Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Transition state”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

High-resolution CH stretch spectroscopy of jet-cooled cyclopentyl radical: First insights into equilibrium structure, out-of-plane puckering, and IVR dynamics

First, high-resolution sub-Doppler infrared spectroscopic results for cyclopentyl radical (C 5 H 9 ) are reported on the α-CH stretch fundamental with suppression of spectral congestion achieved by adiabatic cooling to T rot ≈ 19(4) K in a slit jet expansion. Surprisingly, cyclopentyl radical exhibits a rotationally assignable infrared spectrum, despite 3N – 6 = 36 vibrational modes and an upper vibrational state density (ρ ≈ 40–90 #/cm –1 ) in the critical regime (ρ ≈ 100 #/cm –1 ) necessary for onset of intramolecular vibrational relaxation (IVR) dynamics. Such high-resolution data for cyclopentyl radical permit detailed fits to a rigid-rotor asymmetric top Hamiltonian, initial structural information for ground and vibrationally excited states, and opportunities for detailed comparison with theoretical predictions. Specifically, high level ab initio calculations at the coupled-cluster singles, doubles, and perturbative triples (CCSD(T))/ANO0, 1 level are used to calculate an out-of-plane bending potential, which reveals a C 2 symmetry double minimum 1D energy surface over a C 2v transition state. Here, the inversion barrier [V barrier ≈ 3.7(1) kcal/mol] is much larger than the effective moment of inertia for out-of-plane bending, resulting in localization of the cyclopentyl wavefunction near its C 2 symmetry equilibrium geometry and tunneling splittings for the ground state too small (<1 MHz) to be resolved under sub-Doppler slit jet conditions. The persistence of fully resolved high-resolution infrared spectroscopy for such large cyclic polyatomic radicals at high vibrational state densities suggests a “deceleration” of IVR for a cycloalkane ring topology, much as low frequency torsion/methyl rotation degrees of freedom have demonstrated a corresponding “acceleration” of IVR processes in linear hydrocarbons.

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

Enhanced Sampling of Crystal Nucleation with Graph Representation Learnt Variables

In this study, we present a graph neural network-based learning approach using an autoencoder setup to derive low-dimensional variables from features observed in experimental crystal structures. These variables are then biased in enhanced sampling to observe state-to-state transitions and reliable thermodynamic weights. Our approach uses simple convolution and pooling methods. To verify the effectiveness of our protocol, we examined the nucleation of various allotropes and polymorphs of iron and glycine from their molten states. Our graph latent variables when biased in well-tempered metadynamics consistently show transitions between states and achieve accurate free energy calculations in agreement with experiments, both of which are indicators of dependable sampling. This underscores the strength and promise of our graph neural net variables for improved sampling. Furthermore, the protocol shown here should be applicable for other systems and with other sampling methods.

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

graphenv: a Python library for reinforcement learning on graph search spaces

Many important and challenging problems in combinatorial optimization (CO) can be expressed as graph search problems, in which graph vertices represent full or partial solutions and edges represent decisions that connect them. Graph structure not only introduces strong relational inductive biases for learning (Battaglia et al., 2018) - in this context, by providing a way to explicitly model the value of transitioning (along edges) between one search state (vertex) and the next - but lends itself to problems both with and without clearly defined algebraic structure. For example, classic CO problems on graphs such as the Traveling Salesman Problem (TSP) can be expressed as either pure graph search or integer programs. Other problems, however, such as molecular optimization, do no have concise algebraic formulations and yet are readily implemented as a graph search (V. et al., 2022; Zhou et al., 2019). Such "model-free" problems constitute a large fraction of modern reinforcement learning (RL) research owing to the fact that it is often much easier to write a forward simulation that expresses all of the state transitions and rewards, than to write down the precise mathematical expression of the full optimization problem. In the case of molecular optimization, for example, one can use domain knowledge alongside existing software libraries to model the effect of adding a single bond or atom to an existing but incomplete molecule, and let the RL algorithm build a model of how good a given decision is by "experiencing" the simulated environment many times through. In contrast, a model-based mathematical formulation that fully expresses all the chemical and physical constraints is intractable. In recent years, RL has emerged as an effective paradigm for optimizing searches over graphs and led to state-of-the-art heuristics for games like Go and chess, as well as for classical CO problems such as the TSP. This combination of graph search and RL, while powerful, requires non-trivial software to execute, especially when combining advanced state representations such as Graph Neural Networks (GNN) with scalable RL algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Ultrafast Jahn‐Teller Photoswitching in Cobalt Single‐Ion Magnets

Abstract Single‐ion magnets (SIMs) constitute the ultimate size limit in the quest for miniaturizing magnetic materials. Several bottlenecks currently hindering breakthroughs in quantum information and communication technologies could be alleviated by new generations of SIMs displaying multifunctionality. Here, ultrafast optical absorption spectroscopy and X‐ray emission spectroscopy are employed to track the photoinduced spin‐state switching of the prototypical complex [Co(terpy) 2 ] 2+ (terpy = 2,2′:6′,2″‐terpyridine) in solution phase. The combined measurements and their analysis supported by density functional theory (DFT), time‐dependent‐DFT (TD‐DFT) and multireference quantum chemistry calculations reveal that the complex undergoes a spin‐state transition from a tetragonally elongated doublet state to a tetragonally compressed quartet state on the femtosecond timescale, i.e., it sustains ultrafast Jahn‐Teller (JT) photoswitching between two different spin multiplicities. Adding new Co‐based complexes as possible contenders in the search for JT photoswitching SIMs will greatly widen the possibilities for implementing magnetic multifunctionality and eventually controlling ultrafast magnetization with optical photons.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Influence of Ti‐incorporated Zeolite Topology and Pore Condensation on Vapor Phase Propylene Epoxidation Kinetics with Gaseous H 2 O 2

Abstract Vapor‐phase propylene (C 3 H 6 ) epoxidation kinetics with hydrogen peroxide (H 2 O 2 ) strongly reflects the physical properties of Ti‐incorporated zeolite catalysts and the presence of spectating molecules (“solvent”) near active sites even without a bulk liquid phase. Steady‐state turnover rates of C 3 H 6 epoxidation and product selectivities vary by orders of magnitudes, depending on the zeolite silanol ((SiOH) x ) density, pore topology (MFI, *BEA, FAU), and the quantity of condensed acetonitrile (CH 3 CN) molecules nearby active sites, under identical reaction mechanisms sharing activated H 2 O 2 intermediates on Ti surfaces. Individual kinetic analyses for propylene oxide (PO) ring‐opening, homogeneous diol oxidative cleavage, and homogeneous aldehyde oxidation reveal that secondary reaction kinetics following C 3 H 6 epoxidation responds more sensitively to the changes in zeolite physical properties and pore condensation with CH 3 CN. Thus, higher PO selectivities achieved in hydrophilic Ti‐MFI at steady‐state reflect the preferential stabilization of transition states for C 3 H 6 epoxidation (a primary reaction) relative to PO ring‐opening and oxidative cleavage (secondary reactions) that solvation effects that reflect interactions among condensed CH 3 CN within pores and the extended pore structure.

Kwon, Ohsung↗
DOE OSTI2024

Influence of Ti‐incorporated Zeolite Topology and Pore Condensation on Vapor Phase Propylene Epoxidation Kinetics with Gaseous H 2 O 2

Vapor-phase propylene (C 3 H 6 ) epoxidation kinetics with hydrogen peroxide (H 2 O 2 ) strongly reflects the physical properties of Ti-incorporated zeolite catalysts and the presence of spectating molecules (“solvent”) near active sites even without a bulk liquid phase. Steady-state turnover rates of C 3 H 6 epoxidation and product selectivities vary by orders of magnitudes, depending on the zeolite silanol ((SiOH) x ) density, pore topology (MFI, *BEA, FAU), and the quantity of condensed acetonitrile (CH 3 CN) molecules nearby active sites, under identical reaction mechanisms sharing activated H 2 O 2 intermediates on Ti surfaces. Individual kinetic analyses for propylene oxide (PO) ring-opening, homogeneous diol oxidative cleavage, and homogeneous aldehyde oxidation reveal that secondary reaction kinetics following C 3 H 6 epoxidation responds more sensitively to the changes in zeolite physical properties and pore condensation with CH 3 CN. Thus, higher PO selectivities achieved in hydrophilic Ti-MFI at steady-state reflect the preferential stabilization of transition states for C 3 H 6 epoxidation (a primary reaction) relative to PO ring-opening and oxidative cleavage (secondary reactions) that solvation effects that reflect interactions among condensed CH 3 CN within pores and the extended pore structure.

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

Observation of Single-Electron Transport and Charging on Individual Point Defects in Atomically Thin WSe 2

Defects significantly impact the properties of 2D semiconductors, providing in-gap quantum states that can serve as a natural platform for single-electron operations, localization sites for excitons to serve as single-photon sources, and potentially quantum spin memories. To date, however, a microscopic observation of such a single-electron transport (SET) behavior has been rarely reported on single defects of 2D semiconductors. In this study, we report a SET and charge-state transition on individual point-defect states buried in the bilayer WSe 2 using scanning tunneling microscopy. SET characteristics of their states is evidenced by both the Coulomb staircase and the saturation behavior of the transport current, consistent with the SET model. Furthermore, we demonstrate that, through the local field of a scanning tunneling microscope tip, it is possible to successively charge the defects by single electrons, suggested by both the ring structures and charging peaks in dI/dV measurements. Our results provide new insights into the quantum nature of these defect-bound states, as well as the possibility of using their single-electron behavior and response for applications in quantum information and local-field sensing.

2D semiconductor↗
DOE OSTI2024

Switching Modes for Reduction of Peak Voltage Transients in GaN-Based Three Level ANPC Inverter

The switching pole voltage can transition between P,0, and N states in a three-level active neutral point clamped (ANPC) inverter. State transitions between 0-P and 0-N can be realized with different switching modes. Three switching modes- Short 1 , Short 2 , and Full are studied considering the effect of capacitive current paths. The role of clamping and inner switch on/off conditions is determined using simplified equivalent circuit models. Short 2 with clamping switch off is beneficial for turn-on overvoltage suppression but results in high turn-off overvoltage. Full mode with a parallel current sharing path is effective for turn-off overvoltage suppression but leads to high turn-on overvoltage. Hence, a Modified Full mode is proposed to achieve the simultaneous objective of overvoltage suppression at both turn-on and turn-off transient during high load currents. This benefits 3L ANPC operation at a low power factor with a high load current at fundamental voltage zero crossing points. Here, a 650V GaN-based three-level ANPC inverter prototype is designed and used to evaluate the switching modes and overvoltage suppression strategies through double pulse tests and continuous operation.

42 ENGINEERING↗
DOE OSTI2020

Correlation-Aided Robust Decentralized Dynamic State Estimation of Power Systems with Unknown Control Inputs

This paper proposes a correlation-aided robust adaptive unscented Kalman filter for power system decentralized dynamic state estimation with unknown inputs, termed as robust AUKF-UI. The temporal and spatial correlations among the unknown inputs are used to derive a vector auto-regressive (VAR) model in an adaptive manner. This VAR model is further integrated together with state transition and measurement models for joint state and unknown inputs estimation. This allows taking into account the implicit cross-correlations between the states and the unknown inputs. As a result, the rank requirement for unknown input vector estimation is relaxed and the local generator frequency measurement is not required. The temporal correlations of time series innovation vectors, predicted state and input vectors are also leveraged by the robust AUKFUI to detect, identify and process bad data. Without these correlations, it is very challenging to address bad data with unknown inputs. Simulation results carried out on the IEEE 39-bus system demonstrate that the proposed robust AUKF-UI achieves much better results than other methods in the presence of low measurement redundancy, strong nonlinearity, and bad data.

Zhao, Junbo↗
DOE OSTI2020

Robust Adaptive Decentralized Dynamic State Estimation with Unknown Control Inputs using Field PMU Measurements

This paper proposes a robust adaptive decentralized dynamic state estimation method for power system with unknown inputs of the highly detailed synchronous machine model. The temporal and spatial correlations among the unknown inputs are used to derive a vector auto-regressive model. The latter is further integrated together with state transition and measurement models for joint state and unknown inputs estimation. Thanks to the consideration of implicit cross-correlations between the states and the unknown inputs, only generator terminal voltage and current phasors are needed. Test results on the US WECC system using the field PMU measurements show that the proposed method is able to track both the system dynamic states and unknown controller inputs. These information could significantly benefit the validation and calibration of generator controller parameters.

Zhao, Junbo↗
DOE OSTI2025

Density functional theory-based surrogate kinetic models for heterogeneous reactions of hydrocarbon intermediates on silicon carbide

The increasing demand for high-performance materials in advanced technologies highlights the importance of achieving a fundamental understanding and potential control of silicon carbide (SiC) deposition processes. However, existing models often lack sufficient theoretical detail, relying heavily on empirical data and offering limited predictive capability. In particular, the complex surface chemistry governing SiC growth remains poorly understood. This study addresses these challenges by employing density functional theory (DFT) to investigate key heterogeneous reactions involving hydrocarbon intermediates on SiC surfaces, including dehydrogenation, hydrogenation, and carbon deposition. Transition state searches were conducted to identify reaction pathways and energy barriers. While first-principles calculations offer high accuracy, they are computationally intensive. To extend the utility of these first-principles results, vibrational analyses were performed using phonon-based statistical thermochemistry to compute temperature-dependent reaction rates which were used to develop Arrhenius-type surrogate kinetic models. Furthermore, the resulting framework provides a more rigorous, physically grounded basis for integrating atomistic insights into continuum-scale modeling, ultimately enabling improved prediction and optimization of SiC film growth in high-performance material systems.

Density Functional Theory↗
DOE OSTI2021

State predictive information bottleneck

We report the ability to make sense of the massive amounts of high-dimensional data generated from molecular dynamics simulations is heavily dependent on the knowledge of a low-dimensional manifold (parameterized by a reaction coordinate or RC) that typically distinguishes between relevant metastable states, and which captures the relevant slow dynamics of interest. Methods based on machine learning and artificial intelligence have been proposed over the years to deal with learning such low-dimensional manifolds, but they are often criticized for a disconnect from more traditional and physically interpretable approaches. To deal with such concerns, in this work we propose a deep learning based state predictive information bottleneck approach to learn the RC from high-dimensional molecular simulation trajectories. We demonstrate analytically and numerically how the RC learnt in this approach is connected to the committor in chemical physics and can be used to accurately identify transition states. A crucial hyperparameter in this approach is the time delay or how far into the future the algorithm should make predictions about. Through careful comparisons for benchmark systems, we demonstrate that this hyperparameter choice gives useful control over how coarse-grained we want the metastable state classification of the system to be. We thus believe that this work represents a step forward in systematic application of deep learning based ideas to molecular simulations.

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

Nonadiabatic transition paths from quantum jump trajectories

We present a means of studying rare reactive pathways in open quantum systems using transition path theory and ensembles of quantum jump trajectories. This approach allows for the elucidation of reactive paths for dissipative, nonadiabatic dynamics when the system is embedded in a Markovian environment. Here, we detail the dominant pathways and rates of thermally activated processes and the relaxation pathways and photoyields following vertical excitation in a minimal model of a conical intersection. We find that the geometry of the conical intersection affects the electronic character of the transition state as defined through a generalization of a committor function for a thermal barrier crossing event. Similarly, the geometry changes the mechanism of relaxation following a vertical excitation. Relaxation in models resulting from small diabatic coupling proceeds through pathways dominated by pure dephasing, while those with large diabatic coupling proceed through pathways limited by dissipation. The perspective introduced here for the nonadiabatic dynamics of open quantum systems generalizes classical notions of reactive paths to fundamentally quantum mechanical processes.

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

Simple Approximation for the Ideal Reference State of Gases Adsorbed on Solid-State Surfaces

Reference states are useful as models for facilitating calculations of equilibrium constants, and they may also serve as standard states that are convenient for organizing and tabulating thermodynamic data; however, standard state conventions and appropriate reference states for adsorbed species have received less attention than those for pure substances and solutes. Here, we compare seven choices of reference states for calculations of equilibrium constants and transition state theory rate constants for flat surfaces, in particular (1) an ideal 2D harmonic oscillator, (2) an ideal rigid-molecule harmonic oscillator, (3) an ideal 2D harmonic oscillator with separable surface modes, (4) a 2D ideal gas, (5) an ideal 2D hindered translator, (6) an ideal 2D hindered translator with lowest-order barriers, and (7) a simple ideal 2D hindered translator proposed in this work. The advantage of models 5–7 is that they can treat both mobile and localized adsorbates in a consistent way, whereas models 1–3 are only appropriate for localized adsorbates, and model 4 is only appropriate for a freely translating adsorbate. Furthermore, models 6 and 7 reduce the computational cost without the user having to calculate barrier heights for diffusion. An advantage of the simple ideal 2D hindered translator is that it has a physical high-temperature limit. We also propose a reference state for nonflat surfaces. Here, the user is encouraged to choose a reference state based on the appropriateness of the model and the practicality of the calculations.

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

Predicting the Activity and Selectivity of Bimetallic Metal Catalysts for Ethanol Reforming using Machine Learning

Machine learning is ideally suited for the pattern detection in large uniform datasets, but consistent experimental datasets on catalyst studies are often small. Here we demonstrate how a combination of machine learning and first-principles calculations can be used to extract knowledge from a relatively small set of experimental data. The approach is based on combining a complex machine-learning model trained on an extensive computational library of transition-state energies with simple linear regression models of experimental catalytic activities and selectivities from the literature. Using the combined model, we identify the key C–C bond scission reactions involved in ethanol reforming and perform a computational screening for ethanol reforming on monolayer bimetallic catalysts with architectures TM-Pt-Pt(111) and Pt-TM-Pt(111) (TM = 3d transition metals). The model also predicts four promising catalyst compositions for future experimental studies. In conclusion, the approach is not limited to ethanol reforming but is of general use for the interpretation of experimental observations as well as for the computational discovery of novel catalytic materials.

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

Infrared signature of the hydroperoxyalkyl intermediate (·QOOH) in cyclohexane oxidation: An isomer-resolved spectroscopic study

Infrared (IR) action spectroscopy is utilized to characterize carbon-centered hydroperoxy-cyclohexyl radicals (·QOOH) transiently formed in cyclohexane oxidation. The oxidation pathway leads to three nearly degenerate ·QOOH isomers, β-, γ-, and δ-QOOH, which are generated in the laboratory by H-atom abstraction from the corresponding ring sites of the cyclohexyl hydroperoxide (CHHP) precursor. The IR spectral features of jet-cooled and stabilized ·QOOH radicals are observed from 3590 to 7010 cm −1 (∼10–20 kcal mol −1 ) at energies in the vicinity of the transition state (TS) barrier leading to OH radicals that are detected by ultraviolet laser-induced fluorescence. The experimental approach affords selective detection of β-QOOH, arising from its significantly lower TS barrier to OH products compared to γ and δ isomers, which results in rapid unimolecular decay and near unity branching to OH products. The observed IR spectrum of β-QOOH includes fundamental and overtone OH stretch transitions, overtone CH stretch transitions, and combination bands involving OH or CH stretch with lower frequency modes. The assignment of β-QOOH spectral features is guided by anharmonic frequencies and intensities computed using second-order vibrational perturbation theory. The overtone OH stretch (2ν OH ) of β-QOOH is shifted only a few wavenumbers from that observed for the CHHP precursor, yet they are readily distinguished by their prompt vs slow dissociation rates to OH products.

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

Attaining freshwater and estuarine-water soil saturation in an ecosystem-scale coastal flooding experiment

Abstract Coastal upland forests are facing widespread mortality as sea-level rise accelerates and precipitation and storm regimes change. The loss of coastal forests has significant implications for the coastal carbon cycle; yet, predicting mortality likelihood is difficult due to our limited understanding of disturbance impacts on coastal forests. The manipulative, ecosystem-scale Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments (TEMPEST) experiment addresses the potential for freshwater and estuarine-water disturbance events to alter tree function, species composition, and ecosystem processes in a deciduous coastal forest in MD, USA. The experiment uses a large-unit (2000 m 2 ), un-replicated experimental design, with three 50 m × 40 m plots serving as control, freshwater, and estuarine-water treatments. Transient saturation (5 h) of the entire soil rooting zone (0–30 cm) across a 2000 m 2 coastal forest was attained by delivering 300 m 3 of water through a spatially distributed irrigation network at a rate just above the soil infiltration rate. Our water delivery approach also elevated the water table (typically ~ 2 m belowground) and achieved extensive, low-level inundation (~ 8 cm standing water). A TEMPEST simulation approximated a 15-cm rainfall event and based on historic records, was of comparable intensity to a 10-year storm for the area. This characterization was supported by showing that Hurricane Ida’s (~ 5 cm rainfall) hydrologic impacts were shorter (40% lower duration) and less expansive (80% less coverage) than those generated through experimental manipulation. Future work will apply TEMPEST treatments to evaluate coastal forest resilience to changing hydrologic disturbance regimes and identify conditions that initiate ecosystem state transitions.

54 ENVIRONMENTAL SCIENCES↗
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↗