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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Machine learning approach for vibronically renormalized electronic band structures

Here, we present a machine learning (ML) method for efficient computation of vibrational thermal expectation values of physical properties from first principles. Our approach is based on the nonperturbative frozen phonon formulation in which stochastic Monte Carlo algorithm is employed to sample configurations of nuclei in a supercell at finite temperatures based on a first-principles phonon model. A deep-learning neural network is trained to accurately predict physical properties associated with sampled phonon configurations, thus bypassing the time-consuming ab initio calculations. To incorporate the point-group symmetry of the electronic system into the ML model, group-theoretical methods are used to develop a symmetry-invariant descriptor for phonon configurations in the supercell. We apply our ML approach to compute the temperature dependent electronic energy gap of silicon based on density functional theory (DFT). We show that, with less than a hundred DFT calculations for training the neural network model, an order of magnitude larger number of sampling can be achieved for the computation of the vibrational thermal expectation values. Our work highlights the promising potential of ML techniques for finite temperature first-principles electronic structure methods.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Coupled Process/Device Modeling and Point Defect Engineering of Cu(In,Ga)Se 2 Solar Cells

Point defects directly impact solar cell device performance by limiting the carrier lifetime. In this work, density functional theory calculations are first used to determine the formation energy and diffusion energy barriers of dominant defects in Cu(In,Ga)Se 2 . Next, continuum reaction–diffusion models are developed to analyze the redistribution of defects during manufacturing processes. We estimate defect capture cross sections using a first-principles-based approach. Here, these cross sections are combined with our calculated defect profiles and trap energy levels to parameterize a Shockley–Read–Hall recombination model, which we implement into a device simulator to predict carrier lifetimes and device performance. In that way, a predictive technology computer aided design model is built to predict and optimize the performance of Cu(In,Ga)Se 2 solar cells.

14 SOLAR ENERGY↗
DOE OSTI2025

Wide-ranging predictions of new stable compounds powered by recommendation engines

The computational search for new stable inorganic compounds is faster than ever, thanks to high-throughput density functional theory (DFT). However, stable compound searches remain highly expensive because of the enormous search space and the cost of DFT calculations. To aid these searches, recommendation engines have been developed. We conduct a systematic comparison of the performance of previously developed recommendation engines, specifically ones based on elemental substitution, data mining, and neural network prediction of formation enthalpy. After identifying ways to improve the recommendation engines, we find the neural network to be superior at recommending stable Heusler compounds. Armed with improved recommendation engines, we identify tens of thousands of compounds that are stable at zero temperature and pressure, now available in the Open Quantum Materials Database. We summarize this diverse pool of compounds, including the elusive mixed anion compounds, and two of their many applications: thermoelectricity and solar thermochemical fuel production.

Science & Technology - Other Topics↗
NASA NTRS2014

Computational Design of Materials: Planetary Entry to Electric Aircraft and Beyond

NASA's projects and missions push the bounds of what is possible. To support the agency's work, materials development must stay on the cutting edge in order to keep pace. Today, researchers at NASA Ames Research Center perform multiscale modeling to aid the development of new materials and provide insight into existing ones. Multiscale modeling enables researchers to determine micro- and macroscale properties by connecting computational methods ranging from the atomic level (density functional theory, molecular dynamics) to the macroscale (finite element method). The output of one level is passed on as input to the next level, creating a powerful predictive model.

Materials Design↗
DOE OSTI2025

Raman Digital Twin of Monolayer Janus Transition Metal Dichalcogenides

Monolayer transition metal dichalcogenides (TMDs) are a key class of two-dimensional (2D) materials with broad technological potential. Their Janus counterparts exhibit unique properties due to broken out-of-plane symmetry and further enrich the functionalities of TMDs. However, experimental synthesis and identification of Janus TMDs remain challenging. It is thus highly desirable to have a rapid, simple, and in situ characterization technique to monitor, in real time, the conversion process from the parent to Janus structure. Raman spectroscopy stands out for such a task as it is a powerful, nondestructive, and very commonly used tool to characterize 2D materials both in situ and ex situ. To realize the full potential of Raman spectroscopy on rapid characterization of Janus TMDs, we present a computational “Raman digital twin” library for various monolayer Janus TMDs in both 2H and Td phases. We focus on group-6 TMDs: MoS 2 , WS 2 , MoSe 2 , WSe 2 , MoTe 2 , WTe 2 and their Janus variants: MoSSe, MoSTe, MoSeTe, WSSe, WSTe, and WSeTe. Using first-principles density functional theory (DFT), we calculate their vibrational properties and predict distinct Raman fingerprints. These phonon and Raman signatures reflect each material’s structural symmetry and atomic composition, enabling clear identification via Raman spectroscopy. Our theoretical work supports experimental efforts by providing benchmarks for material identification, structural analysis, and quality control. In conclusion, the computational library expedites the discovery and development of Janus 2D materials, facilitating tighter integration between theoretical predictions and experimental validation.

Chalcogenides↗
DOE OSTI2025

Machine learned potential for high-throughput phonon calculations of metal—organic frameworks

Metal–organic frameworks (MOFs) are highly porous and versatile materials studied extensively for applications such as carbon capture and water harvesting. However, computing phonon-mediated properties in MOFs, like thermal expansion and mechanical stability, remains challenging due to the large number of atoms per unit cell, making traditional Density Functional Theory (DFT) methods impractical for high-throughput screening. Recent advances in machine learning potentials have led to foundation atomistic models, such as MACE-MP-0, that accurately predict equilibrium structures but struggle with phonon properties of MOFs. In this work, we developed a workflow for computing phonons in MOFs within the quasi-harmonic approximation with a fine-tuned MACE model, MACE-MP-MOF0. The model was trained on a curated dataset of 127 representative and diverse MOFs. The fine-tuned MACE-MP-MOF0 improves the accuracy of phonon density of states and corrects the imaginary phonon modes of MACE-MP-0, enabling high-throughput phonon calculations with state-of-the-art precision. The model successfully predicts thermal expansion and bulk moduli in agreement with DFT and experimental data for several well-known MOFs. These results highlight the potential of MACE-MP-MOF0 in guiding MOF design for applications in energy storage and thermoelectrics.

Elena, Alin Marin↗
DOE OSTI2022

Spectral quadrature for the first principles study of crystal defects: Application to magnesium

In this work, we present an accurate and efficient finite-difference formulation and parallel implementation of Kohn-Sham Density (Operator) Functional Theory (DFT) for non periodic systems embedded in a bulk environment. Specifically, employing non-local pseudopotentials, local reformulation of electrostatics, and truncation of the spatial Kohn-Sham Hamiltonian, and the Linear Scaling Spectral Quadrature method to solve for the pointwise electronic fields in real-space and the non-local component of the atomic force, we develop a parallel finite difference framework suitable for distributed memory computing architectures to simulate non-periodic systems embedded in a bulk environment. Choosing examples from magnesium-aluminum alloys, we first demonstrate the convergence of energies and forces with respect to spectral quadrature polynomial order, and the width of the spatially truncated Hamiltonian. Next, we demonstrate the parallel scaling of our framework, and show that the computation time and memory scale linearly with respect to the number of atoms. Next, we use the developed framework to simulate isolated point defects and their interactions in magnesium-aluminum alloys. Our findings conclude that the binding energies of divacancies, Al solute-vacancy and two Al solute atoms are anisotropic and are dependent on cell size. Furthermore, the binding is favorable in all three cases.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Machine-learning enabled thermodynamic model for the design of new rare-earth compounds

We employ a descriptor based machine-learning approach to assess the effect of chemical alloying on formation-enthalpy of rare-earth intermetallics. Application of machine-learning approaches in rare-earth intermetallic design have been sparse due to limited availability of reliable datasets. In this work, we developed an ‘in-house’ rare-earth database with more than 600 + compounds, each entry was populated with formation enthalpy and related atomic features using high-throughput density-functional theory (DFT). The SISSO (sure independence screening and sparsifying operator) based machine-learning method with meaningful atomic features was used for training and testing the formation enthalpies of rare earth compounds. The complex lattice function coupled with the machine-learning model was used to explore the effect of transition metal alloying on the energy stability of Ce based cubic Laves phases (MgCu 2 type). The SISSO predictions show good agreement with high-fidelity DFT calculations and X-ray powder diffraction measurements. Our study provides quantitative guidance for compositional considerations within a machine-learning model and discovering new metastable materials. The electronic-structure of Ce-Fe-Cu based compound was also analyzed to get an in-depth understanding of the electronic origin of phase stability. The interpretable analytical models in combination with density-functional theory and experiments provide a fast and reliable design guide for discovering technologically useful materials.

36 MATERIALS SCIENCE↗
DOE OSTI2021

On the structure sensitivity of and CO coverage effects on formic acid decomposition on Pd surfaces

Using density functional theory calculations, the Pd-catalyzed vapor-phase formic acid decomposition was studied, with a focus on the structure sensitivity and CO coverage effects. A comprehensive reaction network was developed on both the (111) and (100) facets of Pd, at CO coverages of 0 and 5/9 monolayer (ML). Pd(100) was determined to be more reactive than Pd(111) at both CO coverages. The introduction of 5/9 ML CO decreased the activity of both facets significantly, due to destabilization of the surface intermediates and transition states on the CO-decorated surfaces. Three reaction pathways were explored on the clean surfaces: the formate (HCOO) pathway, the carboxyl (COOH) pathway leading to the formation of CO 2 , and the COOH pathway leading to the formation of CO (COOH→CO). Based on the DFT-derived energetics alone, it appears that all three pathways contribute to the reaction on clean Pd, whereas the presence of 5/9 ML of CO inhibits the HCOO pathway on both facets and favors the COOH→CO pathway on the (111) facet, but the COOH→CO 2 one on the (100) facet. Moreover, at high CO coverages, alternative spectator CO-assisted adsorbate decomposition pathways were discovered, which could potentially play a role in formic acid decomposition on Pd catalysts under realistic reaction conditions.

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

Theoretical and Experimental Insights into CO 2 Capture and Methanation over Amine-Grafted Ru-Based Catalysts

Carbon capture and storage (CCS) technologies, along with CO 2 capture and conversion methods, have emerged as crucial research areas to address rising CO 2 emissions. In this study, we seek to understand the mechanistic role of amines in enabling lower-energy pathways for CO 2 conversion. Our research focuses on the development and analysis of dual-functional materials (DFMs) engineered for the reactive capture and conversion (RCC) of CO 2 into methane, utilizing Ru catalysts grafted with amine groups. We employ Density Functional Theory (DFT) calculations using methylamine as a model amine to investigate the impact of amine groups on CO 2 methanation on a Ru(0001) surface, both in the presence and absence of amine groups. The amine ligand alters the carbon coordination environment, promoting direct C–O dissociation and potentially destabilizing the CO* adsorbate, thereby reducing the risk of CO poisoning. Additionally, we observe a preference for hydrogenation, although it becomes more energetically uphill in the amine-bound scenario. Our experiments, however, report similar CO 2 conversion and CH 4 production rates over the synthesized catalysts “Ru/TiO 2 ” and the amine (N-(2-aminoethyl)-3-aminoproplytrimethoxysilane (“diaminosilane”)) deposited catalyst “Diamine−Ru/TiO 2 ”. By constructing comparative reaction-free energy diagrams and performing microkinetic modeling (MKM) simulations, we link our theoretical findings with experimentally observed CO 2 uptake, conversion, and methane production rates. A microkinetic model was employed to investigate the anomaly, showing reduced amine–carbon complex coverage and increased CO 2 coverage at all temperatures. The MKM simulations consistently confirmed these trends. In conclusion, this comprehensive approach offers key insights into the role of the amine-CO 2 bond in methanation, highlighting a pathway toward lower-energy, more efficient CO 2 capture and conversion processes.

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

Machine-learning Kohn–Sham potential from dynamics in time-dependent Kohn–Sham systems

Abstract The construction of a better exchange-correlation potential in time-dependent density functional theory (TDDFT) can improve the accuracy of TDDFT calculations and provide more accurate predictions of the properties of many-electron systems. Here, we propose a machine learning method to develop the energy functional and the Kohn–Sham potential of a time-dependent Kohn–Sham (TDKS) system is proposed. The method is based on the dynamics of the Kohn–Sham system and does not require any data on the exact Kohn–Sham potential for training the model. We demonstrate the results of our method with a 1D harmonic oscillator example and a 1D two-electron example. We show that the machine-learned Kohn–Sham potential matches the exact Kohn–Sham potential in the absence of memory effect. Our method can still capture the dynamics of the Kohn–Sham system in the presence of memory effects. The machine learning method developed in this article provides insight into making better approximations of the energy functional and the Kohn–Sham potential in the TDKS system.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Atomistic Mechanism Underlying the Si ( 111 ) - ( 7 × 7 ) Surface Reconstruction Revealed by Artificial Neural-Network Potential

The 7×7 reconstruction of the Si(111) surface represents arguably the most fascinating surface reconstruction so far observed in nature. Yet, the atomistic mechanism underpinning its formation remains unclear after it was discovered sixty years ago. Experimentally, it is observed post priori so that analysis of its formation mechanism can only be carried out in analogy with archaeology. Theoretically, density-functional theory (DFT) correctly predicts the Si(111)-(7×7) ground state but is impractical to simulate its formation process; while empirical potentials failed to produce it as the ground state. Developing an artificial neural-network potential of DFT quality, we carried out accurate large-scale simulations to unravel the formation of the Si(111)-(7×7) surface. We reveal a possible step-mediated atom-pop rate-limiting process that triggers massive nonconserved atomic rearrangements, most remarkably, a critical process of collective vacancy diffusion that mediates a sequence of selective dimer, corner-hole, stacking-fault, and dimer-line pattern formation, to fulfill the 7×7 reconstruction. Our findings may not only solve the long-standing mystery of this famous surface reconstruction but they also illustrate the power of machine learning in studying complex structures.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

Hierarchical screening for Li-based solid electrolytes using fast, interpretable machine-learned potentials

Li-based solid-state electrolyte materials enable safer, all-solid-state batteries but the computational search for candidates with favorable stability and Li-ion conductivity is challenging due to the size of the search space and the cost of evaluating transport properties with ab initio methods. The prohibitive cost of high-throughput screening with DFT has lead to the development of surrogate models using geometric analysis, empirical potentials, and descriptors for ionic transport. Here, I will discuss a hierarchical screening approach for identifying promising materials using a combination of density functional theory, bond-valence methods, and machine learning potentials generated with the Ultra-Fast Force Fields (UF3) framework. We show how the inexpensive bond-valence method can be used to guide the generation of training samples for machine learning, in addition to filtering candidates. Finally, we apply the hierarchical workflow to screen for ionic conductivity across a database of Li-containing compounds.

Materials discovery↗
NASA NTRSDate not supplied

Hierarchical Screening for Li-Based Solid Electrolytes Using Fast, Interpretable Machine-Learned Potentials

Li-based solid-state electrolyte materials enable safer, all-solid-state batteries but the computational search for candidates with favorable stability and Li-ion conductivity is challenging due to the size of the search space and the cost of evaluating transport properties with ab initio methods. The prohibitive cost of high-throughput screening with DFT has lead to the development of surrogate models using geometric analysis, empirical potentials, and descriptors for ionic transport. Here, I will discuss a hierarchical screening approach for identifying promising materials using a combination of density functional theory, bond-valence methods, and machine learning potentials generated with the Ultra-Fast Force Fields (UF3) framework. We show how the inexpensive bond-valence method can be used to guide the generation of training samples for machine learning, in addition to filtering candidates.

Materials discovery↗
DOE OSTI2025

Identifying Band Inversions in Topological Materials Using Diffusion Monte Carlo

Topological insulators are characterized by insulating bulk states and robust metallic surface states. Band inversion is a hallmark of topological insulators. At time-reversal invariant points in the Brillouin zone, spin–orbit coupling (SOC) induces a swapping of orbital character at the bulk band edges. Reliably detecting band inversion in solid-state systems with many-body methods would aid in identifying possible candidates for spintronics and quantum computing applications and improve our understanding of the physics behind topologically nontrivial systems. Density functional theory (DFT) methods are a well-established means of investigating these interesting materials due to their favorable balance of computational cost and accuracy but often struggle to accurately model the electron–electron correlations present in the many materials containing heavier elements. In this work, we develop a novel method to detect band inversion within continuum quantum Monte Carlo (QMC) methods that can accurately treat the electron correlation and spin–orbit coupling that are crucial to the physics of topological insulators. Our approach applies a momentum-space-resolved atomic population analysis throughout the first Brillouin zone utilizing the Löwdin method and the one-body reduced density matrix produced with diffusion Monte Carlo (DMC). We integrate this method into QMCPACK, an open source ab initio QMC package, so that these ground-state methods can be used to complement experimental studies and validate prior DFT work on predicting the band structures of correlated topological insulators. Here, we demonstrate this new technique on the topological insulator bismuth telluride, which displays band inversion between its Bi-p and Te-p states at the Γ-point. We show an increase in charge on the bismuth-p orbital and a decrease in charge on the tellurium-p orbital when comparing band structures with and without SOC. Additionally, we use our method to compare the degree of band inversion present in monolayer Bi 2 Te 3 , which has no interlayer van der Waals interactions, to that seen in the bilayer and bulk. The method presented here will enable future many-body studies of band inversion that can shed light on the delicate interplay between correlation and topology in correlated topological materials.

Band structure↗
NASA NTRS1992

Self-absorption theory applied to rocket measurements of the nitric oxide (1, 0) gamma band in the daytime thermosphere

Observations of the UV fluorescent emissions of the NO (1, 0) and (0, 1) gamma bands in the lower-thermospheric dayglow, made with a sounding rocket launched on March 7, 1989 from Poker Flat, Alaska, were analyzed. The resonant (1, 0) gamma band was found to be attenuated below an altitude of about 120 km. A self-absorption model based on Holstein transmission functions was developed for the resonant (1, 0) gamma band under varying conditions of slant column density and temperature and was applied for the conditions of the rocket flight. The results of the model agreed with the measured attenuation of the band, indicating the necessity of including self-absorption theory in the analysis of satellite and rocket limb data of NO.

Eparvier, F. G.↗
DOE OSTI2022

From ground to excited electronic state dynamics of electron and ion irradiated graphene nanomaterials

Microscopes utilizing convergent electron and ion beams are emerging as powerful tools for both imaging and manipulating two-dimensional materials with atomic resolution, allowing the ultimate limits of nanofabrication to be realized. In this chapter, we detail the use of time-dependent electronic structure theory to determine the excited state properties and reactivity of functionalized graphene nanostructures. A time-dependent density functional theory treatment of electronic excitations of materials is presented, with specific emphasis on predicting the position-dependent electronic response of two-dimensional nanomaterials to electron beams. The method is demonstrated in a study highlighting the important role that electronic excitation can play in opening reaction pathways relevant to atomically precise defect manipulation in graphene. Finally, we provide some perspective on future development directions for methods of simulating nonequilibrium electronic and vibrational dynamics induced by electron/ion beams.

Lingerfelt, David↗
DOE OSTI2022

Artificial intelligence inferred microstructural properties from voltage–capacity curves

Abstract The quantification of microstructural properties to optimize battery design and performance, to maintain product quality, or to track the degradation of LIBs remains expensive and slow when performed through currently used characterization approaches. In this paper, a convolution neural network-based deep learning approach (CNN) is reported to infer electrode microstructural properties from the inexpensive, easy to measure cell voltage versus capacity data. The developed framework combines two CNN models to balance the bias and variance of the overall predictions. As an example application, the method was demonstrated against porous electrode theory-generated voltage versus capacity plots. For the graphite|LiMn $$_2$$ 2 O $$_4$$ 4 chemistry, each voltage curve was parameterized as a function of the cathode microstructure tortuosity and area density, delivering CNN predictions of Bruggeman’s exponent and shape factor with 0.97 $$R^2$$ R 2 score within 2 s each, enabling to distinguish between different types of particle morphologies, anisotropies, and particle alignments. The developed neural network model can readily accelerate the processing-properties-performance and degradation characteristics of the existing and emerging LIB chemistries.

25 ENERGY STORAGE↗