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DOE OSTI2022

Atoms and interatomic bonding synergism inherent in molecular electronic wave functions

The chemical model of matter consists of atoms held together by bonds. The success of this model implies that the physical interactions of the electrons and nuclei in molecules combine into compound interactions that create the bonding. In the quantum mechanical description, the modified atoms in molecules and the bonding synergism are contained in the molecular electronic wave function. So far, only part of this information has been recovered from the wave function. Notably, the atoms have remained unidentified in the wave function. One reason is that conventional energy decomposition analyses formulate separate model wave functions, independent of the actual wave function, to represent “prepared atoms” and preconceived interactions and, then, intuitively catenate the parts. In the present work, the embedded modified atoms and the inherent physical synergisms between them are determined by a unified derivation entirely from the actual molecular valence space wave function. By means of a series of intrinsic orbital and configurational transformations of the wave function, the energy of formation of a molecule is additively resolved in terms of intra-atomic energy changes, interference energies, and quasi-classical, non-classical, and charge-transfer Coulombic interactions. Here, the analysis furnishes an algorithm for the quantitative resolution of the energy of formation, which enables analyses elucidating reaction energies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Improved loss functions for machine-learned atomic potentials

Machine learning (ML) has become an invaluable tool across a wide array of domains in science as researchers find new ways to leverage its predictive power. This is especially true in chemistry, where ML is used to fit chemical properties or desirable attributes to the local structure of molecules and materials. In the pursuit of greater accuracy, it is relatively simple to increase the size or complexity of such models, although this often requires simultaneously seeking larger datasets in order to both fit and interpret the larger number of parameters. However, it is equally important to assess the quality and relative importance of the data and how these factors impact the training process. We, therefore, investigate the impact of using different loss functions for training neural network potentials (NNPs), as the loss function defines the error and parameter gradients used to train the NNP. In particular, we test the mean-squared error and Huber loss functions and, using insight from these functions, derive a new loss function based on the Asinh function, which yields significant improvement in the accuracy and generality of NNPs. We show that by discounting/minimizing errors and anomalies in the optimization process, both the Huber and Asinh loss functions improve the training of NNPs, leading to a final potential with a greater effective dimensionality.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Diamond surface functionalization via visible light–driven C–H activation for nanoscale quantum sensing

Nitrogen-vacancy (NV) centers in diamond are a promising platform for nanoscale NMR sensing. Despite significant progress toward using NV centers to detect and localize nuclear spins down to the single spin level, NV-based spectroscopy of individual, intact, arbitrary target molecules remains elusive. Such sensing requires that target molecules are immobilized within nanometers of NV centers with long spin coherence. The inert nature of diamond typically requires harsh functionalization techniques such as thermal annealing or plasma processing, limiting the scope of functional groups that can be attached to the surface. Solution-phase chemical methods can be readily generalized to install diverse functional groups, but they have not been widely explored for single-crystal diamond surfaces. Moreover, realizing shallow NV centers with long spin coherence times requires highly ordered single-crystal surfaces, and solution-phase functionalization has not yet been shown with such demanding conditions. In this work, we report a versatile strategy to directly functionalize C–H bonds on single-crystal diamond surfaces under ambient conditions using visible light, forming C–F, C–Cl, C–S, and C–N bonds at the surface. This method is compatible with NV centers within 10 nm of the surface with spin coherence times comparable to the state of the art. As a proof-of-principle demonstration, we use shallow ensembles of NV centers to detect nuclear spins from surface-bound functional groups. Our approach to surface functionalization opens the door to deploying NV centers as a tool for chemical sensing and single-molecule spectroscopy.

Science & Technology - Other Topics↗

DOE OSTI2019

Interactive Effects of Microbes and Nitrogen on Panicum virgatum Root Functional Traits and Patterns of Phenotypic Selection

Premise of research. Plants in natural and agricultural systems are influenced in myriad ways by their microbial communities, particularly by providing goods and services that change plant functional traits. Microbes are considered an influential part of the environmental context that change plant trait expression, but often, microbe-mediated effects are contingent on local resources, such as nitrogen. Here, we ask how microbes and nitrogen affect belowground functional traits and patterns of phenotypic selection. Methodology. We performed a fully factorial greenhouse experiment with switchgrass (Panicum virgatum), manipulating microbial community composition and nitrogen availability. We measured plant performance and belowground functional traits and performed 16S amplicon sequencing of the root-associated microbial communities. We looked for correlations between microbial taxa and root functional traits, and we performed phenotypic selection analysis on five belowground functional traits to determine how traits affect plant relative performance across biotic and abiotic contexts. Pivotal results. All belowground plant functional traits except root tissue density were affected by adding nitrogen. We found that a microbial taxon (amplicon sequence variant [ASV]) in the genus Micromonospora correlated with shorter root lengths. We also found strong positive selection for longer roots regardless of the abiotic or biotic environment. In contrast, selection favored lower root-to-shoot ratios in high-nitrogen conditions, and selection on root tissue density was highest in treatments that had high nitrogen levels and perturbed microbial communities. Conclusions. We did not detect microbial effects on the expression of plant traits (ecological effects); however, patterns of phenotypic selection (evolutionary effects) on root tissue density differed depending on the biotic and abiotic environment. Additionally, we detected strong selection for increased root length across treatments; we also found that one ASV correlated with decreased root length, indicating potential conflict between root microbiome components and plant fitness. Future work would be to include microbial taxa in phenotypic selection analysis and to conduct manipulations of the microbes correlated with functional traits to determine causality.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2017

Two-point correlation functions in inhomogeneous and anisotropic cosmologies

Two-point correlation functions are ubiquitous tools of modern cosmology, appearing in disparate topics ranging from cosmological inflation to late-time astrophysics. When the background spacetime is maximally symmetric, invariance arguments can be used to fix the functional dependence of this function as the invariant distance between any two points. In this paper we introduce a novel formalism which fixes this functional dependence directly from the isometries of the background metric, thus allowing one to quickly assess the overall features of Gaussian correlators without resorting to the full machinery of perturbation theory. As an application we construct the CMB temperature correlation function in one inhomogeneous (namely, an off-center LTB model) and two spatially flat and anisotropic (Bianchi) universes, and derive their covariance matrices in the limit of almost Friedmannian symmetry. We show how the method can be extended to arbitrary N -point correlation functions and illustrate its use by constructing three-point correlation functions in some simple geometries.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Towards accurate orbital-free simulations: A generalized gradient approximation for the noninteracting free energy density functional

For orbital-free ab initio molecular dynamics, especially on systems in extreme thermodynamic conditions, we provide the first pseudo-potential-adapted generalized gradient approximation (GGA) functional for the non-interacting free energy. This is achieved by systematic finite-temperature extension of our recent LKT ground state non-interacting kinetic energy GGA functional (Phys. Rev. B 98, 041111(R) (2018)). We test the performance of the new functional first via static lattice calculations on crystalline aluminum and silicon. Then we compare deuterium equation of state results against both path-integral Monte Carlo and conventional (orbital-dependent) Kohn-Sham results. The new functional, denoted LKTF, outperforms the previous best semi-local free energy functional, VT84F (Phys. Rev. B 88, 161108(R) (2013)), and provides modestly faster simulations. Finally, we discuss subtleties of identification of kinetic and entropic contributions to non-interacting free-energy functionals obtained by extension from ground state orbital-free kinetic energy functionals.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2017

Semilocal exchange hole with an application to range-separated density functionals

The exchange-correlation hole is a central concept in density functional theory. It not only provides justification for an exchange-correlation energy functional but also serves as a local ingredient for nonlocal range-separated density functionals. However, due to the nonlocal nature, modeling the conventional exact exchange hole presents a great challenge to density functional theory. In this work, we propose a semilocal exchange hole underlying the Tao-Perdew-Staroverov-Scuseria (TPSS) meta-generalized gradient approximation functional. Our model is distinct from previous ones not only at small separation between an electron and the hole around the electron but also in the way it interpolates between rapidly varying and slowly varying densities. Here the interpolation is determined by the wave-vector analysis on the infinite-barrier model for a jellium surface. Numerical tests show that our exchange-hole model mimics the conventional exact one quite well for atoms. As a simple application, we apply the hole model to construct a TPSS-based range-separated functional. We find that this range-separated functional can substantially improve the band gaps and barrier heights of TPSS, without losing much accuracy for atomization energies.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2024

Denoising and Extension of Response Functions in the Time Domain

Response functions of quantum systems, such as electron Green’s functions, magnetic, or charge susceptibilities, describe the response of a system to an external perturbation. They are the central objects of interest in field theories and quantum computing and measured directly in experiment. Further, response functions are intrinsically causal. In equilibrium and steady-state systems, they correspond to a positive spectral function in the frequency domain. Since response functions define an inner product on a Hilbert space and thereby induce a positive definite function, the properties of this function can be used to reduce noise in measured data and, in equilibrium and steady state, to construct positive definite extensions for data known on finite time intervals, which are then guaranteed to correspond to positive spectra.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2026

Finite-range pairing in nuclear density functional theory

Pairing correlations are ubiquitous in low-energy states of atomic nuclei. To incorporate them within nuclear density functional theory, often used for global computations of nuclear properties, pairing functionals that generate nucleonic pair densities and pairing fields are introduced. Many pairing functionals currently used can be traced back to zero-range nucleon-nucleon interactions. Unfortunately, such functionals are plagued by deficiencies that become apparent in large model spaces that contain unbound single-particle (continuum) states. In particular, the underlying computational schemes diverge as the single-particle space increases, and the results depend on how marginally occupied states are incorporated. These problems become more pronounced for pairing functionals that contain gradient-density dependence, such as in the Fayans functional. To remedy this, finite-range pairing functionals are introduced. In this study, this is done by folding the pair density with Gaussians. Here, we show that a folding radius of about 1 fm offers the best compromise between quality and stability, and substantially reduces the pathological behavior in different numerical applications.

Nuclear density functional theory↗

DOE OSTI2019

ManiNetCluster: a novel manifold learning approach to reveal the functional links between gene networks

BACKGROUND:The coordination of genomic functions is a critical and complex process across biological systems such as phenotypes or states (e.g., time, disease, organism, environmental perturbation). Understanding how the complexity of genomic function relates to these states remains a challenge. To address this, we have developed a novel computational method, ManiNetCluster, which simultaneously aligns and clusters gene networks (e.g., co-expression) to systematically reveal the links of genomic function between different conditions. Specifically, ManiNetCluster employs manifold learning to uncover and match local and non-linear structures among networks, and identifies cross-network functional links. RESULTS:We demonstrated that ManiNetCluster better aligns the orthologous genes from their developmental expression profiles across model organisms than state-of-the-art methods (p-value <2.2×10-16). This indicates the potential non-linear interactions of evolutionarily conserved genes across species in development. Furthermore, we applied ManiNetCluster to time series transcriptome data measured in the green alga Chlamydomonas reinhardtii to discover the genomic functions linking various metabolic processes between the light and dark periods of a diurnally cycling culture. We identified a number of genes putatively regulating processes across each lighting regime. CONCLUSIONS:ManiNetCluster provides a novel computational tool to uncover the genes linking various functions from different networks, providing new insight on how gene functions coordinate across different conditions. ManiNetCluster is publicly available as an R package at https://github.com/daifengwanglab/ManiNetCluster.

Comparative network analysis↗

DOE OSTI2016

An expanded evaluation of protein function prediction methods shows an improvement in accuracy

Background: A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging. Results: We conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2. Conclusions: The top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Climate mediates continental scale patterns of stream microbial functional diversity

Understanding the large-scale patterns of microbial functional diversity is essential for anticipating climate change impacts on ecosystems worldwide. However, studies of functional biogeography remain scarce for microorganisms, especially in freshwater ecosystems. Here we study 15,289 functional genes of stream biofilm microbes along three elevational gradients in Norway, Spain and China. We find that alpha diversity declines towards high elevations and assemblage composition shows increasing turnover with greater elevational distances. These elevational patterns are highly consistent across mountains, kingdoms and functional categories and exhibit the strongest trends in China due to its largest environmental gradients. Across mountains, functional gene assemblages differ in alpha diversity and composition between the mountains in Europe and Asia. Climate, such as mean temperature of the warmest quarter or mean precipitation of the coldest quarter, is the best predictor of alpha diversity and assemblage composition at both mountain and continental scales, with local non-climatic predictors gaining more importance at mountain scale. Under future climate, we project substantial variations in alpha diversity and assemblage composition across the Eurasian river network, primarily occurring in northern and central regions, respectively. We conclude that climate controls microbial functional gene diversity in streams at large spatial scales; therefore, the underlying ecosystem processes are highly sensitive to climate variations, especially at high latitudes. This biogeographical framework for microbial functional diversity serves as a baseline to anticipate ecosystem responses and biogeochemical feedback to ongoing climate change.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2017

Functional vs. Traditional Analysis in Biomechanical Gait Data: An Alternative Statistical Approach

In human motion studies, discrete points such as peak or average kinematic values are commonly selected to test hypotheses. The purpose of this study was to describe a functional data analysis and describe the advantages of using functional data analyses when compared with a traditional analysis of variance (ANOVA) approach. Nineteen healthy participants (age: 22 ± 2 yrs, body height: 1.7 ± 0.1 m, body mass: 73 ± 16 kg) walked under two different conditions: control and pain+effusion. Pain+effusion was induced by injection of sterile saline into the joint capsule and hypertonic saline into the infrapatellar fat pad. Sagittal-plane ankle, knee, and hip joint kinematics were recorded and compared following injections using 2×2 mixed model ANOVAs and FANOVAs. The results of ANOVAs detected a condition × time interaction for the peak ankle (F1,18 = 8.56, p = 0.01) and hip joint angle (F1,18 = 5.77, p = 0.03), but did not for the knee joint angle (F1,18 = 0.36, p = 0.56). The functional data analysis, however, found several differences at initial contact (ankle and knee joint), in the mid-stance (each joint) and at toe off (ankle). Although a traditional ANOVA is often appropriate for discrete or summary data, in biomechanical applications, the functional data analysis could be a beneficial alternative. Thus when using the functional data analysis approach, a researcher can (1) evaluate the entire data as a function, and (2) detect the location and magnitude of differences within the evaluated function.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Explainable Machine Learning for Functional Data

Black-box machine learning models are recognized as useful tools for prediction applications, but the algorithmic complexity of some models causes interpretation challenges. Explainability methods have been proposed to provide insight into these models, but there is little research focused on supervised modeling with functional data inputs. We argue that, especially in applications of high consequence, it is important to explicitly model the functional dependence in a black-box analysis to not obscure or misrepresent patterns in explanations. As such, we propose the V ariable importance E xplainable E lastic S hape A nalysis (VEESA) pipeline for training supervised machine learning models with functional inputs. The pipeline is an analysis process that includes the data preprocessing, modeling, and post-hoc explanations. The preprocessing is done using elastic functional principal components analysis, which accounts for vertical and horizontal variability in functional data and, ultimately, allows for explanations in the original data space that identify the important functional variability without bias due to correlated variables. Here, we demonstrate the pipeline on two high-consequence applications: explosives classification for national security and inkjet printer identification in forensic science. The applications exhibit the VEESA pipeline’s ability to provide an understanding of the characteristics of the functional data useful for prediction. Code for implementing the pipeline is available in the veesa R package (and supplemental python code).

Elastic Shape Analysis↗

NASA NTRS1987

Vestibulo-ocular and vestibulospinal function before and after cochlear implant surgery

Vestibular function in cochlear implant candidates varies from normal to total absence of function. In patients with intact vestibular function preoperatively, invasion of the otic capsule places residual vestibular function at risk. Speech-processing strategies that result in large amplitude electrical transients or strategies that employ high amplitude broad frequency carrier signals have the potential for disrupting vestibular function. Five patients were tested with and without electrical stimulation via cochlear electrodes. Two patients experienced subjective vestibular effects that were quickly resolved. No long-term vestibular effects were noted for the two types of second generation cochlear implants evaluated. Histopathological findings from another patient, who had electrically generated vestibular reflex responses to intramodiolar electrodes, indicated that responses elicited were a function of several variables including electrode location, stimulus intensity, stimulus amplitude, and stimulus frequency. Differential auditory, vestibulocolic, and vestibulospinal reflexes were demonstrated from the same electrode as a function of stimulus amplitude, frequency, and duration.

Case Reports↗

NASA NTRS1996

What is the minimal vestibular function required for compensation?

Living with an uncompensated, abnormal vestibular system requires oppressive modification of life style and often prevents return to work and activities of daily living. Patients with vestibular abnormalities were studied to determine the minimal residual vestibular function required to achieve compensation. Three groups of patients with (a) complete unilateral loss of vestibular function with normal horizontal canal-vestibulo-ocular (HCVOR) function in the opposite ear, (b) complete unilateral loss with abnormal HCVOR function in the opposite ear, and (c) bilateral reduction of vestibular function from aminoglycoside toxicity underwent vestibuloocular (VOR), optokinetic (OKN), visual-VOR (VVOR), and computerized dynamic posturography (CDP) tests before and after therapeutic procedures. Results suggest that a minimal VOR response amplitude must be present for compensation of VVOR function to occur. The roles of VOR and OKN phase shifts in vestibular compensation are more complicated and require further study. Compensation of vestibulospinal function does not necessarily accompany VOR or VVOR compensation. Ascending and descending vestibular compensatory mechanisms may involve different spatial sensory inputs. Results of these studies have important implications for the diagnosis and treatment of patients with vestibular disorders, including selection and monitoring of patients for therapeutic regimens such as vestibular nerve section and streptomycin therapy.

Non-NASA Center↗

NASA NTRS2011

The Necessity of Functional Analysis for Space Exploration Programs

As NASA moves toward expanded commercial spaceflight within its human exploration capability, there is increased emphasis on how to allocate responsibilities between government and commercial organizations to achieve coordinated program objectives. The practice of program-level functional analysis offers an opportunity for improved understanding of collaborative functions among heterogeneous partners. Functional analysis is contrasted with the physical analysis more commonly done at the program level, and is shown to provide theoretical performance, risk, and safety advantages beneficial to a government-commercial partnership. Performance advantages include faster convergence to acceptable system solutions; discovery of superior solutions with higher commonality, greater simplicity and greater parallelism by substituting functional for physical redundancy to achieve robustness and safety goals; and greater organizational cohesion around program objectives. Risk advantages include avoidance of rework by revelation of some kinds of architectural and contractual mismatches before systems are specified, designed, constructed, or integrated; avoidance of cost and schedule growth by more complete and precise specifications of cost and schedule estimates; and higher likelihood of successful integration on the first try. Safety advantages include effective delineation of must-work and must-not-work functions for integrated hazard analysis, the ability to formally demonstrate completeness of safety analyses, and provably correct logic for certification of flight readiness. The key mechanism for realizing these benefits is the development of an inter-functional architecture at the program level, which reveals relationships between top-level system requirements that would otherwise be invisible using only a physical architecture. This paper describes the advantages and pitfalls of functional analysis as a means of coordinating the actions of large heterogeneous organizations for space exploration programs.

program management↗

NASA NTRSDate not supplied

Defining A Modelling Language to Support Functional Hazard Assessment

Functional Hazard Assessment (FHA) is a key early-stage engineering process that supports the incorporation of safety in design by identifying the high-level functional hazards the system may encounter. While many FHA-like methodologies have been proposed in the design engineering literature, many of these methodologies have had difficulty becoming accepted industry practice. Industry standards, on the other hand, either provide too little recommendation on how to represent the function of the system to perform FHA, or rely on existing design artefacts which insufficiently support the goals of the process. This paper presents some of the problems with current modeling languages (both proposed and used) for FHA which limit the scope, expressiveness, flexibility, and precision of the analysis. It then outlines desirable principles an FHA-supporting analysis language should embody, and introduces the Functional Reasoning Design Language (FRDL), a formal modeling language for describing the functional elements of a system and their interactions, which aims to satisfy these principles. To demonstrate the use of this language, the modeling and hazard analysis of a disaster response drone is presented. While this case study is limited in scope, it highlights how FRDL can represent system function while reducing the ambiguity present in typical FHA-supporting functional modeling languages

Hazard Assessment↗