Correlating Interfacial Charge Transfer Rates with Interfacial Molecular Structure in the Tetraphenyldibenzoperiflanthene/C 70 Organic Photovoltaic System
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SEARCH · Engineering Papers
Results for “analytical methods”
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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Spin-Dependent Stereochemistry: A Nonadiabatic Quantum Dynamics Case Study of S + H 2 → SH + H Reaction
We study the spin-dependent stereodynamics of the S + H 2 → SH + H reaction by using full-dimensional quantum dynamics calculations with zero total nuclear angular momentum along the triplet 3 A" states and singlet 1 A' states. We find that the interplay between the electronic spin direction and the molecular geometry has a measurable influence on the singlet–triplet intersystem crossing reaction probabilities. Our results show that for some incident scattering angles in the body-fixed frame, the relative difference in intersystem crossing reaction probabilities (as determined between spin up and spin down initial states) can be as large as 15%. Furthermore, our findings are an ab initio demonstration of spin-dependent nonadiabatic dynamics, which we hope will shine light as far as understanding the chiral-induced spin selectivity effect.
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Single-Molecule Conductance through Hybrid Radially and Linearly π-Conjugated Macromolecules Reveals an Unusual Intramolecular π-Interaction
We describe the design, synthesis, and single-molecule junction conductance of π-electron molecules bearing both radial and linear π-conjugation pathways, whereby cycloparaphenylene (CPP) radial cores are π-extended linearly with aryl alkyne substituents as models for previously reported CPP-arylene ethynylene conjugated polymers. Although radially and linearly conjugated molecules have been studied previously in isolation as junction-bridging molecular electronic units, this is the first study to examine molecules where both topologies are operative. Our results reveal that the presence of radial CPP components within the junction-spanning pathway leads to a reduction in the conductance of the backbone compared to model linear phenyl substituents. Through tight-binding and DFT-based calculations, we attribute this conductance change to intramolecular van der Waals (vdW) interactions between the CPP ring and the junction-spanning arylene-ethynylene molecular backbone. These interactions induce changes in the dihedral angles of the backbone, leading to a reduced overlap of π orbitals within the molecular junction.
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Quantification of Metabolic Products from Microbial Hosts in Complex Media Using Optically Diffracting Hydrogels
In this work, we herein describe a highly versatile platform approach for the in situ and real-time screening of microbial biocatalysts for enhanced production of bioproducts using photonic crystal hydrogels. This approach was demonstrated by preparing optically diffracting films based on polymerized N-isopropylacrylamide that contracted in the presence of alcohols and organic acids. The hydrogel films were prepared in a microwell plate format, which allows for high-throughput screening, and characterized optically using a microwell plate reader. While demonstrating the ability to detect a broad range of relevant alcohols and organic acids, we showed that the response of the films correlated strongly with the octanol-water partition coefficient (log P) of the analyte. Differences in the secretion of ethanol and succinic acid from strains of Zymomonas mobilis and Actinobacillus succinogenes, respectively, were further detected via optical characterization of the films. These differences, which in some cases were as low as ~3 g/L, were confirmed by high-performance liquid chromatography, thereby demonstrating the sensitivity of this approach. Our findings highlight the potential utility of this multiplexed approach for the detection of small organic analytes in complex biological media, which overcomes a major challenge in conventional optical sensing methods.
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Effect of Framework Composition and NH 3 on the Diffusion of Cu + in Cu-CHA Catalysts Predicted by Machine-Learning Accelerated Molecular Dynamics
Cu-exchanged zeolites rely on mobile solvated Cu + cations for their catalytic activity, but the role of the framework composition in transport is not fully understood. Ab initio molecular dynamics simulations can provide quantitative atomistic insight but are too computationally expensive to explore large length and time scales or diverse compositions. We report a machine-learning interatomic potential that accurately reproduces ab initio results and effectively generalizes to allow multinanosecond simulations of large supercells and diverse chemical compositions. Biased and unbiased simulations of [Cu(NH 3 ) 2 ] + mobility show that aluminum pairing in eight-membered rings accelerates local hopping and demonstrate that increased NH 3 concentration enhances long-range diffusion. The probability of finding two [Cu(NH 3 ) 2 ] + complexes in the same cage, which is key for SCR-NOx reaction, increases with Cu content and Al content but does not correlate with the long-range mobility of Cu + . Supporting experimental evidence was obtained from reactivity tests of Cu-CHA catalysts with a controlled chemical composition.
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High Thermopower in a Zn-Based 3D Semiconductive Metal–Organic Framework
Conductive metal–organic frameworks (c-MOFs) have drawn increasing attention for their outstanding performance in energy-related applications. However, the majority of reported c-MOFs are based on 2D structures. Synthetic strategies for 3D c-MOFs are under-explored, leaving unrealized functionality in both their structures and properties. Here in this paper we report a 3D c-MOF, namely Zn-HAB, designed through topological analysis. Comprising a nitrogen donor linker, hexaaminobenzene, and Zn(II) node, Zn-HAB was found to have microporosity with a band-gap of approximately 1.68 eV. The relatively large band-gap results in a modest conduc-tivity of 0.86 mS cm -1 and a high Seebeck coefficient of 200 μV K -1 at 300 K. The power factor of 3.44 nW m -1 K -2 constitutes the first report of the thermoelectric properties of an intrinsically conductive 3D MOF.
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Au as a Surrogate for F: The Case of UAu 6 vs UF 6
Here, anion photoelectron spectroscopy and first-principles quantum chemistry are used to demonstrate to what degree Au can act as a surrogate for F in UF6 and its anion. Unlike UF6, UAu6 exhibits strong ligand–ligand, i.e., Au–Au, interactions, resulting in three low-lying isomers, two of which are three-dimensional while the third isomer has a ring-like quasi two-dimensional structure. Additionally, all the UAu6 isomers have open-shell electrons, which in nearly all cases are localized on the central U atom. As a result, the adiabatic electron affinity and vertical detachment energy are measured to be 3.05 ± 0.05 and 3.28 ± 0.05 eV, respectively, and are in very good agreement with calculations.
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Speciation and Reactivity Control of Cu-Oxo Clusters via Extraframework Al in Mordenite for Methane Oxidation
The stoichiometric conversion of methane to methanol by Cu-exchanged zeolites can be brought to highest yields by the presence of extraframework Al and high CH 4 chemical potentials. Here, combining theory and experiments, the differences in chemical reactivity of monometallic Cu-oxo and bimetallic Cu-Al-oxo nanoclusters stabilized in zeolite mordenite (MOR) are investigated. Cu-L 3 edge X-ray absorption near-edge structure (XANES), infrared (IR), and ultraviolet–visible (UV–vis) spectroscopies, in combination with CH 4 oxidation activity tests, support the presence of two types of active clusters in MOR and allow quantification of the relative proportions of each type in dependence of the Cu concentration. Ab initio molecular dynamics (MD) calculations and thermodynamic analyses indicate that the superior performance of materials enriched in Cu-Al-oxo clusters is related to the activity of two μ-oxo bridges in the cluster. Replacing H 2 O with ethanol in the product extraction step led to the formation of ethyl methyl ether, expanding this way the applicability of these materials for the activation and functionalization of CH 4 . We show that competition between different ion-exchanged metal-oxo structures during the synthesis of Cu-exchanged zeolites determines the formation of active species, and this provides guidelines for the synthesis of highly active materials for CH 4 activation and functionalization.
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Regioselective On-Surface Synthesis of [3]Triangulene Graphene Nanoribbons
The integration of low-energy states into bottom-up engineered graphene nanoribbons (GNRs) is a robust strategy for realizing materials with tailored electronic band structure for nanoelectronics. Low-energy zero-modes (ZMs) can be introduced into nanographenes (NGs) by creating an imbalance between the two sublattices of graphene. This phenomenon is exemplified by the family of [n]triangulenes (n ϵ $\mathbb{N}$). Here, we demonstrate the synthesis of [3]triangulene-GNRs, a regioregular one-dimensional (1D) chain of [3]triangulenes linked by five-membered rings. Hybridization between ZMs on adjacent [3]triangulenes leads to the emergence of a narrow band gap, E g,exp ~ 0.7 eV, and topological end states that are experimentally verified using scanning tunneling spectroscopy. Tight-binding and first-principles density functional theory calculations within the local density approximation corroborate our experimental observations. Our synthetic design takes advantage of a selective on-surface head-to-tail coupling of monomer building blocks enabling the regioselective synthesis of [3]triangulene-GNRs. Detailed ab initio theory provides insights into the mechanism of on-surface radical polymerization, revealing the pivotal role of Au-C bond formation/breakage in driving selectivity.
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Single-Molecule Detection of the Encounter and Productive Electron Transfer Complexes of a Photosynthetic Reaction Center
Small, diffusible redox proteins play an essential role in electron transfer (ET) in respiration and photosynthesis, sustaining life on Earth by shuttling electrons between membrane-bound complexes via finely tuned and reversible interactions. Ensemble kinetic studies show transient ET complexes form in two distinct stages: an “encounter” complex largely mediated by electrostatic interactions, which subsequently, through subtle reorganization of the binding interface, forms a “productive” ET complex stabilized by additional hydrophobic interactions around the redox-active cofactors. Here, using single-molecule force spectroscopy (SMFS) we dissected the transient ET complexes formed between the photosynthetic reaction center-light harvesting complex 1 (RC-LH1) of Rhodobacter sphaeroides and its native electron donor cytochrome c 2 (cyt c 2 ). Importantly, SMFS resolves the distribution of interaction forces into low (~150 pN) and high (~330 pN) components, with the former more susceptible to salt concentration and to alteration of key charged residues on the RC. Thus, the low force component is suggested to reflect the contribution of electrostatic interactions in forming the initial encounter complex, whereas the high force component reflects the additional stabilization provided by hydrophobic interactions to the productive ET complex. Employing molecular dynamics simulations, we resolve five intermediate states that comprise the encounter, productive ET and leaving complexes, predicting a weak interaction between cyt c 2 and the LH1 ring near the RC-L subunit that could lie along the exit path for oxidized cyt c 2 . The multimodal nature of the interactions of ET complexes captured here may have wider implications for ET in all domains of life.
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Predicting molecular dipole moments by combining atomic partial charges and atomic dipoles
The molecular dipole moment ( μ ) is a central quantity in chemistry. It is essential in predicting infrared and sum-frequency generation spectra as well as induction and long-range electrostatic interactions. Furthermore, it can be extracted directly—via the ground state electron density—from high-level quantum mechanical calculations, making it an ideal target for machine learning (ML). Here, we choose to represent this quantity with a physically inspired ML model that captures two distinct physical effects: local atomic polarization is captured within the symmetry-adapted Gaussian process regression framework which assigns a (vector) dipole moment to each atom, while the movement of charge across the entire molecule is captured by assigning a partial (scalar) charge to each atom. The resulting “MuML” models are fitted together to reproduce molecular μ computed using high-level coupled-cluster theory and density functional theory (DFT) on the QM7b dataset, achieving more accurate results due to the physics-based combination of these complementary terms. The combined model shows excellent transferability when applied to a showcase dataset of larger and more complex molecules, approaching the accuracy of DFT at a small fraction of the computational cost. We also demonstrate that the uncertainty in the predictions can be estimated reliably using a calibrated committee model. The ultimate performance of the models—and the optimal weighting of their combination—depends, however, on the details of the system at hand, with the scalar model being clearly superior when describing large molecules whose dipole is almost entirely generated by charge separation. These observations point to the importance of simultaneously accounting for the local and non-local effects that contribute to μ ; furthermore, they define a challenging task to benchmark future models, particularly those aimed at the description of condensed phases.
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Machine learning Frenkel Hamiltonian parameters to accelerate simulations of exciton dynamics
In this manuscript, we develop multiple machine learning (ML) models to accelerate a scheme for parameterizing site-based models of exciton dynamics from all-atom configurations of condensed phase sexithiophene systems. This scheme encodes the details of a system’s specific molecular morphology in the correlated distributions of model parameters through the analysis of many single-molecule excited-state electronic-structure calculations. These calculations yield excitation energies for each molecule in the system and the network of pair-wise intermolecular electronic couplings. In this work, we demonstrate that the excitation energies can be accurately predicted using a kernel ridge regression (KRR) model with Coulomb matrix featurization. We present two ML models for predicting intermolecular couplings. The first one utilizes a deep neural network and bi-molecular featurization to predict the coupling directly, which we find to perform poorly. The second one utilizes a KRR model to predict unimolecular transition densities, which can subsequently be analyzed to compute the coupling. We find that the latter approach performs excellently, indicating that an effective, generalizable strategy for predicting simple bimolecular properties is through the indirect application of ML to predict higher-order unimolecular properties. Such an approach necessitates a much smaller feature space and can incorporate the insight of well-established molecular physics.
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Sub-system self-consistency in coupled cluster theory
Here, in this article, we provide numerical evidence indicating that the single-reference coupled-cluster (CC) energies can be calculated alternatively to their copybook definition. We demonstrate that the CC energy can be reconstructed by diagonalizing the effective Hamiltonians describing correlated sub-systems of the many-body system. In the extreme case, we provide numerical evidence that the CC energy can be reproduced through the diagonalization of the effective Hamiltonian describing sub-system composed of a single electron. These properties of the CC formalism can be exploited to design protocols to define effective interactions in sub-systems used as probes to calculate the energy of the entire system and introduce a new type of self-consistency for approximate CC approaches.
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Chromatic effects on the PSF and shear measurement for the Roman Space Telescope High-Latitude Wide Area Survey
Weak gravitational lensing (WL) is a key cosmological probe that requires precise measurement of galaxy images to infer shape distortions, or shear, and constrain cosmology. Accurate estimation of the point spread function (PSF) is crucial for shear measurement, but the wavelength dependence of the PSF introduces chromatic biases that can systematically impact shear inference. We focus on biases arising from spectral energy distribution (SED) differences between stars, used for PSF modelling, and galaxies, used for shear measurement. We investigate these effects in Roman’s four design reference mission WL bands (Y106, J129, H158, F184) and wide filter (W146). Using Roman-like image simulations, we quantify the induced shear biases and compare them to requirements on those biases. Multiplicative biases over all galaxies hover around ~0.2 per cent in the WL bands and 2 per cent in the wide filter, exceeding the mission requirement of |m| < 0.032 per cent and relaxed requirement of |m| < 0.1 per cent. In individual redshift bins, biases can reach 0.4–0.9 per cent for the WL bands and 3–6 per cent for the wide filter. Additive biases remain acceptable in the WL bands but exceed systematic limits in the wide filter. We develop and test PSF-level corrections, showing that a first-order correction reduces biases within survey requirements for the WL bands; however, higher-order terms are necessary for the wide filter. Our results highlight the necessity of chromatic corrections for precision WL with Roman and provide a framework for mitigating these biases. Finally, we compare analytical colour-based corrections to self-organizing maps and find that both methods effectively reduce biases.
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Learning unknown physics of non-Newtonian fluids
Here, we use physics-informed neural networks (PINNs) to learn viscosity models of two non-Newtonian systems (polymer melts and suspensions of particles) using only velocity measurements. For synthetic velocity data generated with the power-law viscosity model, the PINN-inferred viscosity model agrees with the analytical model for shear rates with large absolute values but deviates for shear rates near zero where the analytical model has an unphysical singularity. Once the viscosity model is learned the PINN method can solve the momentum conservation equation using only the boundary conditions.
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Positive- and negative-frequency noise from an ensemble of two-level fluctuators
The analysis of charge noise based on the Bloch-Redfield treatment of an ensemble of dissipative two-level fluctuators generally results in a violation of the fluctuation-dissipation theorem. The standard Markov approximation (when applied to the two-level fluctuators coupled to a bath) can be identified as the main origin of this failure. The resulting decoherence rates only involve the bath response at the fluctuator frequency, and thus completely neglect the effects of frequency broadening. A systematic and computationally convenient way to overcome this issue is to employ the spectator-qubit method: by coupling an auxiliary qubit to the two-level fluctuator ensemble, an analytical approximation for S(ω) fully consistent with the fluctuation-dissipation theorem can be obtained. We discuss the resulting characteristics of the noise which exhibits distinct behavior over several frequency ranges, including a 1/f to 1/f2 crossover with a T3 temperature dependence of the crossover frequency.
2-dimensional systems↗
CoSim: A Simulator for Co-Scheduling of Batch and On-Demand Jobs in HPC Datacenters
The increasing scale and complexity of scientific applications are rapidly transforming the ecosystem of tools, methods, and workflows adopted by the high-performance computing (HPC) community. Big data analytics and deep learning are gaining traction as essential components in this ecosystem in a variety of scenarios, such as, steering of experimental instruments , acceleration of high-fidelity simulations through surrogate computations, and guided ensemble searches. In this context, the batch job model traditionally adopted by the supercomputing in-frastructures needs to be complemented with support to schedule opportunistic on-demand analytics jobs, leading to the problem of efficient preemption of batch jobs with minimum loss of progress. In this paper, we design and implement a simulator, CoSim, that enables on-the-fly analysis of the trade-offs arising between delaying the start of opportunistic on-demand jobs, which leads to longer analytics latency, and loss of progress due to preemption of batch jobs, which is necessary to make room for on-demand jobs. To this end, we propose an algorithm based on dynamic programming with predictable performance and scalability that enables supercomputing infrastructure schedulers to analyze the aforementioned trade-off and take decisions in near real-time. Compared with other state-of-art approaches using traces of the Theta pre-Exascale machine, our approach is capable of finding the optimal solution, while achieving high performance and scalability.
batch job preemption↗
Wcomp (Wind Farm Wake Comparison Framework) [SWR-23-72]
The Wind Farm Wake Comparison Framework (Wcomp) is a software tool to facilitate the comparison of a specific collection of wind farm wake modeling tools: Python-based, steady-state, analytical wake modeling utilities. Wcomp integrates another software project, windIO, to create a consistent method for describing a wind farm flow control problem. Additionally, a data structure is included to represent the outputs a wind farm flow control simulation. Well-described interfaces allow existing wake modeling tools to plug into this framework.
Mudafort, Rafael↗