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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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At least 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

New Methods for Predicting Non-Born-Oppenheimer Chemistry

Current methods for modeling non-adiabatic molecular dynamics face fundamental limitations when treating geometric phase effects: quantum mechanical phenomena where nuclear wavepackets acquire phase shifts when encircling conical intersections. Existing approaches either neglect these effects entirely or rely on potential energy surfaces arising from the Born-Oppenheimer approximation, which introduce artificial singularities and can overestimate geometric phase contributions. This project developed a new theoretical framework based on exact factorization (XF) methods to overcome these limitations. We derived mathematical formulations for hybrid quantum-classical XF dynamics that selectively treat critical nuclear degrees of freedom quantum mechanically while propagating others classically. This approach addresses the computational intractability that has previously limited exact methods to toy systems. Key innovations include a new approach to systematically identifying nuclear coordinates requiring quantum treatment, as well as novel implementation strategies that interface with existing quantum chemistry codes. The project also developed a proof-of-concept code for treating Jahn-Teller systems and creation of educational materials on non-adiabatic dynamics geared at the graduate level. The theoretical framework developed will enable future systematically improvable calculations of nuclear quantum effects in realistic molecular systems, filling a critical gap in non-adiabatic dynamics methods. This foundation supports future development of predictive tools for designing energy-relevant photochemical processes where quantum coherence effects may be exploited to control reaction outcomes.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2025

Computationally Guided and Experimentally Validated Design of Custom Chelators for Critical Mineral Recovery

Selective, high throughput separation of target critical metals from complex environments such as fly ash leachates and mining process streams presents a significant challenge for economical production. Custom chelators and sorbents are an attractive technology for selective metal extraction, however it can be difficult to predict their performance, and significant experimental efforts are often required to develop chelating technologies. Here, we present a computational strategy focused on modelling chelator-metal binding interactions and benchmark these results versus experimental data. A computational pipeline combining forcefield, semiempirical, and meta-GGA methods with a thermodynamic framework optimized for error cancellation has been developed to predict binding energies of chelator complexes towards critical mineral recovery applications. This approach, originally validated on [2.2.2] cryptates binding mono- and divalent cations, demonstrated robust predictive capabilities with an R2 of 0.850 against experimental aqueous binding energies. The workflow includes metadynamics for exploring high-dimensional potential energy surfaces and a cluster-continuum model for accurate yet computationally efficient solvation modeling. Error cancellation between solvation energies of free and chelator-coordinated ions enables faster convergence, even with finite cluster sizes. Initial studies on the cryptates revealed consistent metal-ligand coordination patterns, with systematic variations influenced by ion size and charge, highlighting key structural features linked to binding selectivity. Further studies of a proprietary chelator have resulted in identification of previously unreported selectivity towards economically significant metals, which in-house experiments have confirmed, demonstrating the feasibility of this approach. By applying this methodology to new chelators targeting critical minerals such as lithium, cobalt, nickel and other strategic metals, we aim to accelerate the discovery of next-generation chelators for efficient recovery, recycling, and separation processes. This computational framework serves as the backbone of a high-throughput design pipeline tailored for sustainable resource utilization and may be applied to a wide range of systems to meet experimental needs.

computational materials↗
DOE OSTI2026

Investigation of the Effect of Framework Flexibility on Adsorption in SIFSIX-3-Cu using a Machine-Learned Force Field

Metal-organic frameworks (MOFs) are a promising class of adsorbents. The performance of MOF sorbents relies on high selectivity and low regeneration energy. This work focuses on the use of machine learned force fields (MLFFs) to model adsorption in a flexible MOF, SIFSIX-3-Cu. A DeePMD-based MLFF was trained to reproduce DFT (PBE+D3) energies, forces, and stresses, using an iterative sampling scheme combining sampling based on molecular dynamics, Monte Carlo, and geometry optimization to capture both attractive and repulsive regions of the potential energy surface. Flexibility of the MOF was explicitly included in this model. Hybrid Monte Carlo/molecular dynamics (MC/MD) simulations using the MLFF predicted adsorption isotherms in good agreement with experimental data for a range of pressures (40 Pa – 104 Pa) in contrast to rigid models, which overpredict CO2 adsorption at low pressures. The improvement was the result of a description of the variability of fluorine-fluorine diagonal distances at adsorption sites. This detailed description of flexibility afforded by the MLFF resulted in more accurate predictions adsorption isotherms when compared to the experimentally measured values. These results underscore the importance of including framework flexibility when modeling adsorption phenomena in MOFs, particularly for low pressure applications and provide a robust procedure for training MLFF models for MOFs.

Atomistic Simulation↗
DOE OSTI2022

Inefficient intramolecular vibrational energy redistribution for the H + HO2 reaction and negative internal energy dependence for its rate constant

Quasiclassical trajectories (QCT) and newly constructed global potential energy surfaces are used to compute thermal and nonthermal rate constants for the H + HO 2 reaction. The thermal QCTs rate constants are up to 50% smaller than transition state theory (TST) rate constants based on the same level of electronic structure theory. This reduction is demonstrated to result from inefficient intramolecular vibrational energy redistribution (IVR) in the transient H 2 O 2 well, with a significant fraction of trajectories that reach the H 2 O 2 well promptly dissociating back to reactants instead of via the heavily statistically favored 2OH channel. The nonstatistical reduction factor, κ IVR , that quantifies this effect is shown to increase in importance with temperature, with κ IVR = 0.81 at 300 K and 0.47 at 2500 K. Finally, we show that inefficient IVR causes H + HO 2 rate constants mediated by H 2 O 2 to depend inversely on the initial vibrational excitation of HO 2 .

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

Hybrid Quantum-Classical Neural Network for Calculating Ground State Energies of Molecules

We present a hybrid quantum-classical neural network that can be trained to perform electronic structure calculation and generate potential energy curves of simple molecules. The method is based on the combination of parameterized quantum circuits and measurements. With unsupervised training, the neural network can generate electronic potential energy curves based on training at certain bond lengths. To demonstrate the power of the proposed new method, we present the results of using the quantum-classical hybrid neural network to calculate ground state potential energy curves of simple molecules such as H 2 , LiH, and BeH 2 . The results are very accurate and the approach could potentially be used to generate complex molecular potential energy surfaces.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2023

A Theoretical Study of NH 2 Radical Reactions with Propane and Its Kinetic Implications in NH 3 -Propane Blends’ Oxidation

The reaction of NH 2 radicals with C 3 H 8 is crucial for understanding the combustion behavior of NH 3 /C 3 H 8 blends. In this study, we investigated the temperature dependence of the rate coefficients for the hydrogen abstraction reactions of C 3 H 8 by NH 2 radicals using high-level theoretical approaches. The potential energy surface was constructed at the CCSD(T)/cc-pV(T, Q)//M06-2X/aug-cc-pVTZ level of theory, and the rate coefficients were computed using conventional transition state theory, incorporating the corrections for quantum tunneling and hindered internal rotors (HIR). The computed rate coefficients showed a strong curvature in the Arrhenius behavior, capturing the experimental literature data well at low temperatures. However, at T > 1500 K, the theory severely overpredicted the experimental data. The available theoretical studies did not align with the experiment at high temperatures, and the possible reasons for this discrepancy are discussed. At 300 K, the reaction of NH 2 with C 3 H 8 predominantly occurs at the secondary C-H site, which accounts for approximately 95% of the total reaction flux. However, the hydrogen abstraction reaction at the primary C-H site becomes the dominant reaction above 1700 K. A composite kinetic model was built, which incorporated the computed rate coefficients for NH 2 + C 3 H 8 reactions. The importance of NH 2 + C 3 H 8 reactions in predicting the combustion behavior of NH 3 /C 3 H 8 blends was demonstrated by kinetic modeling.

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

Raw Data for: Ultra-Confined Environments May Restrict the Possible Configurations of Supported Metal Complexes

Raw NMR data (1D 2H spectrum and pseudo-2D 2H CODEX spectra) in Bruker topspin format. Raw DRIFTS IR data as text files. Results from the DFT potential energy surface scans (coordinates are in the supporting information of the main publication). Raw BET nitrogen physisorption isotherms and the determined more size distributions.

Perras, Frederic A [Ames National Laboratory]↗
DOE OSTI2020

From Atom to Engine: Understanding Fundamental Effects of Structure on Combustion Using Tandem Experiment and Computation

The ability to tie structural features to a fuel candidate’s ignition properties provides a path for rational design of advantaged fuels that facilitate higher-efficiency, lower-emitting combustion in engines. The time evolution of a fuel’s radical population directly correlates with global combustion parameters, and because the generated radicals depend on the initial structure and reaction pathways of the fuel molecule, it follows that the molecular structure of a fuel has a direct impact on this cascade, and therefore salient ignition-tied fuel properties. This work aims to understand the impact of structural features in the context of global combustion properties for direct application to emerging engine technologies. Specifically, we highlight the ability to identify potential fuel blendstocks for specific engine strategies by examining and elucidating the radical cascade tied to ignition. A representative subset of alcohols possessing a variety of structural features (e.g. branching, chain length) was selected for study using a robust combination of experimental and computational methods. The approach begins with identifying short-lived radicals and likely abstraction sites upon pyrolysis in a microreactor coupled with photoionization mass spectrometry for direct detection of product species. In tandem, electronic structure calculations were performed to calculate the relevant potential energy surfaces for fuel pyrolysis as well as the subsequent O2 addition-isomerization reactions, which govern ignition behavior. Furthermore, the theoretical energetic and ro-vibrational data are used to calculate rate constants for direct ignition modeling to explore how structural effects impact NTC behavior. The microreactor data combined with electronic structure theory calculations provide a well-informed picture of the reactions relevant to ignition for each fuel in this study, as well as the most likely radicals generated under engine conditions. This thorough fundamental picture of fuel decomposition and ignition chemistry is used to correlate the observed ignition behavior to molecular structural effects.

advanced biofuels↗
DOE OSTI2026

Transition-state lattice modes and the breakdown of adiabatic tunneling for hydrogen and deuterium in bcc Nb

Interstitial hydrogen and deuterium in body-centered-cubic metals constitute archetypal quantum tunneling systems. Their relevance has been renewed by the connection between hydrogenic tunneling in Nb and defect-induced decoherence in superconducting qubits, motivating a predictive microscopic theory. Existing theoretical treatments invoke an adiabatic separation between the light interstitial and the host lattice, an assumption whose validity has not been rigorously established for hydrogenic species. Here, we show that the experimentally measured tunnel splittings of O-trapped H and D in bcc Nb are quantitatively reproduced only within a five-dimensional (5D) Lattice-Renormalized Born-Oppenheimer (LRBO) framework. This approach treats three interstitial modes and two judiciously selected lattice modes, which includes a transition-state mode, on equal quantum footing. By recasting nested Born-Oppenheimer hierarchies within this same formalism and benchmarking against modern \textit{ab initio} potential energy surfaces, we show that adiabatic separation of the light particle from lattice dynamics is satisfied only in the positive-muon ($μ^{+}$) mass limit. In contrast, tunneling for H and D is fundamentally a collective, nonadiabatic process mediated by anharmonic lattice couplings. Finally, we show that the breakdown of adiabaticity can be anticipated from simple energy estimates involving the ground-state light-particle energy evaluated at a small number of fixed lattice configurations, providing a practical criterion for assessing the validity of adiabatic tunneling theories in other systems.

Pritchard, P. Graham [Northwestern U.]↗
DOE OSTI2021

hippynn Python Package

hippynn is a python package for defining, training, and applying neural networks to atomistic systems. In particular, it focuses on Hierarchical Interacting Particle Neural Networks (HIP-NNs), a deep learning architecture for atomistic systems. HIP-NNs take input data describing the properties of atomistic systems (often generated using ab-initio quantum mechanics) to learn fast and accurate models. A trained HIP-NN can predict potential energy surfaces, atomic charges, and more. hippynn uses PyTorch for portable high-performance code, including both CPU and GPU support. hippynn allows for extensive customization, including user-defined models and loss functions, to facilitate future research into extensions of HIP-NN as well as other atomistic deep learning models.

Lubbers, Nicolas↗
DOE OSTI2021

Moisture-induced autonomous surface potential oscillations for energy harvesting

A variety of autonomous oscillations in nature such as heartbeats and some biochemical reactions have been widely studied and utilized for applications in the fields of bioscience and engineering. Here, we report a unique phenomenon of moisture-induced electrical potential oscillations on polymers, poly([2-(methacryloyloxy)ethyl] dimethyl-(3-sulfopropyl) ammonium hydroxide-co-acrylic acid), during the diffusion of water molecules. Chemical reactions are modeled by kinetic simulations while system dynamic equations and the stability matrix are analyzed to show the chaotic nature of the system which oscillates with hidden attractors to induce the autonomous surface potential oscillation. Using moisture in the ambient environment as the activation source, this self-excited chemoelectrical reaction could have broad influences and usages in surface-reaction based devices and systems. As a proof-of-concept demonstration, an energy harvester is constructed and achieved the continuous energy production for more than 15,000 seconds with an energy density of 16.8 mJ/cm 2 . A 2-Volts output voltage has been produced to power a liquid crystal display toward practical applications with five energy harvesters connected in series.

42 ENGINEERING↗
DOE OSTI2024

Hybrid Quantum Mechanical, Molecular Mechanical, and Machine Learning Potential for Computing Aqueous-Phase Adsorption Free Energies on Metal Surfaces

Performing reliable computer simulations of elementary processes occurring at metal–water interfaces is pivotal for novel catalyst design in sustainable energy applications. Computational catalyst design hinges on the ability to reliably and efficiently compute the potential energy surface (PES) of the system. Here, due to the large system sizes needed for studying processes at liquid water–metal interfaces, these systems can currently not be described using density functional theory (DFT). In this work, we used a hybrid quantum mechanical, molecular mechanical, and machine learning potential for studying the adsorption behavior of phenol, atomic hydrogen, 2-butanol, and 2-butanone on the (0001) facet of Ru under reducing conditions when Ru is not oxidized. Specifically, we describe the adsorbate and the surrounding metal atoms at the DFT level of theory. Here, we also considered the electrostatic field effect of the water molecules on adsorbate–metal interactions. Next, for the water–water and water–adsorbate interactions, we used established classical force fields. Finally, for the water–Ru surface interaction, for which no reliable force fields have been published, we used Behler–Parrinello high-dimensional neural network potentials (HDNNPs). Employing this setup, we used our explicit solvation for metal surface (eSMS) approach to compute the aqueous-phase effect on the low-coverage adsorption of selected molecules and atoms on the (0001) facet of Ru. In agreement with previous experimental and computational studies of oxygenated molecules over transition metal facets, we found that liquid water destabilizes the tested adsorbates on Ru(0001). Interestingly, our findings indicate that adsorbates on Ru are less affected by the presence of an aqueous phase than on other transition metals (e.g., Pt), highlighting the necessity of experimental investigations of Ru-based catalytic systems in liquid water.

Adsorption↗
DOE OSTI2021

Dy adsorption on and intercalation under graphene on 6 H -SiC(0001) surface from first-principles calculations

Previous experimental observations motivate clarification of configuration stabilities and kinetic processes for intercalation of guest atoms into a layered van der Waals material such as a graphene-SiC system. From our first-principles density functional theory (DFT) calculations, we analyze Dy adsorption and intercalation for graphene on a 6H-SiC(0001) surface, where the system includes two single-atom-thick graphene layers: the top-layer graphene (TLG) and the underling buffer-layer graphene (BLG) above the terminal Si layer. Our chemical potential analysis shows that intercalation of a single Dy atom into the gallery between TLG and BLG is more favorable than adsorption on TLG but that intercalation into the gallery underneath BLG is highly unfavorable. We obtain diffusion barriers of ~0.45 and 0.54 eV for a Dy atom diffusing on and under TLG, respectively. We find that the direct penetration of a Dy atom from the graphene top into the gallery under TLG is almost inhibited below a temperature of ~1400 K due to a large global barrier of at least ~3.5 eV. Instead, we find that a single Dy atom on TLG can easily intercalate by crossing a TLG step (e.g., a zigzag step presaturated by a Dy chain or a reconstructed zigzag step zz57). Additionally, we also perform DFT calculations for different Dy coverages to demonstrate how the favorability of Dy intercalation, as well as the corresponding interlayer spacings, depend on the coverage. Consequently, we can provide general insight and guidance for extensively studied systems involving intercalation of foreign atoms into graphene on a SiC substrate.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Enhancing high-fidelity neural network potentials through low-fidelity sampling

The efficacy of neural network potentials (NNPs) critically depends on the quality of the configurational datasets used for training. Prior research using empirical potentials has shown that well-selected liquid–solid transitional configurations of a metallic system can be translated to other metallic systems. This study demonstrates that such validated configurations can be relabeled using density functional theory (DFT) calculations, thereby enhancing the development of high-fidelity NNPs. Training strategies and sampling approaches are efficiently assessed using empirical potentials and subsequently relabeled via DFT in a highly parallelized fashion for high-fidelity NNP training. Our results reveal that relying solely on energy and force for NNP training is inadequate to prevent overfitting, highlighting the necessity of incorporating stress terms into the loss functions. To optimize training involving force and stress terms, we propose employing transfer learning to fine-tune the weights, ensuring that the potential surface is smooth for these quantities composed of energy derivatives. This approach markedly improves the accuracy of elastic constants derived from simulations in both empirical potential-based NNPs and relabeled DFT-based NNPs. Overall, this study offers significant insights into leveraging empirical potentials to expedite the development of reliable and robust NNPs at the DFT level.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Enabling complete multichannel nonadiabatic dynamics: A global representation of the two-channel coupled, 1,2 1 A and 1 3 A states of NH 3 using neural networks

Global coupled three-state two-channel potential energy and property/interaction (dipole and spin–orbit coupling) surfaces for the dissociation of NH 3 (Ã) into NH + H 2 and NH 2 + H are reported. The permutational invariant polynomial-neural network approach is used to simultaneously fit and diabatize the electronic Hamiltonian by fitting the energies, energy gradients, and derivative couplings of the two coupled lowest-lying singlet states as well as fitting the energy and energy gradients of the lowest-lying triplet state. The key issue in fitting property matrix elements in the diabatic basis is that the diabatic surfaces must be smooth, that is, the diabatization must remove spikes in the original adiabatic property surfaces attributable to the switch of electronic wavefunctions at the conical intersection seam. Here, we employ the fit potential energy matrix to transform properties in the adiabatic representation to a quasi-diabatic representation and remove the discontinuity near the conical intersection seam. The property matrix elements can then be fit with smooth neural network functions. The coupled potential energy surfaces along with the dipole and spin–orbit coupling surfaces will enable more accurate and complete treatment of optical transitions, as well as nonadiabatic internal conversion and intersystem crossing.

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

Resonant Auger decay of dissociating CH 3 I near the I 4d threshold

Resonant Auger processes provide a unique perspective on electronic interactions and excited vibrational and electronic states of molecular ions. Here, new data are presented on the resonant Auger decay of excited CH 3 I in the region just below the I 4d -1 ionization threshold. The resonances include the Rydberg series converging to the five spin–orbit and ligand-field split CH 3 I (I 4d -1 ) thresholds, as well as resonances corresponding to excitation from the I 4d 5/2,3/2 orbitals into the σ* lowest unoccupied molecular orbital. This study focuses on participator decay that populates the lowest lying states of CH 3 I + , in particular, the X̃ 2 E 3/2 and 2 E 1/2 states, and on spectator decay that populates the lowest-lying (CH 3 I 2+ )σ* states of CH 3 I + . The CH 3 I (I 4d -1 )σ* resonances are broad, and dissociation to CH 3 + I competes with the autoionization of the core-excited states. Auger decay as the molecule dissociates produces a photoelectron spectrum with a long progression (up to v 3 + ~ 25) in the C–I stretching mode of the X̃ 2 E 3/2 and 2 E 1/2 states, providing insights into the shape of the dissociative core-excited surface. Further, the observed spectator decay processes indicate that CH 3 I + is formed on the repulsive wall of the lower-lying (CH 3 I 2+ )σ* potentials, and the photon-energy dependence of the processes provides insights into the relative slopes of the (4d -1 )σ* and (CH 3 I 2+ )σ* potential surfaces. Data are also presented for the spectator decay of higher lying CH 3 I (I 4d -1 )nl Rydberg resonances. Photoelectron angular distributions for the resonant Auger processes provide additional information that helps distinguish these processes from the direct ionization signal.

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

Chemical control of the Rashba spin splitting size of α-GeTe(111) surface states by adjusting the potential at the topmost atomic layer

As control of the Rashba spin splitting size is highly desirable for spintronic devices, intensive studies have been performed to vary the splitting size by, for example, applying an electric field or designing novel heterostructures. However, direct observation of Rashba spin splitting size change via spectroscopic measurements has not been done so far. Here, we report results of angle-resolved photoemission studies on ferroelectric α-GeTe(111). We observe that the Rashba splitting size of α-GeTe(111) surface states is reduced upon dosing with potassium (K) which has a very low electron affinity. Based on density functional theory calculations, we find that the electric potential energy at the topmost atomic layer and the surface potential energy barrier is reduced upon K dosing. Furthermore, this change in the surface potential induces both delocalization of the surface states and reduction of the splitting size. We expect to increase the splitting size by dosing other elements or molecules with high electron affinity.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Characterization of light-induced potentials in the strong-field dissociation of O 2 +

In this work, we investigate theoretically the imprints of light-induced potentials (LIPs) on the dissociation dynamics of O 2 + molecular ions, as observable in angle-resolved fragment kinetic-energy-release (KER) spectra. Following the vibrational and rotational dynamics of the initial pump-laser-excited cationic nuclear wave packet, while accounting for the dipole coupling between the O 2 + ( a 4 Π u ) and O 2 + ( f 4 Π g ) electronic states in 800-nm 40-fs probe-laser pulses with peak intensities between 10 13 and 10 14 W / cm 2 , we calculate angle-resolved KER spectra which reveal characteristic energy- and angle-dependent fringe structures. These fringes shift downward in energy as the molecular alignment angle θ relative to the probe-pulse polarization direction increases from 0 to π / 2 . The angle-dependent shifts in the KER fringes increase for larger probe-pulse peak intensities and follow the angle and light-wave-intensity dependence of the vibrational spectrum in the associated Floquet bond-hardening well, which is a manifestation of transient O 2 + nuclear-probability trapping in the LIP during dissociation. By examining the rovibrational dynamics of the dissociating molecular cation near the light-induced conical intersection (LICI) in the cationic LIP surface at θ = π / 2 , we identify related angle-dependent structures in the KER spectra, suggesting a means for assessing the significance of LICIs in molecular dissociation pathways.

74 ATOMIC AND MOLECULAR PHYSICS↗