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All sourcesNASA NTRSDOE OSTI
NASA NTRS2001

Methodology Development for Assessment of Spaceport Technology Returns and Risks

As part of Kennedy Space Center's (KSC's) challenge to open the space frontier, new spaceport technologies must be developed, matured and successfully transitioned to operational systems. R&D investment decisions can be considered from multiple perspectives. Near mid and far term technology horizons must be understood. Because a multitude of technology investment opportunities are available, we must identify choices that promise the greatest likelihood of significant lifecycle At the same time, the costs and risks of any choice must be well understood and balanced against its potential returns The problem is not one of simply rank- ordering projects in terms of their desirability. KSC wants to determine a portfolio of projects that simultaneously satisfies multiple goals, such as getting the biggest bang for the buck, supporting projects that may be too risky for private funding, staying within annual budget cycles without foregoing the requirements of a long term technology vision, and ensuring the development of a diversity of technologies that, support the variety of operational functions involved in space transportation. This work aims to assist in the development of in methods and techniques that support strategic technology investment decisions and ease the process of determining an optimal portfolio of spaceport R&D investments. Available literature on risks and returns to R&D is reviewed and most useful pieces are brought to the attention of the Spaceport Technology Development Office (STDO). KSC's current project management procedures are reviewed. It is found that the "one size fits all" nature of KSC's existing procedures and project selection criteria is not conducive to prudent decision-making. Directions for improving KSC's - procedures and criteria are outlined. With help of a contractor, STDO is currently developing a tool, named Change Management Analysis Tool (CMAT)/ Portfolio Analysis Tool (PAT), to assist KSC's R&D portfolio determination. A critical review of CMAT/PAT is undertaken. Directions for the improvement of this tool are provided. STDO and KSC intend to follow up on many, if not all, of the recommendations provided.

Joglekar, Prafulla↗
NASA NTRS2016

Options for Staging Orbits in Cis-Lunar Space

NASA has been studying options to conduct missions beyond Low Earth Orbit, but within the Earth‐Moon system, in preparation for deep space exploration including human missions to Mars. Referred to as the Proving Ground, this arena of exploration activities will enable the development of human spaceflight systems and operations to satisfy future exploration objectives beyond the cis‐lunar environment. One option being considered includes the deployment of a habitable element or elements, which could be used as a central location for aggregation of supplies and resources for human missions in cis‐lunar space and beyond. Characterizing candidate orbit locations for this asset and the impacts on system design and mission operations is important in the overall assessment of the options being considered. The orbits described in this paper were initially selected by taking advantage of previous studies conducted by NASA and the work of other authors. In this paper orbits are assessed for their relative attractiveness based on various factors. A set of constraints related to the capability of the combined Orion and SLS system to deliver humans and cargo to and from the orbit are evaluated. Deployed assets intended to spend multiple years in the Proving Ground would ideally require minimal station keeping costs to reduce the mass budget allocated to this function. Additional mission design drivers include eclipse frequency, potential for uninterrupted communication with deployed assets, thermal, attitude control, communications, and other operational implications. Also the ability to support potential lunar surface activities and excursion missions beyond Earth‐Moon space is considered. The results of the characterization and evaluation of the selected orbits indicate a Near Rectilinear Orbit (NRO) is an attractive candidate as an aggregation point or staging location for operations. In this paper, the NRO is further described in terms which balance a number of key attributes that favor a variety of mission classes to meet multiple, sometimes competing, constraints.

Martinez, Roland↗
NASA NTRS2026

Benchmarking Bayesian Optimization Frameworks and Acquisition Strategies for Materials Discovery and Autonomous Laboratories

Bayesian optimization (BO) can accelerate materials discovery by guiding expensive experiments toward the most promising processing conditions. We systematically compare five BO surrogate and framework combinations (Gaussian processes in Ax, Gaussian processes and Monte-Carlo neural networks in BayBE, random forests in Lolopy, and tree-structured Parzen (TPE) estimators in Hyperopt) on three benchmarks that mimic common materials design tasks (a discrete solid-electrolyte composition space, a hybrid discrete/continuous laminate-composite design problem solved with micromechanics modeling, and the continuous Ishigami analytic function which is a standard optimization benchmark). Each BO surrogate is paired with posterior mean, probability of improvement, and expected improvement acquisition functions and run for 100 trials from randomized initial samples with uniform random search providing a control. Across five random seeds per setting, BayBE’s Gaussian-process surrogate with expected improvement consistently reached ≥95 % of the known optimum in the fewest evaluations, while Lolopy’s random forest matched or exceeded GP performance on purely categorical or mixed spaces at a higher computational cost. Posterior mean alone often stagnated at local optima, underscoring the need for exploration, whereas probability and expected improvement balanced exploration and exploitation leading to better optimization in fewer trials. Execution times ranged from milliseconds for TPE to minutes for neural-network and random-forest surrogates. These results establish baseline expectations for BO in automated materials laboratories and highlight expected improvement with Gaussian processes as a reliable first choice, with random forests offering a strong alternative when categorical variables dominate. The benchmark suite and code are released to facilitate future surrogate, acquisition, and constraint-handling research in data-driven materials optimization.

Bayesian optimization↗
DOE OSTI2025

Search for jet quenching with dijets from high-multiplicity pPb collisions at $\sqrt{s_{NN}}$ = 8.16 TeV

The first measurement of the dijet transverse momentum balance xj in proton-lead (pPb) collisions at a nucleon-nucleon center-of-mass energy of $\sqrt{s_{NN}}$ = 8.16 TeV is presented. The xj observable, defined as the ratio of the subleading over leading jet transverse momentum in a dijet pair, is used to search for jet quenching effects. The data, corresponding to an integrated luminosity of 174.6 nb −1 , were collected with the CMS detector in 2016. The x j distributions and their average values are studied as functions of the charged-particle multiplicity of the events and for various dijet rapidity selections. The latter enables probing hard scattering of partons carrying distinct nucleon momentum fractions x in the proton- and lead-going directions. The former, aided by the high-multiplicity triggers, allows probing for potential jet quenching effects in high-multiplicity events (with up to 400 charged particles), for which collective phenomena consistent with quark-gluon plasma (QGP) droplet formation were previously observed. The ratios of x j distributions for high- to low-multiplicity events are used to quantify the possible medium effects. These ratios are consistent with simulations of the hard-scattering process that do not include QGP production. These measurements set an upper limit on medium-induced energy loss of the subleading jet of 1.26% of its transverse momentum at the 90% confidence level in high multiplicity pPb events.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Controlling Molecular Structure and Spin with Multiconfigurational Quantum Chemistry (Final Technical Report)

For many first-row transition metal complexes, structure-property relationships can be obtained from high-level molecular geometry optimizations and subsequent electronic structure studies. The project funded under this award utilized newly implemented fully internally contracted (FIC) nuclear gradients for extended multi-state (XMS) complete active space second-order multireference perturbation theory (CASPT2) to explore geometry changes in first-row transition metal coordination complexes. At the start of the project, only one full geometry optimization using FIC-CASPT2 analytical gradients had been reported (J. Chem. Theory Comput., 2016, 12 (8), 3781), and many open-questions regarding the performance and achievable accuracy in applying such computations to larger complexes persisted. Key deliverables in the report include the implementation of the numerical Hessian and subsequent vibrational analysis in the BAGEL program package. This includes both full Hessian vibrational analysis (FHVA) and a partial Hessian vibrational analysis (PHVA). The latter of which allows us to reduce the significant cost of computing the vibrational frequencies in a large molecule by focusing on the modes of highest interest. The gradient code also proved useful in the development of an approach to improve the Hubbard-U correction by removing bias in predicting spin-splitting in Fe(II) complexes via plane-wave density functional theory (DFT). Finally, we showed for three families of complexes (spin-crossover (SCO) complexes, metallocorroles complexes, and those with metal-metal bonds) that CASPT2 can result in good molecular geometries and established best practices in undertaking this work. We are now using this approach in collaborative projects with experimental groups, which would not have been possible at the start of this project. A common theme also arose through this work that static and dynamic correlation must be recovered in a balanced way to yield quantitative results. By systematically varying how both effects were included, we were able to make key chemical insights. This work supported the training of two postdoctoral scholars, one graduate student, and one undergraduate student in applying high-level wavefunction based methods to challenging systems.

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

Frank–Kasper Phases of Diblock Copolymer Melts: Self-Consistent Field Results of Two Commonly Used Models

We constructed phase diagrams of conformationally asymmetric diblock copolymer A-B melts using the polymer self-consistent field (SCF) calculations of both the dissipative particle dynamics chain (DPDC) model (i.e., compressible melts of discrete Gaussian chains with the DPD non-bonded potential) and the “standard” model (i.e., incompressible melts of continuous Gaussian chains with the Dirac δ-function non-bonded potential) in the χN-ε plane, where χN and ε characterize, respectively, the repulsion and conformational asymmetry between the A and B blocks, at the A-block volume fraction f = 0.2 and 0.3. Consistent with previous SCF calculations of the “standard” model, σ and A15 are the only stable Frank–Kasper (FK) phases among the five FK (i.e., σ, A15, C14, C15 and Z) phases considered. The stability of σ and A15 is due to their delicate balance between the energetic and entropic contributions to the Helmholtz free energy per chain of the system, which, within our parameter range, increases in the order of σ/A15, Z, and C14/C15. While in general the SCF phase diagrams of these two models are qualitatively consistent, A15 is not stable for the DPDC model at the copolymer chain length N = 10 and f = 0.3; any differences in the SCF phase diagrams are solely due to the differences between these two models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1987

Electrostatic charge on a dust size distribution in a plasma

The capacitance of a grain immersed in a steady state plasma containing a size distribution of dust particles is studied. The grain charge is determined by assuming the equilibrium potential has been obtained by a simple balance of electron and ion collection currents. It is shown that the validity of the analytical treatment given here for the linearized Poisson equation is confined to a certain region of space. Within this region and starting at very small plasma Debye length lambda(D), the capacitance at first exhibits a monotonic increase with increasing lambda(D). The capacitance eventually reaches a maximum, followed by a monotonic decrease. The charge density of the dust in the plasma is found to be only a function of the lambda(D); there is no significant dependence on the interparticle spacing.

Houpis, Harry L. F.↗
NASA NTRS2005

Using In-Situ Deposition of Metallic Thin Films on Mars to Monitor Atmospheric H20

We describe the use of a novel sensor, designed to characterize the reactive nature of the Martian atmosphere, to also characterize the instantaneous abundance of atmospheric H,O. The sensor deposits, in situ, a thin silver film onto a sapphire substrate and monitors oxidation by measuring resistance of the Ag film during both deposition and subsequent oxidation [1,2]. . The evaporation source is placed in the center of a hollow, open-ended tube. On flash heating the source, evaporated metallic silver rapidly reacts with oxidizing gases in the tube, depositing on the tube walls as a resisitive Ag oxide film. Unoxidized silver deposits over the Ag oxides, once the oxidizing gases in the vicinity of the source have been consumed. Since the ends of the tube are open, atmospheric gases in the tube cause a time delay as the evaporated silver initially reacts with the available O2, and H2O; once oxidizing gases in the tube are significantly depleted, a silver film closes the chemiresistor circuit. In principle this operation mode provides a repeatable measure of the variable H2O abundance, since O2, levels are constant in the atmosphere. To explore the utility of the instrument as an H2O sensor, predict its behavior, and identify possible failure modes, we developed a numerical model of the instrument, and exercised it for Mars conditions. The model predicts the behavior of the electrical circuit, the temperature dependence of resistivity for each component, and the resultant Joule heating. The model balances Joule heating against radiation, sensible heat loss to ambient CO2, and, latent heat loss from sublimating Ag. The flux of Ag atoms from the source is tracked continuously. In the gas phase, the number density of Ag, O2, and H2O is calculated, along with their reaction rates on collision. The model suggests that Ag substantially depletes H2O only near the center of the tube. The model supports the hypothesis that the onset of high conductivity between the A u electrodes is a function of the H2O abundance in the ambient gas, but only to a limit; when H2O abundances are buffered at or above 240K (an H2O abundance that would not have occurred on Mars), metallic Ag never deposits in excess of Ago, and the circuit does not close. A significant difference in the Mars simulation is that the Ag source sublimates more quickly, primarily because of differences in sensible heat fluxes from the source. This allows subsequent heating of the W filament used to support the Ag source. Had the originally-designed experimental sequence been carried out on Mars, the filament might have melted, making subsequent investigations impossible.

Zent, A. P.↗
DOE OSTI2024

Triboplasma assisted chemical conversion in granular systems: a semi-analytic model

We present a semi-analytic model for a novel plasma-assisted chemical conversion pathway using triboplasmas generated in granular flows. Triboelectric charge relaxation is a well known phenomena where the potential generated from contact charging of particles exceeds the breakdown voltage of the background gas. In this work, we extend the triboelectric charge relaxation theory to include non equilibrium plasma energy and particle balance equations to predict the formation of dissociated and excited species that act as precursors to chemical conversion, for example in plasma-assisted ammonia synthesis. Our example case study with nitrogen background gas and teflon/aluminum tribomaterial system yielded high excited nitrogen species densities per collision that are comparable to current plasma-assisted conversion pathways. We also present a regime diagram for various gases where Paschen breakdown parameters are used to determine whether triboplasmas can be formed for a given effective work-function difference between two materials. Our sensitivity studies indicate particle velocity, particle radius, solids fraction and space charge effects play a critical role in overall plasma densities and excited species production.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Microreactor Automated Control System - Digital Twin Models and Advanced Control Systems Updates

Automation of control systems is expected to be important in the economic and safe operation of microreactors. Therefore, there is a need to develop and demonstrate automated control for microreactors, along with the development of testbeds for this purpose. This report provides updates on the status of a nonnuclear microreactor automated control system (MACS)—a real-time, hardware-in-the-loop testbed for non-nuclear testing of microreactor control system automation. A real-time hardware-in-the-loop testbed incorporates the realistic dynamics of physical systems into control system development and testing. The collaborative effort between Oak Ridge National Laboratory (ORNL) and Idaho National Laboratory (INL) resulted in the development of a prototypic microreactor plant-level digital twin that includes the reactor and a balance of plant system. Advanced control strategies were incorporated to demonstrate testing of control automation solutions. The gRPC communication protocol, which was implemented in the hardware-in-the-loop testbed by INL, was coupled to a digital twin model developed using the TRANsient Simulation Framework of Reconfigurable Models (TRANSFORM) library in Modelica. This digital twin simulation was tested with the ViBRANT hardware for realistic feedback and visual representation of control action in real time. A modular Python client structure was developed to manage functional mock-up unit-based simulation and real-time gRPC communication. Hardware-in-the-loop testing indicated that the modeled reactor—a natural-convection, molten-salt coolant loop configuration—responds well to control of drum positioning for modulation of reactor core power, as well as system-level control and downstream demand changes. Ongoing research is focused on integrating additional control algorithms that utilize data from newly included sensors within the MACS hardware testbed, as well as demonstrating and assessing the performance of the different automated control algorithms on multiple additional operational scenarios.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS1977

A numerical investigation of the severe thunderstorm gust front

The structure of the thunderstorm gust front is investigated by a nonhydrostatic, two-dimensional (x,z) numerical model. In the model, which is dry, the production of negatively buoyant air by evaporation is parameterized via an externally imposed, local-cooling function. This parameterization sustains a steady cold downdraft, which drives the surface outflow and associated gust front. It is shown that two dominant factors influencing gust front structure in the vertical plane are the solenoidal field coincident with the front and surface friction, modeled by means of a simple bulk aerodynamic drag formulation. The circulation theorem is invoked to illustrate how solenoidal accelerations oppose the deceleration by surface friction. After the onset of a downdraft in the model, these opposing tendencies soon reach a balance. Thus, following a brief transient stage, the model gust front exhibits a persistent configuration as it propagates rapidly forward. The essential features of this configuration are examined and compared with both tower observations of gust fronts and laboratory models of gravity currents.

Mitchell, K. E.↗
NASA NTRS2012

Robustness Analysis and Optimally Robust Control Design via Sum-of-Squares

A control analysis and design framework is proposed for systems subject to parametric uncertainty. The underlying strategies are based on sum-of-squares (SOS) polynomial analysis and nonlinear optimization to design an optimally robust controller. The approach determines a maximum uncertainty range for which the closed-loop system satisfies a set of stability and performance requirements. These requirements, de ned as inequality constraints on several metrics, are restricted to polynomial functions of the uncertainty. To quantify robustness, SOS analysis is used to prove that the closed-loop system complies with the requirements for a given uncertainty range. The maximum uncertainty range, calculated by assessing a sequence of increasingly larger ranges, serves as a robustness metric for the closed-loop system. To optimize the control design, nonlinear optimization is used to enlarge the maximum uncertainty range by tuning the controller gains. Hence, the resulting controller is optimally robust to parametric uncertainty. This approach balances the robustness margins corresponding to each requirement in order to maximize the aggregate system robustness. The proposed framework is applied to a simple linear short-period aircraft model with uncertain aerodynamic coefficients.

Dorobantu, Andrei↗
DOE OSTI2024

Photovoltaic Analysis and Response Support (PARS) Platform for Solar Situational Awareness and Resiliency Services

The project's primary objective is to develop a digital-twin based Photovoltaic (PV) Analysis and Response Support (PARS) platform, which aims to provide real-time situational awareness and optimal response plans. This platform is designed to enhance the performance of hybrid PV systems, making them competitive with or even superior to conventional generation resources. The PARS platform enabled the project team to develop and evaluate an extensive suite of grid support functionalities for the hybrid PV systems to enhance grid performance, across key areas including visibility, dispatchability, security, resilience, and reliability. Given the global push toward achieving 100% clean energy by 2035, there is a significant increase in the integration of inverter-based resources (IBRs) throughout the energy grid. Effectively managing the inherent variability and uncertainty associated with IBRs is crucial for ensuring cost-effectiveness, reliability, and security in both the main grid and islanded microgrids. Constrained to a limited array of IEEE test systems or standard feeder models, traditional IBR modeling struggles to assimilate new field data, accurately reflect system dynamics, and adapt to the evolving energy landscape. In our project, we embraced a Digital Twin (DT) strategy for crafting the PARS platform. A digital twin acts as a precise virtual counterpart of a physical system, built on historical data and continuously honed with real-time insights. This enables the high-fidelity DT to accurately mirror current system operations and forecast future scenarios. Consequently, the PARS platform becomes an ideal environment for testing and refining monitoring, control, power, and energy management algorithms designed to boost hybrid PV system performance. The defining feature of the PARS platform, distinguishing it from other advanced simulation tools, is its exceptional adaptability. This is achieved by employing actual network topologies and utilizing real-time field data for fine-tuning and calibration, ensuring a close emulation of real-world conditions. The project deliverables include: 1) High-fidelity IBR models and tools for real-time parameterization, utilizing real-time field measurements to refine IBR models for enhanced accuracy and performance; 2) Grid-forming and Grid-following capabilities to deliver resilience services, including blackstart, voltage and frequency support, cold-load pick-up, power reserves, and three-phase load balancing across grid-connected and microgrid settings; 3) Machine learning-based forecasting tools and methods for generating synthetic data and topologies, creating diverse and realistic simulation environments for evaluating varied operational scenarios; 4) Advanced microgrid power and energy management algorithms for optimizing the integration and operation of PV, storage, and demand response resources within both feeder and community scales. The power grid data sets are provided by four utility companies in North Carolina and the New York Power Administration. Acting as industry advisors, our industry partners communicated stakeholder needs and regulatory standards to the research teams, aiding technology transfer by incorporating the developed methodologies into their daily operations. This collaboration ensures that the PARS platform, functioning as a power system digital twin, enhances our understanding of IBR dynamic behaviors and enables the development and evaluation of IBR control functions that match or exceed the capabilities of conventional synchronous generators.

14 SOLAR ENERGY↗
DOE OSTI2025

SwinCell: a 3D transformer and flow-based framework for improved cell segmentation

Segmentation of three-dimensional (3D) cellular images is fundamental for studying and understanding cell structure and function. However, 3D cellular segmentation is challenging, particularly for dense cells and tissues. This challenge arises mainly from the complex contextual information within 3D images, anisotropic properties, and the sensitivity to internal cellular structures, which often lead to incorrect segmentation. In this work, we introduce SwinCell, a 3D transformer-based framework that leverages Swin-transformer to predict flow and differentiate individual cell instances. We demonstrate SwinCell’s utility in the segmentation of nuclei, colon tissue cells, and densely cultured cells. SwinCell strikes a balance between maintaining detailed local feature recognition and understanding broader contextual information. Through extensive testing with both public and in-house 3D cell imaging datasets, SwinCell shows utility in segmenting dense cells, making it a valuable tool for 3D segmentation in cellular analysis that could expedite research in cell biology and tissue engineering.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Genomics of sorghum local adaptation to a parasitic plant

Host–parasite coevolution can maintain high levels of genetic diversity in traits involved in species interactions. In many systems, host traits exploited by parasites are constrained by use in other functions, leading to complex selective pressures across space and time. Here, we study genome-wide variation in the staple crop Sorghum bicolor (L.) Moench and its association with the parasitic weed Striga hermonthica (Delile) Benth., a major constraint to food security in Africa. We hypothesize that geographic selection mosaics across gradients of parasite occurrence maintain genetic diversity in sorghum landrace resistance. Suggesting a role in local adaptation to parasite pressure, multiple independent loss-of-function alleles at sorghum LOW GERMINATION STIMULANT 1 (LGS1) are broadly distributed among African landraces and geographically associated with S. hermonthica occurrence. However, low frequency of these alleles within S. hermonthica-prone regions and their absence elsewhere implicate potential trade-offs restricting their fixation. LGS1 is thought to cause resistance by changing stereochemistry of strigolactones, hormones that control plant architecture and below-ground signaling to mycorrhizae and are required to stimulate parasite germination. Consistent with trade-offs, we find signatures of balancing selection surrounding LGS1 and other candidates from analysis of genome-wide associations with parasite distribution. Experiments with CRISPR–Cas9-edited sorghum further indicate that the benefit of LGS1-mediated resistance strongly depends on parasite genotype and abiotic environment and comes at the cost of reduced photosystem gene expression. Our study demonstrates long-term maintenance of diversity in host resistance genes across smallholder agroecosystems, providing a valuable comparison to both industrial farming systems and natural communities.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Transcriptome and secretome profiling of sensory neurons reveals sex differences in pathways relevant to insulin sensing and insulin secretion

Sensory neurons in the dorsal root ganglia (DRG) convey somatosensory and metabolic cues to the central nervous system and release substances from stimulated terminal endings in peripheral organs. Sex-biased variations driven by the sex chromosome complement (XX and XY) have been implicated in the sensory–islet crosstalk. However, the molecular underpinnings of these male–female differences are not known. Here, we aim to characterize the molecular repertoire and the secretome profile of the lower thoracic spinal sensory neurons and to identify molecules with sex-biased insulin sensing- and/or insulin secretion-modulating activity that are encoded independently of circulating gonadal sex hormones. We used transcriptomics and proteomics to uncover differentially expressed genes and secreted molecules in lower thoracic T5-12 DRG sensory neurons derived from sexually immature 3-week-old male and female C57BL/6J mice. Comparative transcriptome and proteome analyses revealed differential gene expression and protein secretion in DRG neurons in males and females. Furthermore, the transcriptome analysis identified, among others, higher insulin signaling/sensing capabilities in female DRG neurons; secretome screening uncovered several sex-specific candidate molecules with potential regulatory functions in pancreatic β cells. Together, these data suggest a putative role of sensory interoception of insulin in the DRG–islet crosstalk with implications in sensory feedback loops in the regulation of β-cell activity in a sex-biased manner. Finally, we provide a valuable resource of molecular and secretory targets that can be leveraged for understanding insulin interoception and insulin secretion and inform the development of novel studies/approaches to fathom the role of the sensory–islet axis in the regulation of energy balance in males and females.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Microscopic Imprints of Learned Solutions in Tunable Networks

In physical networks trained using supervised learning, physical parameters are adjusted to produce desired responses to inputs. An example is an electrical contrastive local learning network of nodes connected by edges that adjust their conductances during training. When an edge conductance changes, it upsets the current balance of every node. In response, physics adjusts the node voltages to minimize the dissipated power. Learning in these systems is therefore a coupled double-optimization process, in which the network descends both a cost landscape in the high-dimensional space of edge conductances and a physical landscape—the power dissipation—in the high-dimensional space of node voltages. Because of this coupling, the physical landscape of a trained network contains information about the learned task. Here, we derive a structure-function relation for trained tunable networks and demonstrate that all the physical information relevant to the trained input-output relation can be captured by a tuning susceptibility, an experimentally measurable quantity. We supplement our theoretical results with simulations to show that the tuning susceptibility is correlated with functional importance and that we can extract physical insight into how the system performs the task from the conductances of highly susceptible edges. Our analysis is general and can be applied directly to mechanical networks, such as networks trained for protein-inspired function such as allostery.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Molecule-surface interaction from van der Waals-corrected semilocal density functionals: The example of thiophene on transition-metal surfaces

Semilocal density functional approximations are widely used. None of them can capture the long-range van der Waals (vdW) attraction between separated subsystems, but they differ remarkably in the extent to which they capture intermediate-range vdW effects responsible for equilibrium bonds between neighboring small closed-shell subsystems. The local density approximation (LDA) often overestimates this effect, while the Perdew-Burke-Ernzerhof (PBE) generalized gradient approximation (GGA) underestimates it. The strongly constrained and appropriately normed (SCAN) meta-GGA often estimates it well. All of these semilocal functionals require an additive nonlocal correction such as the revised Vydrov-Van Voorhis 2010 (rVV10) to capture the long-range part. This work reports adsorption energies and the corresponding geometry of aromatic thiophene (C 4 H 4 S) bound to transition-metal surfaces. The adsorption process requires a genuine interplay of covalent and weak binding and requires a simultaneously accurate description of surface and adsorption energies with the correct prediction of the adsorption site. All these quantities must come from well-balanced short- and long-range correlation effects for a universally applicable method for weak interactions with chemical accuracy. Here, our methods indicate that the correct interplay is not present in any combination of recent meta-GGA's and rVV10. The simple short-range damping of the vdW correction scheme that is practically successful in the combination of GGA's and vdW approximations is less transferable in SCAN+rVV10 or in the revised version, revSCAN+rVV10. In addition, we present accurate random-phase-approximation-quality adsorption energies from a model based on the one of Zaremba and Kohn.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗