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DOE OSTI2022

Structural insights into the assembly and the function of the plant oxidative phosphorylation system

One of the key functions of mitochondria is the production of ATP to support cellular metabolism and growth. The last step of mitochondrial ATP synthesis is performed by the oxidative phosphorylation (OXPHOS) system, an ensemble of protein complexes embedded in the inner mitochondrial membrane. In the last 25 yr, many structures of OXPHOS complexes and supercomplexes have been resolved in yeast, mammals, and bacteria. However, structures of plant OXPHOS enzymes only became available very recently. In this review, we highlight the plant-specific features revealed by the recent structures and discuss how they advance our understanding of the function and assembly of plant OXPHOS complexes. Here we also propose new hypotheses to be tested and discuss older findings to be re-evaluated. Further biochemical and structural work on the plant OXPHOS system will lead to a deeper understanding of plant respiration and its regulation, with significant agricultural, environmental, and societal implications.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

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.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Residue Specific Characterization of Electrostatics, Conformational Heterogeneity, and Dynamics of Electron Transfer Proteins with Linear and Nonlinear Infrared Spectroscopy. Final Report

The goal of this research project was to further our understanding of biological electron transfer through the development and application of new methods for studying proteins and their complexes with high spatial and temporal detail. The approach was based on incorporation of reporter bonds with distinct vibrational frequencies at specific locations in proteins and their characterization through the interaction of the bonds with pulses of infrared light. The project focused on the proteins plastocyanin (Pc) and cytochrome (cyt) f, two electron transfer partners in photosynthesis. The first thrust of the project was the incorporation of carbon deuterium bonds as non-perturbative probes at a ligand to the copper ion of Pc and their use to detect changes in the bonding due to the number of electrons at the metal and how the bonding changes upon association with cyt f. Toward developing more advanced approaches to characterize proteins, we incorporated a stronger reporter, the cyano group of cyanophenylalanine, at several locations in Pc and measured the frequency dynamics in the absence and presence of cyt f. The acquired data improved the current model for the protein complex. Notably, the data indicated that the majority of the population is involved in a loosely bound complex, providing insight into the nature of the ensembles formed by electron transfer protein complexes that is not available via other approaches. In addition to learning about the protein complex, we made strides toward developing and improving the new experimental approach to study highly dynamic behaviors of proteins.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Quantifying uncertainty for deep learning based forecasting and flow-reconstruction using neural architecture search ensembles

Classical problems in computational physics such as data-driven forecasting and signal reconstruction from sparse sensors have recently seen an explosion in deep neural network (DNN) based algorithmic approaches. However, most DNN models do not provide uncertainty estimates, which are crucial for establishing the trustworthiness of these techniques in downstream decision making tasks and scenarios. In recent years, ensemble-based methods have achieved significant success for the uncertainty quantification in DNNs on a number of benchmark problems. However, their performance on real-world applications remains under-explored. In this work, we present an automated approach to DNN discovery and demonstrate how this may also be utilized for ensemble-based uncertainty quantification. Specifically, we propose the use of a scalable neural and hyperparameter architecture search for discovering an ensemble of DNN models for complex dynamical systems. We highlight how the proposed method not only discovers high-performing neural network ensembles for our tasks, but also quantifies uncertainty seamlessly. This is achieved by using genetic algorithms and Bayesian optimization for sampling the search space of neural network architectures and hyperparameters. Subsequently, a model selection approach is used to identify candidate models for an ensemble set construction. Afterwards, a variance decomposition approach is used to estimate the uncertainty of the predictions from the ensemble. We demonstrate the feasibility of this framework for two tasks — forecasting from historical data and flow reconstruction from sparse sensors for the sea-surface temperature. In conclusion, we demonstrate superior performance from the ensemble in contrast with individual high-performing models and other benchmarks.

Deep ensembles↗

DOE OSTI2026

Ensemble Effects on Hydroxide Bond Dissociation Free Energies in Polyoxovanadate Clusters

Understanding structure-property relationships is foundational to numerous modern chemistries, such as proton-coupled electron transfer (PCET). However, an experimentally measured property is the result of the behavior from an ensemble of molecules. Neglecting ensemble effects, especially under complex chemical environments, may obfuscate these relationships and lead to discrepancies between theory and experiment. In this work, we demonstrate the impact of configurational entropy and local chemical environments on hydroxide bond dissociation free energies [BDFE- (O−H)] for a set of polyoxovanadate nanoclusters, at ambient conditions. The O−H bond strengths are investigated via density functional theory (DFT) coupled with statistical thermodynamic analysis and bilinear modeling, and compared with previous experimental results on the same systems, namely electrochemical solutions of: [V 6 O 13−x (OH) x (TRIOL R ) 2 ] −2 (x = 2, 4, 6; R = NO 2 , Me) and [V 6 O 11−x (OMe) 2 (OH) x (TRIOL NO 2 ) 2 ] −2 (x = 2, 4). Interestingly, we find that ensemble effects, even at room temperature, can account for a significant portion of the BDFE(O−H) trend with the degree of reduction via H atom binding, which cannot be fully captured by single-structure, static DFT calculations. Moreover, we find that the ensemble effects may be replicated statistically, requiring only enumeration of energetically accessible H-binding sites. With the ensemble effects resolved, we present a simple bilinear model to reconcile remaining biases between experiment and ensemble-informed theory, which corelate with clusterspecific electronic environment differences. The bilinear model achieves outstanding accuracy vs experiments with a root-mean squared error of 0.4 kcal/mol. Finally, based on the physicochemical characteristics of hydrogen interaction with polyoxometalates, we present a simple methodology that captures the BDFE(O−H) trend while dramatically reducing required DFT calculations by 98% and achieving accuracy within 1 kcal/mol. Overall, this work elucidates the roles and structural origins of configurational entropy and chemical effects on polyoxometalate hydroxide bond energies, with potential applicability to various atomically precise metal oxide systems. Importantly, it introduces models for rapid and highly accurate property calculations in connection with experiments.

Cluster chemistry↗

DOE OSTI2026

Charge Transport in Solvated Donor–Acceptor Functionalized Peptoids: Molecular Dynamics and Rate Theory

Scalable solar-energy conversion requires photoactive materials that combine the efficiency of natural photosynthetic systems with the stability and processability needed for practical applications. Achieving reliable charge transport in soft, self-assembled organic materials remains challenging, as structural fluctuations and environmental effects strongly influence charge-transfer (CT) rates. Here, we present a broadly applicable computational framework for evaluating CT rates in the condensed phase, combining Fermi’s golden rule rate theory with inputs from all-atom molecular dynamics (MD) simulations and first-principles electronic-structure calculations. The approach does not rely on system-specific parametrization and is applicable to a wide range of soft and disordered materials. We demonstrate the applicability and usefulness of the framework on redox-active peptoids functionalized with iron–porphyrin (Fe–P) complexes, a bioinspired platform with programmable donor–acceptor units and tunable three-dimensional organization. The calculated CT rates exhibit strong sensitivity to molecular conformation, with variations spanning several orders of magnitude. This dependence is shown to arise from the pronounced variation in diabatic electronic coupling with the relative orientations and separations of the Fe–P complexes across the conformational ensemble. The framework provides a consistent route for connecting atomistic structure to CT kinetics in the condensed phase and enables analysis of structure–rate relations in organic semiconducting systems.

Charge transfer↗

DOE OSTI2020

Analytical integration of the tractions induced by non-singular dislocations on an arbitrary shaped triangular quadratic element

Here, an analytical model is proposed to evaluate the nodal force induced by a segment of dislocation upon an arbitrary shaped triangular element. This calculation is required in hybrid methods that associate dislocation dynamics to boundary or finite element to solve simultaneously the evolution of large ensembles of dislocations with complex boundary conditions. Nodal forces are defined as the triple integration of the unbalanced traction field induced by a straight dislocation upon the surface of the element. Following our previous approach (Queyreau et al 2014 Modelling Simul. Mater. Sci. Eng. 22 035004) on a simpler geometry and in the case of linear isotropic elasticity, triple integrals are solved by sequences of integration by parts that exhibit recurrence relations. The traction field is defined and finite everywhere even at the core of dislocations, thanks to the use of the non-singular stress expression formulated by Cai et al (2006 J. Mech. Phys. Solids 54 561–587). The nodal force expressions can be used when considering both a single convolution or double convolution of the Green's function with the core distribution. A solution is also proposed for the case of a semi-infinite segment through the study of the asymptotic behavior of the analytical expressions. The proposed approach is exact and very computationally efficient. The choice of an arbitrary shaped triangular element and quadratic shape functions allow the consideration of complex geometries and comply with automatic meshing procedures. These analytical expressions could also be employed to estimate dislocation interactions with interfaces.

42 ENGINEERING↗

DOE OSTI2020

Vanadium spin qubits as telecom quantum emitters in silicon carbide

Solid-state quantum emitters with spin registers are promising platforms for quantum communication, yet few emit in the narrow telecom band necessary for low-loss fiber networks. Here, we create and isolate near-surface single vanadium dopants in silicon carbide (SiC) with stable and narrow emission in the O band, with brightness allowing cavity-free detection in a wafer-scale material. In vanadium ensembles, we characterize the complex d 1 orbital physics in all five available sites in 4H-SiC and 6H-SiC. The optical transitions are sensitive to mass shifts from local silicon and carbon isotopes, enabling optically resolved nuclear spin registers. Optically detected magnetic resonance in the ground and excited orbital states reveals a variety of hyperfine interactions with the vanadium nuclear spin and clock transitions for quantum memories. Last, we demonstrate coherent quantum control of the spin state. These results provide a path for telecom emitters in the solid state for quantum applications.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2023

Data Science Infrastructure SOFTWARE

LANL science workflows generate complex data sets and ensembles of data requiring significant compute and storage resources. The Data Science Infrastructure (DSI) project focuses on data-driven approaches to make data more readily available to LANL projects. DSI workflows leverage metadata stored in data-agnostic databases, supported by an abstraction layer API to simplify searching and accessing data across simulation runs, experimental runs, filesystems and environments. The abstraction layer API allows the user to query a range of data types: raw output, processed data, configuration data, machine learning models, performance data, etc. In addition to the abstraction backend API, the DSI project is developing client-driven query APIs and UIs to support specific user workflows.

Turton, Terece↗

NASA NTRSDate not supplied

Response of the QBO to Abrupt Increases in CO2 Using Three Atmospheric Chemistry Configurations

Long-term projections of the Quasi-Biennial Oscillation (QBO) remain highly uncertain. This is partly due to the paucity of models which are able to properly simulate that phenomenon. Only 5 of the 47 CMIP5 models are capable of spontaneously generating a realistic QBO (Butchart et al., 2018), and even those models exhibit large biases in key QBO characteristics (e.g. amplitude, period, vertical extent) when compared with observations. Furthermore, only 1 of these 5 employed interactive atmospheric chemistry, which is known to modulate QBO dynamics. We here investigate the QBO response to increased greenhouse gases using the NASA Goddard Institute for Space Studies Middle Atmosphere Model E2.2. Compared to lower vertical resolution versions of Model E, version 2.2 has a higher model top (0.002 hPa), and additional interactive non-orographic gravity wave drag sources from convection and shear, which produce a sufficiently realistic QBO, thus rendering it suitable for use in climate change studies. Steady-state responses to doubled and quadrupled CO2 from a pre-industrial control are analyzed, as well as the transient response to a 1% per year CO2 increase. In addition, we systematically explore the impact of interactive chemistry in modulating the QBO response to increased CO2 by contrasting interactive, prescribed, and linearized ozone chemistry configurations of the model. Overall, in response to increase CO2 concentrations the QBO is seen to increase in frequency and weaken in amplitude, consistent with previous results, but the memory of the tropical stratosphere may complicate assessments of trends in chemistry and surface impacts. We also discuss implications for the trade-off between ensemble size and the complexity of the chemistry scheme in the model.

Quasi-Biennial Oscillation (QBO)↗

DOE OSTI2024

CO 2 storage site characterization using ensemble-based approaches with deep generative models

Estimating spatially distributed properties such as permeability from available sparse measurements is a great challenge in efficient subsurface CO 2 storage operations. In this paper, a deep generative model that can accurately capture complex subsurface structure is tested with an ensemble-based inversion method for accurate and accelerated characterization of CO 2 storage sites. We chose Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) for its realistic reservoir property representation and Ensemble Smoother with Multiple Data Assimilation (ES-MDA) for its robust data fitting and uncertainty quantification capability. WGAN-GP are trained to generate high-dimensional permeability fields from a low-dimensional latent space and ES-MDA then updates the latent variables by assimilating available measurements. Several subsurface site characterization examples including Gaussian, channelized, and fractured reservoirs are used to evaluate the accuracy and computational efficiency of the proposed method and the main features of the unknown permeability fields are characterized accurately with reliable uncertainty quantification. Furthermore, the estimation performance is compared with a widely-used variational, i.e., optimization-based, inversion approach, and the proposed approach outperforms the variational inversion method in several benchmark cases. We explain such superior performance by visualizing the objective function in the latent space: because of nonlinear and aggressive dimension reduction via generative modeling, the objective function surface becomes extremely complex while the ensemble approximation can smooth out the multi-modal surface during the minimization. This suggests that the ensemble-based approach works well over the variational approach when combined with deep generative models at the cost of forward model runs unless convergence-ensuring modifications are implemented in the variational inversion.

42 ENGINEERING↗

DOE OSTI2023

Structural characterization of an intrinsically disordered protein complex using integrated small-angle neutron scattering and computing

Characterizing structural ensembles of intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) of proteins is essential for studying structure–function relationships. Due to the different neutron scattering lengths of hydrogen and deuterium, selective labeling and contrast matching in small-angle neutron scattering (SANS) becomes an effective tool to study dynamic structures of disordered systems. However, experimental timescales typically capture measurements averaged over multiple conformations, leaving complex SANS data for disentanglement. We hereby demonstrate an integrated method to elucidate the structural ensemble of a complex formed by two IDRs. We use data from both full contrast and contrast matching with residue-specific deuterium labeling SANS experiments, microsecond all-atom molecular dynamics (MD) simulations with four molecular mechanics force fields, and an autoencoder-based deep learning (DL) algorithm. From our combined approach, we show that selective deuteration provides additional information that helps characterize structural ensembles. We find that among the four force fields, a99SB-disp and CHARMM36m show the strongest agreement with SANS and NMR experiments. In addition, our DL algorithm not only complements conventional structural analysis methods but also successfully differentiates NMR and MD structures which are indistinguishable on the free energy surface. Finally, we present an ensemble that describes experimental SANS and NMR data better than MD ensembles generated by one single force field and reveal three clusters of distinct conformations. Our results demonstrate a new integrated approach for characterizing structural ensembles of IDPs.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Data and scripts associated with the manuscript "Using Mutual Information for Global Sensitivity Analysis on Watershed Modeling"

This package contains the data and scripts used in "Using Mutual Information for Global Sensitivity Analysis on Watershed Modeling" (Jiang et al., 2022). The ARW_SWAT.zip file contains the SWAT simulation and the sensitivity analysis result in the American River Watershed. The Portage_SWAT.zip file contains the SWAT simulation and the sensitivity analysis result in the Portage River Watershed. The src folder contains the source code of sensitivity analysis and plotting functions. The notebooks folder contains the Jupyter notebooks for performing sensitivity analysis and other post analyses.Global Sensitivity Analysis (GSA) often is applied to assess the sensitivity of model outputs to their inputs using ensemble simulations. However, increasing model complexity and the associated computational cost have limited the use of most GSA approaches for process-based watershed models. We propose to use Mutual Information (MI) as a computationally efficient GSA method for watershed modeling. Such MI computed from several hundred realizations usually can capture nonlinear relationships between inputs and outputs of interest. We perform MI-based watershed sensitivity analyses in studies of the Portage River Watershed in Ohio and the American River Watershed in Washington. In these studies, MI is used to evaluate the sensitivity of river discharges simulated by the Soil and Water Assessment Tool to no less than 20 parameters for each watershed. Our MI-based sensitivity analyses achieved convergence with about 300~500 realizations, a small fraction of the ensemble size (i.e., several thousand) required by the Sobol method. Nevertheless, MI yields similar sensitivity ranking compared to the Sobol method, especially for sensitive parameters. Our study thus sheds new light on the use of MI as an affordable GSA method for computationally intensive models such as the hyper-resolution, watershed hydrobiogeochemical models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Single-Point Extrapolation to the Complete Basis Set Limit through Deep Learning

Machine learning (ML) offers an attractive method for making predictions about molecular systems while circumventing the need to run expensive electronic structure calculations. Once trained on ab initio data, the promise of ML is to deliver accurate predictions of molecular properties that were previously computationally infeasible. In this work, we develop and train a graph neural network model to correct the basis set incompleteness error (BSIE) between a small and large basis set at the RHF and B3LYP levels of theory. Our results show that, when compared to fitting to the total potential, an ML model fitted to correct the BSIE is better at generalizing to systems not seen during training. We test this ability by training on single molecules while evaluating on molecular complexes. We also show that ensemble models yield better behaved potentials in situations where the training data is insufficient. However, even when only fitting to the BSIE, acceptable performance is only achieved when the training data sufficiently resemble the systems one wants to make predictions on. As a result, the test error of the final model trained to predict the difference between the cc-pVDZ and cc-pV5Z potential is 0.184 kcal/mol for the B3LYP density functional, and the ensemble model accurately reproduces the large basis set interaction energy curves on the S66x8 dataset.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2011

Preface for the ILWS Special Issue of JASTP

During the week of October 4-9,2009, about 160 participants from 19 countries met at the Itamambuca resort area of Ubatuba, Sao Paulo, Brazil to discuss the "Influence of solar variability on geophysical and heliospheric phenomena" Conference, organized by the International Living With a Star (ILWS) Program of NASA and by the National Institute of Space Research (INPE) of Brazil. This special issue collects 38 papers from that Conference, with 3 papers dealing with solar topics, 12 with interplanetary and cosmic rays,12 with magnetospheric, 9 with the low-latitude ionosphere and 2 with solar terrestrial system and climate. Furthermore six of the review papers presented at that Conference are being published in a special issue of the SpaceScience Reviews journal. The combined ensemble of papers describes the complex series of phenomena that link the Sun to the Earth.

Gonzalez, Walter D.↗

NASA NTRS2008

Discrete Element Modeling of Triboelectrically Charged Particles

Tribocharging of particles is common in many processes including fine powder handling and mixing, printer toner transport and dust extraction. In a lunar environment with its high vacuum and lack of water, electrostatic forces are an important factor to consider when designing and operating equipment. Dust mitigation and management is critical to safe and predictable performance of people and equipment. The extreme nature of lunar conditions makes it difficult and costly to carry out experiments on earth which are necessary to better understand how particles gather and transfer charge between each other and with equipment surfaces. DEM (Discrete Element Modeling) provides an excellent virtual laboratory for studying tribocharging of particles as well as for design of devices for dust mitigation and for other purposes related to handling and processing of lunar regolith. Theoretical and experimental work has been performed pursuant to incorporating screened Coulombic electrostatic forces into EDEM, a commercial DEM software package. The DEM software is used to model the trajectories of large numbers of particles for industrial particulate handling and processing applications and can be coupled with other solvers and numerical models to calculate particle interaction with surrounding media and force fields. While simple Coulombic force between two particles is well understood, its operation in an ensemble of particles is more complex. When the tribocharging of particles and surfaces due to frictional contact is also considered, it is necessary to consider longer range of interaction of particles in response to electrostatic charging. The standard DEM algorithm accounts for particle mechanical properties and inertia as a function of particle shape and mass. If fluid drag is neglected, then particle dynamics are governed by contact between particles, between particles and equipment surfaces and gravity forces. Consideration of particle charge and any tribocharging and electric field effects requires calculation of the forces due to these effects.

Hogue, Michael D.↗

DOE OSTI2023

Manipulation of self-organized multi-vortical states in active magnetic roller suspensions

Ensembles of active magnetic colloids demonstrate complex collective behavior and self-organization when driven out of equilibrium by external magnetic fields. The interplay between magnetic and hydrodynamic interactions often result in spontaneous self-assembly and the emergence of localized vortices in ensembles of ferromagnetic rollers. We report experimental and computational study of the self-organized multi-vortical state in ensembles of magnetic rollers under the imposed external confining potential realized by patterned substrates. We explore the behavior of the system on a substrate patterned with shallow wells ordered in a lattice with square symmetry and lattice spacing smaller than the wells’ diameter. Here we demonstrate, that ensembles of active magnetic rollers evolve in response to changes in the geometry of the confining lattice from a globally correlated state characterized by anti-ferromagnetic vortex ordering to a state with rapidly changing particle flows and self-organized vortical states with a lattice constant larger than one of the confining pattern. Our experimental observations are accompanied by numerical simulations based on phenomenological coarse grained particle dynamics coupled to Navier–Stokes hydrodynamics in shallow water approximation. The reported results provide insights into the collective behavior of active magnetic rollers under confinement and suggest strategies for the manipulation of collective dynamic states in active magnetic liquids.

36 MATERIALS SCIENCE↗

NASA NTRS1981

Asymptotic form of the longitudinal correlation function for isotropic homogeneous turbulence

An asymptotic form is derived for the longitudinal correlation function for isotropic homogeneous turbulence in an incompressible fluid governed by the Navier-Stokes equation. The result is obtained from an analysis of the algebraic-differential structure of the two-point correlation tensor contained in the complex-valued Fourier transform of the probability measure over the turbulence ensemble, which reveals that the Hopf characteristic functional (the complex-valued Fourier transform of the probability measure) satisfies the Fourier interference inequality. Consequences of the expression obtained, in which the correlation function is a positive definite function of the inverse cube of the spatial coordinate as it approaches infinity, are shown to include the nonexistence of the Loitsianskii invariant, and the solution is shown to be consistent with empirical formulas.

Rosen, G.↗