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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Semi-Automated Creation of Density Functional Tight Binding Models through Leveraging Chebyshev Polynomial-Based Force Fields

Density functional tight binding (DFTB) is an attractive method for accelerated quantum simulations of condensed matter due to its enhanced computational efficiency over standard density functional theory (DFT) approaches. However, DFTB models can be challenging to determine for individual systems of interest, especially for metallic and interfacial systems where different bonding arrangements can lead to significant changes in electronic states. In this regard, we have created a rapid-screening approach for determining systematically improvable DFTB interaction potentials that can yield transferable models for a variety of conditions. Our method leverages a recent reactive molecular dynamics force field where many-body interactions are represented by linear combinations of Chebyshev polynomials. This allows for the efficient creation of multi-center representations with relative ease, requiring only a small investment in initial DFT calculations. Here, we have focused our workflow on TiH 2 as a model system and show that a relatively small training set based on unit-cell-sized calculations yields a model accurate for both bulk and surface properties. Our approach is easy to implement and can yield reliable DFTB models over a broad range of thermodynamic conditions, where physical and chemical properties can be difficult to interrogate directly and there is historically a significant reliance on theoretical approaches for interpretation and validation of experimental results.

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

The Reaction Rates of Amidogen and Ammonia with Nitrous Oxide: Implications for Combustion Mechanisms

Pulsed laser photolysis experiments with laser-induced fluorescence detection of NH 2 set an upper limit to the rate constant for reaction with N 2 O of k < 1 × 10 −15 cm 3 molecule −1 s −1 at 513 K. Computations were based on geometries and anharmonic frequency analysis (B2PLYP-D3/cc-pVTZ) followed by coupled cluster calculations extrapolated to the infinite basis set limit, with corrections for core−valence electron correlation, scalar relativistic effects, and correlation up to CCSDT(Q). Species that showed multireference character were quantified with MRCI(7,7)+Q/cc-pVTZ theory. Rate constants were obtained for the dominant product channel H 2 NN + NO, along with HN 3 + OH, H 2 NO + N 2 , NNH + HNO, ON(NH) 2 and HNNH + NO. The last channel is slow even at 2500 K, contrary to an early empirical estimate and confirming recent suggestions. Modeling of literature experiments on oxidation of NH 3 by N 2 O shows that all channels are too slow to make a significant impact on the loss of N 2 O in ammonia flames. Similarly, the direct NH 3 + N 2 O reaction is found to be negligibly slow.

Ammonia↗
DOE OSTI2020

Enabling Efficient and Accurate Computational Studies of MOF Reactivity via QM/MM and QM/QM Methods

Electronic structure calculations can provide unique insight into metal–organic framework (MOF) reactivity and defect formation. Such calculations can be broadly categorized as utilizing either periodic or cluster models, each with their respective advantages and disadvantages. In the present study, we demonstrate how multiscale methods can leverage the advantages of both approaches to enable high levels of accuracy and computational efficiency in studies of MOF reactivity. Using defect formation in a zeolitic imidazolate framework (ZIF) as a prototypical example, we benchmark a quantum mechanics/molecular mechanics (QM/MM) scheme that enables highly efficient cluster-based calculations on MOFs. We demonstrate the importance of correctly accounting for the influence of both dative bond cleavage in the QM cluster and long-range mechanical coupling to the bulk to achieving accurate QM/MM studies of MOFs. We subsequently leverage these cluster models in a QM/QM scheme that goes beyond standard DFT to yield “gold-standard” correlated wave function results on MOFs at modest computational cost. Crucially, we find several cases in which the incorporation of these correlated corrections yields qualitatively important corrections over conventional DFT values.

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

Multi-reward Reinforcement Learning Based Bond-Order Potential to Study Strain-Assisted Phase Transitions in Phosphorene

Here, we introduce a multi-reward reinforcement learning (RL) approach to train a flexible bond-order potential (BOP) for 2D phosphorene based on ab initio training data sets. Our approach is based on a continuous action space Monte Carlo tree search algorithm that is general and scalable and presents an efficient multiobjective optimization scheme for high-dimensional materials design problems. As a proof-of-concept, we deploy this scheme to parametrize multiple structural and dynamical properties of 2D phosphorene polymorphs. Our RL-trained BOP model adequately captures the structure, energetics, transformation barriers, equation of state, elastic constants, and phonon dispersions of various 2D P polymorphs. We use this model to probe the impact of temperature and strain rate on the phase transition from black (α-P) to blue phosphorene (β-P) through molecular dynamics simulations. A decrease in critical strain for this phase transition with increase in temperature is observed, and the underlying atomistic mechanisms are discussed.

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

Electrolyte Reactivity on the MgV 2 O 4 Cathode Surface

Predictive understanding of the solvation-dependent reactivity and molecular interaction of electrolyte ions and solvent molecules on reactive electrodes has been a major challenge but is essential for addressing instabilities and surface passivation that occur at electrode-electrolyte interface (EEI) of multivalent Mg batteries. In this work, the isolated intrinsic reactivities of prominent chemical species present in magnesium bis(trifluoromethanesulfonimide) (Mg(TFSI) 2 ) in diglyme (G2) electrolytes, including ionic (TFSI - , [Mg(TFSI)] + , [Mg(TFSI):G2] + , [Mg(TFSI):2G2] + ) as well as neutral molecules (G2) on magnesium vanadate cathode (MgV 2 O 4 ) surface has been studied using a combination of first-principles calculations and multimodal analysis of well-defined cathode electrolyte interphase (CEI) layers. Here, our calculations show that non-solvated [Mg(TFSI)] + is the strongest adsorbing species on the MgV 2 O 4 surface compared to all other ions while fully solvated [Mg(TFSI):2G2] + are least favorable to decomposition. The cleavage of C-S bonds in TFSI - to form CF 3 - is predicted to be most desired pathway for all ionic species, which is followed by the cleavage of C-O bonds of G2 to yield CH 3 + or OCH 3 - species. The strong stabilization and electron transfer between ionic electrolyte species and MgV 2 O 4 is found to significantly favor these decomposition reactions on the surface compared to intrinsic gas phase dissociation. Experimentally, we used state-of-the-art ion soft landing to selectively deposit mass-selected TFSI - , [Mg(TFSI):G2] + and [Mg(TFSI):2G2] + on MgV 2 O 4 thin film to form well-defined electrolyte-MgV 2 O 4 interface. Analysis of soft-landed interphase using X-ray photoelectron, X-ray absorption near edge structure, electron energy-loss spectroscopies as well as transmission electron microscopy confirmed the presence of decomposition species (e.g., MgFx, carbonates) formed in the interfacial region and the higher amount of MgFx with [Mg(TFSI):G2] + , which corroborates the theoretical observation. Overall, we established the mechanistic pathway for the electrolyte-induced formation of passivating fluorides on MgV 2 O 4 cathode facilitated by the surface adsorption and charge transfer, which provided essential knowledge for rational design of stable electrolytes for multivalent cathodes.

Cathode-electrolyte interphase formation↗
DOE OSTI2020

Formic Acid: A Hydrogen-Bonding Cocatalyst for Formate Decomposition

Hydrogen bonding accelerates many catalytic reactions by orienting intermediates, stabilizing transition states, and even opening reaction pathways. However, most mechanistic studies regarding the decomposition of formic acid (FA), a promising hydrogen storage material, neglect hydrogen-bonding interactions even though FA is a strong hydrogen-bond donor and acceptor. Here, we probe the formation of bimolecular hydrogen-bonded complexes between FA and formate (FA–HCOO complexes) adsorbed on metal surfaces and how these complexes affect HCOO* decomposition. Using first-principles density functional theory (DFT) calculations on 12 close-packed (111)/(0001) and 8 open (100) surfaces of 12 transition metals—Ag, Au, Co, Cu, Ir, Ni, Os, Re, Pd, Pt, Rh, and Ru, we—show that FA–HCOO complexes are generally thermodynamically stable, even at elevated temperatures and pressures. We then illustrate that these complexes produce infrared spectroscopic signatures consistent with as yet unassigned experimental peaks. We last demonstrate that by stabilizing the dangling bond of monodentate HCOO*, these complexes significantly lower the barriers for rotation of HCOO* from a bidentate to a monodentate configuration, the rate-limiting step for HCOO* decomposition on many surfaces. FA thus acts as a cocatalyst for HCOO* decomposition. Our results may guide the community toward improved catalysts for reactions involving HCOO* such as FA decomposition, methanol steam reforming, and the water gas shift reaction. More broadly, our work highlights the ability of hydrogen bonding to modify the adsorbed structures of intermediates and lower the barriers for their reaction on heterogeneous catalysts. Lastly, this phenomenon can be relevant for other reactions involving ammonia, alcohols, and carboxylic acids.

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

Mechanistic Insights on the Low-Temperature Oxidation of CO Catalyzed by Isolated Co Ions in N-Doped Carbon

Isolated cobalt ions on nitrogen-doped carbon (Co-N-C) catalyze CO oxidation at temperatures as low as 196 K, but the active site and mechanism for this reaction remain elusive. In this work, steady-state CO oxidation around 273 K over Co-N-C revealed nearly first order behavior in both CO and O 2 as well as a negative apparent activation energy. Isotopic transient analysis of the reaction confirmed a rapid turnover frequency and low surface coverage of adsorbed intermediates leading to CO 2 (< 10% of the Co). Results from kinetics experiments combined with quantum chemical calculations and molecular dynamics simulations are consistent with a reaction path involving weak adsorption of CO onto Co ions followed by a low barrier for the CO-assisted activation of weakly adsorbed O 2 . Furthermore, this proposed mechanism for dioxygen activation does not involve a redox cycle with the transition metal ion and may be important in other low temperature catalytic reactions involving O 2 .

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

A Transferable Force Field for Predicting Adsorption and Diffusion of Water in Cationic Zeolites with Coupled Cluster Accuracy

We present a transferable force field for water in proton-exchanged, alkali (Li, Na, K, Rb, and Cs) metal-exchanged, and alkaline-earth (Mg, Ca, Sr, and Ba) metal-exchanged zeolites. The fitting methodology is based on adsorbate–adsorbent interaction energies obtained from periodic density functional theory calculations and corrected using the coupled-cluster method applied to small model clusters. To ensure an accurate prediction of both adsorption and diffusion properties of water, sets of configurations that sample both adsorption sites and intracrystalline hopping transition states were used in the fitting. The quality of the force field is assessed for a wide range of zeolites with different topologies and chemical compositions, demonstrating good agreement between theoretical predictions and experimental measurements of water adsorption and diffusion.

Adsorption↗
DOE OSTI2023

Ultra-fast interpretable machine-learning potentials

Abstract All-atom dynamics simulations are an indispensable quantitative tool in physics, chemistry, and materials science, but large systems and long simulation times remain challenging due to the trade-off between computational efficiency and predictive accuracy. To address this challenge, we combine effective two- and three-body potentials in a cubic B-spline basis with regularized linear regression to obtain machine-learning potentials that are physically interpretable, sufficiently accurate for applications, as fast as the fastest traditional empirical potentials, and two to four orders of magnitude faster than state-of-the-art machine-learning potentials. For data from empirical potentials, we demonstrate the exact retrieval of the potential. For data from density functional theory, the predicted energies, forces, and derived properties, including phonon spectra, elastic constants, and melting points, closely match those of the reference method. The introduced potentials might contribute towards accurate all-atom dynamics simulations of large atomistic systems over long-time scales.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Density functional study of relaxation of adsorbate vibration modes: Dominance of anharmonic interaction

Formulation and density functional workflow for calculating the lifetime of vibrational modes of molecular adsorbates on solid surfaces due to vibration–phonon coupling are presented. The anharmonic coupling is invoked to give the correct description of the origin of temperature dependence. Using pyrrolidine (C 4 H 9 N) absorbed on the Cu(001) surface as a concrete example, here we show that the anharmonic coupling can be one to two orders more significant than the harmonic interaction for the broadening of vibrational spectra, especially as temperature increases. These results challenge the common assumption that the anharmonic interaction is weak and call for attention of considering its effect in quantum relaxation and related problems.

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

Machine-learning accelerated geometry optimization in molecular simulation

Geometry optimization is an important part of both computational materials and surface science because it is the path to finding ground state atomic structures and reaction pathways. These properties are used in the estimation of thermodynamic and kinetic properties of molecular and crystal structures. This process is slow at the quantum level of theory because it involves an iterative calculation of forces using quantum chemical codes such as density functional theory (DFT), which are computationally expensive and which limit the speed of the optimization algorithms. It would be highly advantageous to accelerate this process because then one could do either the same amount of work in less time or more work in the same time. Here, we provide a neural network (NN) ensemble based active learning method to accelerate the local geometry optimization for multiple configurations simultaneously. We illustrate the acceleration on several case studies including bare metal surfaces, surfaces with adsorbates, and nudged elastic band for two reactions. In all cases, the accelerated method requires fewer DFT calculations than the standard method. In addition, we provide an Atomic Simulation Environment (ASE)-optimizer Python package to make the usage of the NN ensemble active learning for geometry optimization easier.

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

Machine Learned Hückel Theory: Interfacing Physics and Deep Neural Networks

The Hückel Hamiltonian is an incredibly simple tight-binding model known for its ability to capture qualitative physics phenomena arising from electron interactions in molecules and materials. Part of its simplicity arises from using only two types of empirically fit physics-motivated parameters: the first describes the orbital energies on each atom and the second describes electronic interactions and bonding between atoms. By replacing these empirical parameters with machine-learned dynamic values, we vastly increase the accuracy of the extended Hückel model. The dynamic values are generated with a deep neural network, which is trained to reproduce orbital energies and densities derived from density functional theory. The resulting model retains interpretability, while the deep neural network parameterization is smooth and accurate and reproduces insightful features of the original empirical parameterization. Altogether, this work shows the promise of utilizing machine learning to formulate simple, accurate, and dynamically parameterized physics models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗