, which enables the cleavage of N 2 via a pull–pull effect, thereby significantly reducing the energy barrier to −0.28 eV. Impressively, BCN achieves a high Faraday efficiency of 18.9 %, an ammonia yield of 20.9 μg h −1 mg −1 cat. , and long‐term durability.

Lin, Wenwen↗
DOE OSTI2022

Creating Frustrated Lewis Pairs in Defective Boron Carbon Nitride for Electrocatalytic Nitrogen Reduction to Ammonia

Abstract The electrocatalytic nitrogen reduction reaction (NRR) on metal‐free catalysts is an attractive alternative to the industrial Haber–Bosch process. However, the state‐of‐the‐art metal‐free electrocatalysts still suffer from low Faraday efficiencies and low ammonia yields. Herein, we present a molecular design strategy to develop a defective boron carbon nitride (BCN) catalyst with the abundant unsaturated B and N atoms as Lewis acid and base sites, which upgrades the catalyst from a single “Lewis acid catalysis” to “frustrated Lewis pairs (FLPs) catalysis.” 14 N 2 / 15 N 2 exchange experiments and density functional theory (DFT) calculations reveal that FLPs can adsorb an N 2 molecule to form a six‐membered ring intermediate, which enables the cleavage of N 2 via a pull–pull effect, thereby significantly reducing the energy barrier to −0.28 eV. Impressively, BCN achieves a high Faraday efficiency of 18.9 %, an ammonia yield of 20.9 μg h −1 mg −1 cat. , and long‐term durability.

Lin, Wenwen↗
DOE OSTI2023

Load-Displacement Relation and Gap Distribution Between Rough Surfaces: Partial Differential Equations Approach

We develop a theoretical model to predict the load-displacement relation and probability density function for gaps between contacting rough surfaces. We derive partial differential equations from the previous model (Joe et al., 2018), and extend them to the non-adhesive contact problem. The predictions of the present theory are compared with numerical results using the Green's function molecular dynamics algorithm, and a good agreement is obtained.

adhesion↗
DOE OSTI2023

Strengthening model development and effects of low diffusing solutes to coarsening resistance in aluminum alloys

A modified Orowan strengthening model is proposed to account for finite rod-shaped precipitates with hemispherical caps in aluminum alloy systems. A combined computational and experimental approach is used to study the influences of anisotropic Orowan looping and solute-dislocation interaction on temperature-dependent yield strength. Here, the strengthening model is validated with a dataset containing 297 experimental precipitate geometries, chemistries, temperatures, and strength measurements, and achieves a strong predictive correlation of 0.8713 with experimentally measured yield strengths. Under conditions that are applicable to coarsening, constant particle volume fraction and aspect ratio, the model predicts that short rod precipitates provide far superior strengthening effects compared to plate precipitates. A cast Al-Si-Mg-Cu alloy with rod-shaped Q-phase (Al 3 Cu 2 Mg 9 Si 7 ) precipitates was developed with a novel chemistry exploring the use of low-diffusivity elements (Mn, Ni, V, Zr) to limit precipitate coarsening. The thermodynamic behavior of Mn, Ni, V, and Zr across the Q-phase interface is examined using transmission electron microscopy (TEM), first-principles density-functional theory (DFT) calculations. and atom-probe tomography (APT). DFT calculations utilizing TEM identified Q-phase/Al-matrix interfaces show that Mn, Ni, V, and Zr preferentially segregate to the Q-phase precipitate boundaries which suggests inhibition of precipitate coarsening and higher strengths after temperature exposure. Atom-probe tomography confirms solute atom partitioning/segregation at the Q-phase/Al-matrix interface, found in the modified commercial AS7GU alloy (A356 +0.5%Cu), which supports these observations.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Machine Learning-Driven Solvent Screening for Biobased 2,3-Butanediol Extraction

Biobased 2,3-butanediol (2,3-BDO) is a valuable biomass-derived chemical due to its versatility in being transformed into a wide variety of products. However, the separation and purification of 2,3-BDO from fermentation broth remain a significant challenge owing to its high boiling point and hydrophilic nature. Herein, we developed a machine learning (ML)-based screening workflow that uses molecular calculations as training data and requires only a small number of experimental measurements for validation to identify alternative solvent candidates for the liquid–liquid extraction (LLE) of 2,3-BDO from aqueous solution. In particular, 130 density functional theory (DFT) calculations with the implicit solvation method not only built a correlation between the computational partition coefficient and the experimental distribution coefficient of 2,3-BDO but also parameterized an Extra-Trees ML model to screen the distribution coefficient for a wider range of 6717 organic solvents. The experimental measurements of only 24 solvents were needed to validate the computational results. A list of 50 prioritized solvents was proposed for 2,3-BDO LLE, and seven additional experimental measurements were conducted to further verify our selected solvents. The impact of the extraction temperature and solvent-to-feed ratio was also investigated for selected solvents in experiments. Furthermore, this work suggested alternative solvents for 2,3-BDO LLE and proposed a versatile workflow that requires fewer experiments and can be applied to a broader range of LLE studies.

Extraction↗
DOE OSTI2024

On-the-fly machine learned force fields for the study of warm dense matter: Application to diffusion and viscosity of CH

We develop a framework for on-the-fly machine learned force field (MLFF) molecular dynamics (MD) simulations of warm dense matter (WDM). In particular, we employ an MLFF scheme based on the kernel method and Bayesian linear regression, with the training data generated from the Kohn–Sham density functional theory (DFT) using the Gauss spectral quadrature method, within which we calculate energies, atomic forces, and stresses. We verify the accuracy of the formalism by comparing the predicted properties of warm dense carbon with recent Kohn–Sham DFT results in the literature. In so doing, we demonstrate that ab initio MD simulations of WDM can be accelerated by up to three orders of magnitude, while retaining ab initio accuracy. We apply this framework to calculate the diffusion coefficients and shear viscosity of CH at a density of 1 g/cm3 and temperatures in the range of 75 000–750 000 K. We find that the self- and inter-diffusion coefficients and the viscosity obey a power law with temperature, and that the diffusion coefficient results suggest a weak coupling between C and H in CH. In addition, we find agreement within standard deviation with previous results for C and CH but disagreement for H, demonstrating the need for ab initio calculations as presented here.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Maximizing efficiency of dataset compression for machine learning potentials with information theory

Machine learning interatomic potentials (MLIPs) balance high accuracy and lower costs compared to density functional theory calculations, but their performance often depends on the size and diversity of training datasets. Large datasets improve model accuracy and generalization but are computationally expensive to produce and train on, while smaller datasets risk discarding rare but important atomic environments and compromising MLIP accuracy/reliability. Here, we develop an information-theoretical framework to quantify the efficiency of dataset compression methods and propose an algorithm that maximizes this efficiency. By framing atomistic dataset compression as an instance of the minimum set cover (MSC) problem over atom-centered environments, our method identifies the smallest subset of structures that contains as much information as possible from the original dataset while pruning redundant information. The approach is extensively demonstrated on the GAP-20 and TM23 datasets and validated on 64 varied datasets from the ColabFit repository. Across all cases, MSC consistently retains outliers, preserves dataset diversity, and reproduces the long-tail distributions of forces even at high compression rates, outperforming other subsampling methods. Furthermore, MLIPs trained on MSC-compressed datasets exhibit reduced error for out-of-distribution data even in low-data regimes. We explain these results using an outlier analysis and show that such quantitative conclusions could not be achieved with conventional dimensionality reduction methods. The algorithm is implemented in the open-source QUESTS package and can be used for several tasks in atomistic modeling, from data subsampling, outlier detection, and training improved MLIPs at a lower cost.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Flat band driven itinerant magnetism in the Co-pnictides CaCo 2⁢ As 2 and LaCo 2 ⁢P 2

Flat bands can induce strong electron correlation effects that help stabilize both magnetic and superconducting states. Here, in this study, we carry out angle-resolved photoemission spectroscopy and density functional theory calculations to study the electronic structure of the Co-pnictides CaCo 2 ⁢As 2 and LaCo 2 ⁢P 2 . We find that the 𝑘 𝑧 Fermi topology of ferromagnetic LaCo 2 ⁢P 2 is markedly two-dimensional, while the antiferromagnetic CaCo 2 ⁢As 2 develops a 3D Fermi surface described by a zigzaglike band dispersion perpendicular to the Co-As plane. Furthermore, the magnetism is driven by the electronic correlations of the flat bands with 𝑑 𝑥⁢𝑦 and 𝑑 𝑧 2 orbital character at the Fermi level. Our results link the electronic dimensionality and the magnetic order and emphasize the critical role of the As-As and P-P bond strength along the 𝑐 direction to understand the electronic band structure and the rich phase diagram of transition metal pnictides.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Machine Learned Force Field Modeling of Metal Organic Frameworks for CO2 Direct Air Capture

Direct air capture (DAC) is a method for removing CO2 directly from air. Metal organic frameworks (MOFs) have been studied as DAC sorbent materials because of their structural and chemical diversity. Thermodynamic calculations using classical force fields are often used to evaluate MOFs for their performance in separations such as CO2 capture. Machine-learned force fields (MLFFs) can use machine learning to form quantitative relationships between a material’s chemical structure and the forces and energies predicted by more accurate quantum mechanical calculations, such as dispersion-corrected density functional theory (DFT). These descriptions of forces and energies can be used to improve the accuracy of adsorption calculations. In this work, classical models were used to pre-screen MOFs for CO2 capture. DFT calculations were then used to examine the adsorption mechanism. Next, MLFF models were developed for MOFs to achieve DFT-level accuracy for the forces and energies associated with MOF flexibility and CO2 adsorption. These methods were parametrized based on thousands of DFT calculations of CO2 in flexible MOFs and used to predict MOF structural properties as well as CO2 adsorption properties.

Findley, John↗
DOE OSTI2023

Extending the thermodynamic form factor bootstrap program: multiple particle-hole excitations, crossing symmetry, and reparameterization invariance

In this study, we further the thermodynamic bootstrap program which involves a set of recently developed ideas used to determine thermodynamic form factors of local operators in integrable quantum field theories. These form factors are essential building blocks for dynamic correlation functions at finite temperatures or non-equilibrium stationary states. In this work we extend this program in three ways. Firstly, we demonstrate that the conjectured annihilation pole axiom is valid in the low energy particle-hole excitations. Secondly, we introduce a crossing relation, which establishes a connection between form factors with different excitation content. Typically, the crossing relation is a consequence of Lorentz invariance, but due to the finite energy density of the considered states, Lorentz invariance is broken. Nonetheless a crossing relation involving excitations with both particles and holes can established using the finite volume representation of the thermodynamic form factors. Finally, we demonstrate that the thermodynamic form factors satisfy a reparameterization invariance, an invariance which encompasses crossing. Reparameterization invariance exploits the fact that the details of the representation of the thermodynamic state are unimportant. In the course of developing these results, we demonstrate the internal consistency of the thermodynamic form factor bootstrap program in a number of ways. Finally, we provide explicit computations of form factors of conserved charges and densities with crossed excitations and show our results can be used to infer information about thermodynamic form factors in the Lieb-Liniger model.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Unlocking enhanced gas capture via core scrambling of porous-organic cages

The demand for low-cost, low-energy, and highly selective gas capture and separations is an ongoing driver of porous material development. Porous liquids have been identified as a promising gas separation material by creating permanent porosity in inorganic solvents through inclusion of nanoporous materials that sterically exclude solvent from their internal porosity. Among the nanoporous materials that can be used to form porous liquids, porous-organic cages (POCs) have been one of the most popular due to the inherent tunability of POCs. “Scrambled” POCs with varying functionalities on the POC vertices have been developed and incorporated into porous liquid compositions, increasing their gas adsorption capacity. An unexplored avenue to tailor the properties of porous liquids is through scrambling the functionality of the core of the POC. Here, therefore, we have synthesized a new POC, a CC3-OH derivative with scrambled hydroxides on the core and evaluated the impact on the CO 2 uptake capacity in silicon oil-based porous liquids. Core scrambling of the POC resulted in a twofold increase CO 2 adsorption capacity in the porous liquid, an emergent property that is a dramatic increase beyond a linear combination of the gas adsorption capacity of the neat solvent and the POC. Density functional theory modeling of the CC3 POC and its hydroxide-based derivatives identified that free rotation of the linker hydroxide allowed for forced interaction between the CO 2 molecule and the hydroxide in the pore window. Solvation of the POC may release scrambled core hydroxides from intramolecular bonding with a neighboring imine, allowing for increased gas uptake in the porous liquid over the neat POC. These results identify a key structural relationship of POCs that enables emergent properties in porous liquids and can guide future development of liquid phase gas capture and separation materials for environmental and industrial applications.

Gas capture↗
DOE OSTI2024

Coexistence of ferromagnetism and antiferromagnetic dimers in topological insulators

The addition of magnetic impurities in topological insulators (TIs) can drive ferromagnetic order that leads to quantum anomalous Hall transport well below the Curie temperature. The fragility of the quantized regime has been ascribed to the random nature of the magnetic moment distribution. Here, we refine this hypothesis by using inelastic neutron scattering and density-functional theory calculations to show that two antagonistic components define the magnetism in Mn-substituted SnTe, thereby limiting the effectiveness of dilute magnetic TIs. One component is strongly bound antiferromagnetic dimers that compete with ferromagnetic order. In conclusion, the other component consists of undimerized moments where ferromagnetic order develops via long-range interactions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Implications of Microstructure in Helium-Implanted Nanocrystalline Metals

Helium bubbles are known to form in nuclear reactor structural components when displacement damage occurs in conjunction with helium exposure and/or transmutation. If left unchecked, bubble production can cause swelling, blistering, and embrittlement, all of which substantially degrade materials and—moreover—diminish mechanical properties. On the mission to produce more robust materials, nanocrystalline (NC) metals show great potential and are postulated to exhibit superior radiation resistance due to their high defect and particle sink densities; however, much is still unknown about the mechanisms of defect evolution in these systems under extreme conditions. Here, the performances of NC nickel (Ni) and iron (Fe) are investigated under helium bombardment via transmission electron microscopy (TEM). Bubble density statistics are measured as a function of grain size in specimens implanted under similar conditions. While the overall trends revealed an increase in bubble density up to saturation in both samples, bubble density in Fe was over 300% greater than in Ni. To interrogate the kinetics of helium diffusion and trapping, a rate theory model is developed that substantiates that helium is more readily captured within grains in helium-vacancy complexes in NC Fe, whereas helium is more prone to traversing the grain matrices and migrating to GBs in NC Ni. Our results suggest that (1) grain boundaries can affect bubble swelling in grain matrices significantly and can have a dominant effect over crystal structure, and (2) an NC-Ni-based material can yield superior resistance to irradiation-induced bubble growth compared to an NC-Fe-based material and exhibits high potential for use in extreme environments where swelling due to He bubble formation is of significant concern.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Importance of Van der Waals Interactions in Hydrogen Adsorption on a Silicon-carbide Nanotube Revisited with vdW-DFT and Quantum Monte Carlo

Density functional theory (DFT) is a valuable tool for calculating adsorption energies toward designing materials for hydrogen storage. However, dispersion forces being absent from the local/semi-local theory, it remains unclear as to how the consideration of van der Waals (vdW) interactions affects such calculations. For the first time, we applied diffusion Monte Carlo (DMC) to evaluate the adsorption characteristics of a hydrogen molecule on a (5,5) armchair silicon-carbide nanotube (H 2 -SiCNT). Within the DFT framework, we benchmarked various exchange-correlation functionals, including those recently developed for treating dispersion or vdW interactions. We found that the vdW-corrected DFT methods agree well with DMC, whereas the local (semilocal) functional significantly over (under)-binds. Furthermore, we fully optimized the H 2 -SiCNT geometry within the DFT framework and investigated the correlation between the structure and charge density. The vdW contribution to the adsorption was found to be non-negligible at similar to 1 kcal/mol per hydrogen molecule, which amounts to 9-29% of the ideal adsorption energy required for hydrogen storage applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗