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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Incorporating Electronic Information into Machine Learning Potential Energy Surfaces via Approaching the Ground-State Electronic Energy as a Function of Atom-Based Electronic Populations

Machine learning (ML) approximations to density functional theory (DFT) potential energy surfaces (PESs) are showing great promise for reducing the computational cost of accurate molecular simulations, but at present, they are not applicable to varying electronic states, and in particular, they are not well suited for molecular systems in which the local electronic structure is sensitive to the medium to long-range electronic environment. With this issue as the focal point, we present a new machine learning approach called "BpopNN" for obtaining efficient approximations to DFT PESs. Conceptually, the methodology is based on approaching the true DFT energy as a function of electron populations on atoms; in practice, this is realized with available density functionals and constrained DFT (CDFT). Additionally, the new approach creates approximations to this function with neural networks. These approximations thereby incorporate electronic information naturally into a ML approach, and optimizing the model energy with respect to populations allows the electronic terms to self-consistently adapt to the environment, as in DFT. We confirm the effectiveness of this approach with a variety of calculations on LinHn clusters.

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

Third-Order Møller–Plesset Theory Made More Useful? The Role of Density Functional Theory Orbitals

The practical utility of Møller–Plesset (MP) perturbation theory is severely constrained by the use of Hartree–Fock (HF) orbitals. It has recently been shown that the use of regularized orbital-optimized MP2 orbitals and scaling of MP3 energy could lead to a significant reduction in MP3 error. In this work, we examine whether density functional theory (DFT)-optimized orbitals can be similarly employed to improve the performance of MP theory at both the MP2 and MP3 levels. We find that the use of DFT orbitals leads to significantly improved performance for prediction of thermochemistry, barrier heights, noncovalent interactions, and dipole moments relative to the standard HF-based MP theory. Indeed, MP3 (with or without scaling) with DFT orbitals is found to surpass the accuracy of coupled-cluster singles and doubles (CCSD) for several data sets. We also found that the results are not particularly functional sensitive in most cases (although range-separated hybrid functionals with low delocalization error perform the best). As such, MP3 based on DFT orbitals thus appears to be an efficient, noniterative O(N 6 ) scaling wave-function approach for single-reference electronic structure computations. Scaled MP2 with DFT orbitals is also found to be quite accurate in many cases, although modern double hybrid functionals are likely to be considerably more accurate.

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

Application of Self-Interaction Corrected Density Functional Theory to Early, Middle, and Late Transition States

Density functional theory (DFT)-based methods often significantly underpredict chemical reaction barriers compared with experiments because of the tendency of DFT to over-stabilize transition states with stretched bonds due to the impact of unphysical electron self-interaction. But many reactions have early or late transition states where the transition state geometry closely resembles the reactants or products, respectively. The role of self-interaction in those cases is not known. Here we compare the performance of DFT with and without self-interaction correction (SIC) for describing the hydrogenation of CO and CO 2 catalyzed by a Lewis acid-base pair incorporated onto an aromatic cluster, using CCSD(T) results for reference. The three elementary steps in these reactions consist of an early, a middle, and a late transition. Our results show that the Perdew-Zunger SIC (PZ-SIC), implemented in the Fermi-L Lowdin orbital SIC (FLO-SIC) approach, qualitatively improves the description of the forward and reverse reaction barriers relative to uncorrected DFT for the middle transition, but not the early or late transitions. By contrast, the local scaling SIC (LSIC) method, also implemented in the FLO-SIC framework, significantly improves the calculated barriers over DFT and PZ-SIC in all but one case. Lastly, the results also show how the FLO-SIC approach can provide insight into the bonding in aromatic systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Electrocatalytic Reduction of CO 2 to CO over Ag(110) and Cu(211) Modeled by Grand-Canonical Density Functional Theory

We report the results of modeling CO 2 reduction (CO 2 R) to CO over Ag(110) and Cu(211) surfaces at different applied potentials using grand-canonical density functional theory (GC-DFT), a method specifically designed to accurately model electrochemical systems. In addition to demonstrating GC-DFT’s ability to accurately model electrochemical processes, we also compare it with the computational hydrogen electrode (CHE) approach. GC-DFT predicts that the geometries of these reacting systems strongly depend on the applied potential and the Helmholtz free energies vary nonlinearly with the applied potential, which contradicts a central assumption of the CHE approach. The CHE approach neglects the change in the number of electrons on the electrode surface at different applied potentials, which reduces its accuracy as the potential changes from the potential of zero charge. Our results further demonstrate that the grand free energies of the reaction intermediates not only depend on the value of the applied potential but also on the metal surface type, adsorption site, and adsorbate. GC-DFT’s ability to predict the effect of the applied potential on adsorbate geometry enables it to evaluate different possible reaction mechanisms at different applied potentials. For instance, GC-DFT predicts that the first step of CO2R likely switches from proton-coupled electron transfer to sequential electron transfer and then proton transfer at more reducing potentials, a result that cannot be determined by the CHE because it assumes that all electron transfers are coupled to proton transfers and neglects the effect of the applied potential on the adsorbate geometry.

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

A Coverage Self-Consistent Microkinetic Model for Vapor-Phase Formic Acid Decomposition over Pd/C Catalysts

An iterative approach utilizing density functional theory (DFT, PW91-GGA)-informed mean-field microkinetic models and reaction kinetics experiments is used to determine the reaction mechanism and the active site for formic acid (HCOOH, FA) decomposition over a Pd/C catalyst. Models parametrized using DFT energetics on clean Pd(100) and Pd(111) required large corrections to the DFT energetics for capturing our experimental data. Further, both Pd(111) and Pd(100) models predicted a high coverage of adsorbed CO (CO*), inconsistent with the assumption of a clean surface at which the rate parameters for these models were calculated. To better represent the active site under reaction conditions and explicitly account for the presence of CO*, subsequent microkinetic models were formulated using DFT energetics that were calculated on partially (5/9 ML) CO*-covered Pd (111) and (100) facets. Upon parameter adjustment, the resultant 5/9 ML CO*-covered Pd(100) model, although consistent in terms of CO* coverage, was unable to capture the dehydration path measured in the experiments and was, therefore, deemed not to offer an accurate representation of the active site for FA decomposition over Pd/C. In contrast, a partially CO*-covered Pd(111) model was better at representing the catalytic active site, as in addition to being consistent in terms of CO* coverages, it required small adjustments of the DFT parameters to accurately capture the experimental data set (both dehydrogenation and dehydration). Our results suggest that the reaction occurs via the spectroscopically elusive carboxyl (COOH*) intermediate and that spectator CO*-assisted decomposition pathways play an important role under typical experimental conditions. In addition, our study highlights the importance of striving for coverage self-consistent microkinetic models and for including spectator-assisted mechanisms in order to develop an improved picture of the active site under reaction conditions.

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

Machine Learning Accelerated First-Principles Study of the Hydrodeoxygenation of Propanoic Acid

The complex reaction network of catalytic biomass conversions often involves hundreds of surface intermediates and thousands of reaction steps, greatly hindering the rational design of metal catalysts for these conversions. Here, we present a framework of machine learning (ML)-accelerated first-principles studies for the hydrodeoxygenation (HDO) of propanoic acid over transition metal surfaces. The microkinetic model (MKM) is initially parametrized by ML-predicted energies and iteratively improved by identifying the rate-determining species and steps (RDS), computing their energies by density functional theory (DFT), and reparameterizing the MKM until all the RDS are computed by DFT. The Gaussian process (GP) model performs significantly better than the linear ridge regression model for predicting both the adsorption free energies and transition state free energies. Parameterized with energies from the GP model, only 5–20% of the full reaction network has to be computed by DFT for the MKM to possess DFT-level accuracy for the TOF and dominant reaction pathway. While the linear ridge regression model performs worse than the GP model, its performance is greatly improved when only transition states are predicted by the regression model and adsorption energies are computed by DFT. Overall, we find that a high accuracy in adsorption free energies is more important for a reliable MKM than a high accuracy in TS free energies. Lastly, based on the GP model with GOH and GCHCHCO as catalyst descriptors, we build two-dimensional volcano plots in activity and selectivity that can help design promising alloy catalysts for HDO reactions of organic acids.

adsorption↗
DOE OSTI2026

Ripening of Rh Nanoparticle Catalysts in Reverse Water–Gas Shift via a Data-Driven Model Combining Physics, Theory, and Experiment

Degradation via sintering is an ongoing challenge that impedes the broad commercial success of supported metallic nanoparticle catalysts. To mitigate degradation via informed catalyst design and process operations, here we aim to disambiguate the underlying mechanisms of sintering by combining theory and experiment in a quantitative framework. While mechanistic sintering models exist, they only model a single sintering pathway, even though multiple sintering mechanisms can occur simultaneously or dominate at different stages of the process. Data-driven machine learning models have emerged as a means to represent complex processes through data regression. However, machine learning models have very large data needs and lack mechanistic insights due to their black-box encoding. To develop an interpretive model of catalyst degradation via sintering, we constructed a hybrid model combining mechanistic “physics-based” models and data-driven methods to obtain both reliable predictions and mechanistic insights regarding experimentally observed sintering phenomena. Focusing on nanoparticle sintering in the Rh–TiO 2 catalyst for the reverse water–gas shift (RWGS) reaction, the hybrid model couples a mechanistic term for Ostwald ripening with energy values calculated via density functional theory (DFT) with a parametric, data-driven discrepancy function term for unmodeled mechanisms. The hybrid model is trained using Bayesian inference with data collected from small-angle X-ray scattering (SAXS) in situ experiments wherein average nanoparticle diameter versus time was measured at three relevant operating temperatures. The calibrated hybrid model results show that an Ostwald ripening-only model parameterized with fixed DFT energies does not fully capture the time and temperature dependence of the SAXS-observed sintering kinetics, and that an additional functional contribution, or DFT energy calibration, is required to reconcile simulation and experiment. Analysis of the hybrid-model error confirms that the hybrid model outperforms both the purely mechanistic and purely data-driven alternatives in terms of expected predictive accuracy for time-evolving average particle sizes. Furthermore, the results support the hypothesis that the Ostwald ripening mechanism is less important for explaining the sintering phenomena as operating temperature increases under an assumed fixed DFT parameterization. This could be explained in one of two ways: either latent, unmodeled sintering mechanisms dominate at higher temperatures, or the DFT uncertainty increases with temperature. The proposed modeling approach directly links theory to experiments and simulations via a statistical hybrid modeling framework and can be extended to other catalytic systems to improve predictive models and mechanistic understanding.

Bayesian hybrid modeling↗
DOE OSTI2023

One-bond 13 C– 13 C spin-coupling constants in saccharides: a comparison of experimental and calculated values by density functional theory using solid-state 13 C NMR and X-ray crystallography

Methyl aldohexopyranosides were 13 C-labeled at contiguous carbons, crystallized, and studied by single-crystal X-ray crystallography and solid-state 13 C nuclear magnetic resonance (NMR) spectroscopy to examine the degree to which density functional theory (DFT) can calculate one-bond 13C–13C spin-coupling constants ( 1 J CC ) in saccharides with sufficient accuracy to permit their use in MA'AT analysis, a newly-reported hybrid DFT/NMR method that provides probability distributions of molecular torsion angles in solution (Zhang et al., J. Phys. Chem. B, 2017, 121, 3042–3058; Meredith et al., J. Chem. Inf. Model., 2022, 62, 3135–3141). Experimental 1 J CC values in crystalline samples of the doubly 13 C-labeled compounds were measured by solid-state 13 C NMR and compared to those calculated from five different DFT models: (1) 1 J CC values calculated from single structures identical to those observed in crystalline samples by X-ray crystallography (all atom refinement); (2) 1 J CC values calculated from the single structures in (1) but after Hirshfeld atom refinement (HAR); (3) 1 J CC values calculated from the single structures in (1) after DFT-optimization of hydrogen atoms only; and (4 and 5) 1 J CC values calculated in rotamers of torsion angle θ 2 (C1–C2–O2–O2H) or ω(C4–C5–C6–O6) from which either specific or generalized parameterized equations were obtained and used to calculate 1 J CC values in the specific θ 2 or ω rotamers observed in crystalline samples. Good qualitative agreement was observed between calculated 1 J CC values and those measured by solid-state 13 C NMR regardless of the DFT model, but in no cases were calculated 1 J CC values quantitative, differing (over-estimated) on average by 4–5% from experimental values. These findings, and those reported recently from solution NMR studies (Tetrault et al., J. Phys. Chem. B 2022, 126, 9506–9515), indicate that improvements in DFT calculations are needed before calculated 1 J CC values can be used directly as reliable constraints in MA'AT analyses of saccharides in solution.

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

Highly transferable atomistic machine-learning potentials from curated and compact datasets across the periodic table

Machine learning atomistic potentials trained using density functional theory (DFT) datasets allow for the modeling of complex material properties with near-DFT accuracy while imposing a fraction of its computational cost. The curation of the DFT datasets can be extensive in size and time-consuming to train and refine. In this study, we focus on addressing these barriers by developing minimalistic and flexible datasets for many elements in the periodic table regardless of their mass, electronic configuration, and ground state lattice. These DFT datasets have on average, ~4000 different structures and 27 atoms per structure, which we found sufficient to maintain the predictive accuracy of DFT properties and notably with high transferability. We envision these highly curated training sets as starting points for the community to expand, modify, or use with other machine learning atomistic potential models, whatever may suit individual needs, further accelerating the utilization of machine learning as a tool for material design and discovery.

42 ENGINEERING↗
DOE OSTI2022

Simulations of x-ray absorption spectra for CO desorbing from Ru(0001) with transition-potential and time-dependent density functional theory approaches

The desorption of a carbon monoxide molecule from a Ru(0001) surface was studied by means of X-ray Absorption Spectra (XAS) computed with Transition Potential (TP-DFT) and Time Dependent (TD-DFT) DFT methods. By unraveling the evolution of the CO electronic structure upon desorption, we observed that at 2.3 Å from the surface, the CO molecule has already predominantly gas-phase character. While C 1s XAS is quite insensitive to changes in the C–O bond length, the O 1s excitation is very sensitive with the π* coming down in energy upon CO bond stretching, which competes with the increase in orbital energy due to the repulsive interaction with the metallic surface. We show in a systematic way that the TP-DFT method can describe the XAS rather well at the endpoints (chemisorbed and gas phase) but is affected by artificial charge transfer and/or incorrect spin treatment in the transition region in cases like CO, where there are low-lying π* orbitals and large exchange interactions between the core 1s and valence-acceptor π* orbitals. As an alternative, we demonstrate by comparing with experimental data that a linear response approach using TD-DFT employing common exchange-correlation functionals and finite-size clusters can yield a good description of the spectral evolution of the 1s → π* transition with correct spin and gas-to-chemisorbed chemical shifts in good agreement with experiment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Describing adsorption of benzene, thiophene, and xenon on coinage metals by using the Zaremba–Kohn theory-based model

Semilocal (SL) density functional approximations (DFAs) are widely applied but have limitations due to their inability to incorporate long-range van der Waals (vdW) interaction. Non-local functionals (vdW-DF, VV10, and rVV10) or empirical methods (DFT+D, DFT+vdW, and DFT+MBD) are used with SL-DFAs to account for such missing interaction. The physisorption of a molecule on the surface of the coinage metals (Cu, Ag, and Au) is a typical example of systems where vdW interaction is significant. However, it is difficult to find a general method that reasonably describes both adsorption energy and geometry of even the simple prototypes of cyclic and heterocyclic aromatic molecules such as benzene (C 6 H 6 ) and thiophene (C 4 H 4 S), respectively, with reasonable accuracy. Here, we present an alternative scheme based on Zaremba–Kohn theory, called DFT+vdW-dZK. We show that unlike other popular methods, DFT+vdW-dZK and particularly SCAN+vdW-dZK give an accurate description of the physisorption of a rare-gas atom (xenon) and two small albeit diverse prototype organic molecules on the (111) surfaces of the coinage metals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Modeling dark- and light-induced crystal structures and single-crystal optical absorption spectra of ruthenium-based complexes that undergo SO 2 -linkage photoisomerization

A family of coordination complexes of the type [Ru(SO 2 )(NH 3 ) 4 X] m+ Y n – (m, n = 1 or 2) exhibit optical switching capabilities in their single-crystal states. This striking effect is caused by the light-induced formation of SO 2 -linkage photoisomers, which are metastable if kept at suitably cool temperatures. We modeled the dark- and light-induced states of these large crystalline complexes via plane-wave (PW)- and molecular-orbital (MO)-based density functional theory (DFT) and time-dependent DFT in order to calculate their structural and optical properties; the calculated results are compared with experimental data. We show that the PW-DFT-based periodic models replicate the structural properties of these complexes more effectively than the MO-DFT-based molecular-fragment models, observing only small deviations in key bond lengths relative to the experimentally derived crystal structures. The periodic models were also found to more effectively simulate trends seen in experimental optical absorption spectra, with optical absorbance and coverage of the visible region increasing with the formation of the photoinduced geometries. The contribution of the metastable photoisomeric species more heavily focuses on the lower-energy end of the spectra. Spectra generated from the molecular-fragment models are limited by the geometry of the fragment used and the number of excited-state roots considered in those calculations. In general, periodic models outperform the molecular-fragment models owing to their ability to better appreciate the periodic phenomena that are present in these crystalline materials as opposed to MO approaches, which are finite methods. We thus demonstrate that PW-DFT-based periodic models should be considered as a more than viable method for simulating the optical and electronic properties of these single-crystal optical switches.

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

Fermi–Löwdin orbital self-interaction correction of adsorption energies on transition metal ions

Density functional theory (DFT)-based descriptions of the adsorption of small molecules on transition metal ions are prone to self-interaction errors. Here, we show that such errors lead to a large over-estimation of adsorption energies of small molecules on Cu + , Zn + , Zn 2+ , and Mn + in local spin density approximation (LSDA) and Perdew, Burke, Ernzerhof (PBE) generalized gradient approximation calculations compared to reference values computed using the coupled-cluster with single, doubles, and perturbative triple excitations method. These errors are significantly reduced by removing self-interaction using the Perdew–Zunger self-interaction correction (PZ-SIC) in the Fermi–Löwdin Orbital (FLO) SIC framework. In the case of FLO-PBE, typical errors are reduced to less than 0.1 eV. Furthermore, analysis of the results using DFT energies evaluated on self-interaction-corrected densities [DFT(@FLO)] indicates that the density-driven contributions to the FLO-DFT adsorption energy corrections are roughly the same size in DFT = LSDA and PBE, but the total corrections due to removing self-interaction are larger in LSDA.

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

Polarization consistent dielectric screening in polarizable continuum model calculations of solvation energies

A polarization consistent framework, where dielectric screening is affected consistently in polarizable continuum model (PCM) calculations, is employed for the study of solvation energies. The computational framework combines a screened range-separated-hybrid functional (SRSH) with PCM calculations, SRSH-PCM, where dielectric screening is imposed in both PCM self-consistent reaction field (SCRF) iterations and the electronic structure Hamiltonian. We begin by demonstrating the impact of modifying the Hamiltonian to include such dielectric screening in SCRF iterations by considering the solutions of electrostatically embedded Hartree–Fock (HF) exact exchange equations. Long-range screened HF-PCM calculations are shown to capture properly the linear dependence of gap energy of frontier orbitals on the inverse of the dielectric constant, whereas unscreened HF-PCM orbital energies are fallaciously semi-constant with respect to the dielectric constant and, therefore, inconsistent with the ionization energy gaps. Similar trends affect density functional theory (DFT) calculations that aim to achieve predictive quality. Importantly, the dielectric screened calculations are shown to significantly affect DFT- and HF PCM-based solvation energies, where screened solvation energies are smaller compared to the unscreened values. Importantly, SRSH-PCM, therefore, appears to reduce the tendency of DFT-PCM to overestimate solvation energies, where we find the effect to increase with the dielectric constant and the polarity of the molecular solute, trends that enhance the quality of DFT-PCM calculations of solvation energy. Understanding the relationship of dielectric screening in the Hamiltonian and DFT-PCM calculations can ultimately benefit on-going efforts for the design of predictive and parameter free descriptions of solvation energies.

Chemistry↗
DOE OSTI2023

Impact of solvation on the GW quasiparticle spectra of molecules

First-principles calculations for electrochemistry require accurate treatment of both electronic structure and solvation. The perturbative GW approximation starting from density functional theory (DFT) calculations accurately models materials systems with varying dimensionality. Continuum solvation models enable efficient treatment of solvation effects in DFT calculations, but their applications with beyond-DFT electronic structure methods such as GW have been limited. Here, we introduce the frequency-dependent liquid polarizability from a nonlocal continuum solvation model in the screened Coulomb interaction of full-frequency GW calculations with a solvated DFT starting point. In this study, we show that the liquid screening contributions substantially reduce the HOMO–LUMO gap of molecules by 3–5 eV, while solvent effects on the DFT starting point negligibly impact the GW gap. The resulting framework facilitates the simultaneous electronic and solvation accuracy needed for first-principles electrochemistry.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Accelerating defect predictions in semiconductors using graph neural networks

First-principles computations reliably predict the energetics of point defects in semiconductors but are constrained by the expense of using large supercells and advanced levels of theory. Machine learning models trained on computational data, especially ones that sufficiently encode defect coordination environments, can be used to accelerate defect predictions. Here, we develop a framework for the prediction and screening of native defects and functional impurities in a chemical space of group IV, III–V, and II–VI zinc blende semiconductors, powered by crystal Graph-based Neural Networks (GNNs) trained on high-throughput density functional theory (DFT) data. Using an innovative approach of sampling partially optimized defect configurations from DFT calculations, we generate one of the largest computational defect datasets to date, containing many types of vacancies, self-interstitials, anti-site substitutions, impurity interstitials and substitutions, as well as some defect complexes. We applied three types of established GNN techniques, namely crystal graph convolutional neural network, materials graph network, and Atomistic Line Graph Neural Network (ALIGNN), to rigorously train models for predicting defect formation energy (DFE) in multiple charge states and chemical potential conditions. We find that ALIGNN yields the best DFE predictions with root mean square errors around 0.3 eV, which represents a prediction accuracy of 98% given the range of values within the dataset, improving significantly on the state-of-the-art. We further show that GNN-based defective structure optimization can take us close to DFT-optimized geometries at a fraction of the cost of full DFT. The current models are based on the semi-local generalized gradient approximation-Perdew–Burke–Ernzerhof (PBE) functional but are highly promising because of the correlation of computed energetics and defect levels with higher levels of theory and experimental data, the accuracy and necessity of discovering novel metastable and low energy defect structures at the PBE level of theory before advanced methods could be applied, and the ability to train multi-fidelity models in the future with new data from non-local functionals. The DFT-GNN models enable prediction and screening across thousands of hypothetical defects based on both unoptimized and partially optimized defective structures, helping identify electronically active defects in technologically important semiconductors.

Rahman, Md Habibur (ORCID:000000027705984X)↗
DOE OSTI2024

Time-dependent density functional theory with the orthogonal projector augmented wave method

The projector augmented wave (PAW) method of Blöchl linearly maps smooth pseudo wavefunctions to the highly oscillatory all-electron DFT orbitals. Compared to norm-conserving pseudopotentials (NCPP), PAW has the advantage of lower kinetic energy cutoffs and larger grid spacing at the cost of having to solve for non-orthogonal wavefunctions. We earlier developed orthogonal PAW (OPAW) to allow the use of PAW when orthogonal wavefunctions are required. In OPAW, the pseudo wavefunctions are transformed through the efficient application of powers of the PAW overlap operator with essentially no extra cost compared to NCPP methods. Previously, we applied OPAW to DFT. Here, we take the first step to make OPAW viable for post-DFT methods by implementing it in real-time time-dependent (TD) DFT. Using fourth-order Runge–Kutta for the time-propagation, we compare calculations of absorption spectra for various organic and biological molecules and show that very large grid spacings are sufficient, 0.6–0.7 bohr in OPAW-TDDFT rather than the 0.4–0.5 bohr used in traditional NCPP-TDDFT calculations. This reduces the memory and propagation costs by around a factor of 3. Our method would be directly applicable to any post-DFT methods that require time-dependent propagations such as the GW approximation and the Bethe–Salpeter equation.

Chemistry↗
DOE OSTI2026

Importance of finite-size corrections for accurate ab initio modeling of carrier capture at semiconductor defects: A case study of substitutional C N in GaN

In ab initio studies of carrier-capture processes in defective semiconductor materials, the single-effective-mode formalism and the static-coupling approximation have become the predominant theoretical approaches for determining carrier-capture coefficients. The single-mode formalism relies on accurate nonequilibrium defect energies obtained from density-functional theory (DFT), where required inputs are a series of configurationally displaced, defect-containing supercells obtained using an interpolative ansatz, and where the DFT outputs are corresponding total energies that have traditionally been postprocessed using a long-established ground-state formulation of finite-size corrections and defect-formation energies. This formulation remains commonly used even though the defects that form a configuration-coordinate (CC) diagram typically exist as structures that are displaced from the ground state. To remedy this inconsistency, Kumagai has recently proposed novel methods for implementing finite-size corrections specifically intended for DFT calculations of the defect energies used to construct CC diagrams and implement the single-mode formalism [Y. Kumagai, Phys. Rev. B 107, L220101 (2023)]. Kumagai's approach builds on the latest finite-size-correction methods introduced to describe vertical charge-state transitions for charge-localizing point defects in semiconductors and insulators [T. Gake et al., Phys. Rev. B 101, 020102 (2020); S. Falletta et al., Phys. Rev. B 102, 041115 (2020)]. The newly identified finite-size artifact treated in these studies is the polarization charge induced on a configurationally frozen defect and its subsequent interaction with a vertical transition in charge state. In this work, we evaluate Kumagai's proposed methodology by applying it in a high-precision DFT study of carrier capture by substitutional C N in GaN, a well-characterized and technologically relevant defect and material. We have rigorously calculated C N defect energies across various supercell sizes for each defect configuration and charge state on the hole-capture CC diagram of C N (𝑞=−1), enabling a direct comparison of the slopes of the defect energies versus inverse cell size with those predicted by Kumagai. The most consequential prediction of Kumagai's method is that these slopes distinctly vary as the square of the linear-interpolation parameter used to construct the nonequilibrium defect configurations. Our results quantitatively support this prediction. Moreover, with these new finite-size corrections and multiple-cell-size DFT calculations in place, we find that the classical energy barrier for hole capture by C N (𝑞=−1) in GaN decreases to 0.092–0.127 eV. This finding confirms the recent ≈ 0.1 eV prediction of Reshchikov based on the weak temperature dependence for hole capture observed in photoluminescence experiments [M. A. Reshchikov, J. Appl. Phys. 129, 121101 (2021)]. These results stand in stark contrast to previously calculated barriers of 0.486 and 0.73 eV, which also used the single-mode formalism but were obtained by instead using ground-state-based finite-size corrections. Our reduced classical barrier for capture increases the temperature-dependent hole-capture coefficient of a C N (𝑞=−1) defect by more than two to four orders of magnitude for temperatures of 100–600 K, compared to the previous 0.486 eV results. While other defects may not be as dramatically affected as here, we suggest that incorporating proper finite-size corrections for the vertical-transition-like states embedded within CC diagrams is an essential, yet previously unrecognized, component of accurate modeling of carrier-capture when using the single-effective-mode formalism.

dielectric properties↗