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DOE OSTI2020

Active Learning A Neural Network Model For Gold Clusters & Bulk From Sparse First Principles Training Data

Small metal clusters are of fundamental scientific interest and of tremendous significance in catalysis. These nanoscale clusters display diverse geometries and structural motifs depending on the cluster size; a knowledge of this size-dependent structural motifs and their dynamical evolution has been of longstanding interest. Given the high computational cost of first-principles calculations, molecular modeling and atomistic simulations such as molecular dynamics (MD) has proven to be an important complementary tool to aid this understanding. Classical MD typically employ predefined functional forms which limits their ability to capture such complex size-dependent structural and dynamical transformation. Neural Network (NN) based potentials represent flexible alternatives and in principle, well-trained NN potentials can provide high level of flexibility, transferability and accuracy on-par with the reference model used for training. A major challenge, however, is that NN models are interpolative and requires large quantities (similar to 10 4 or greater) of training data to ensure that the model adequately samples the energy landscape both near and far-from-equilibrium. A highly desirable goal is minimize the number of training data, especially if the underlying reference model is first-principles based and hence expensive. In this work, we introduce an active learning (AL) scheme that trains a NN model on-the-fly with minimal amount of first-principles based training data. Our AL workflow is initiated with a sparse training dataset (similar to 1 to 5 data points) and is updated on-the-fly via a Nested Ensemble Monte Carlo scheme that iteratively queries the energy landscape in regions of failure and updates the training pool to improve the network performance. Using a representative system of gold clusters, we demonstrate that our AL workflow can train a NN with similar to 500 total reference calculations. Using an extensive DFT test set of similar to 1100 configurations, we show that our AL-NN is able to accurately predict both the DFT energies and the forces for clusters of a myriad of different sizes. Our NN predictions are within 30 meV/atom and 40 meV/angstrom of the reference DFT calculations. Moreover, our AL-NN model also adequately captures the various size-dependent structural and dynamical properties of gold clusters in excellent agreement with DFT calculations and available experiments. We finally show that our AL-NN model also captures bulk properties reasonably well, even though they were not included in the training data.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Regulating the Third Metal to Design and Engineer Multilayered NiFeM (M: Co, Mn, and Cu) Nanofoam Anode Catalysts for Anion-Exchange Membrane Water Electrolyzers

In this study, alkaline anion-exchange membrane water electrolyzers (AEMWEs) for green hydrogen production have received intensive attention due to their feasibility of using earth-abundant platinum group metal (PGM)-free catalysts. Herein, the third metal is incorporated into NiFe-based catalysts to regulate their electronic structures and morphologies, aiming to achieve sufficient oxygen evolution reaction (OER) activity and performance in AEMWEs. The ternary NiFeM (M: Cu, Co, or Mn) catalysts are featured with multiple layered structures and nanofoam network morphologies, consisting of highly OER-active amorphous Ni-rich oxide shells and electrically conductive metallic alloy cores. The physical and electronic perturbations to the NiFe induced by a third element lead to a fine-tuning of the redox ability of the metal sites at the reaction centers, which breaks the scaling relationship between OH* and O* intermediates at the reaction centers. Thus, the unique structural configuration and electronic regulation simultaneously benefit catalytic activity and performance improvements. These NiFeM nanofoam catalysts demonstrated promising anode performance in actual AEMWEs, comparable to the IrO 2 reference, especially at high current densities. Notably, using various electrolytes (e.g., KOH solution or pure water) for AEMWEs exhibited a different performance trend among studied NiFeM catalysts, likely due to dynamic changes of catalysts under various OER environments.

25 ENERGY STORAGE↗

DOE OSTI2025

An attention-based neural ordinary differential equation framework for modeling inelastic processes

To preserve strictly conservative behavior as well as model the variety of dissipative behavior displayed by solid materials, we propose a significant enhancement to the internal state variable-neural ordinary differential equation (ISV-NODE) framework. In this data-driven, physics-constrained modeling framework internal states are inferred rather than prescribed. The ISV-NODE consists of: (a) a stress model dependent on observable deformation and inferred internal state, and (b) a model of the evolution of the internal states. The enhancements to ISV-NODE proposed in this work are multifold: (a) a partially input convex neural network stress potential provides polyconvexity in terms of observed strain while leaving the inferred state unconstrained, and (b) an internal state flow model uses common latent features to inform novel attention-based gating and drives the flow of internal state only in dissipative regimes. We demonstrated that this architecture can accurately model dissipative and conservative behavior across an isotropic, isothermal elastic-viscoelastic-elastoplastic spectrum with three exemplars, while maintaining fundamental principles by design.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1992

Tectonic Evolution of Mars

The Final Technical Report on tectonic evolution of Mars is presented. Two papers and an abstract are included. Topics addressed include: scientific rationale and requirements for a global seismic network on Mars, permanent uplift in magmatic systems with application to the Tharsis Region of Mars, and the geophysical signal of the Martian global dichotomy.

Phillips, Roger J.↗

NASA NTRS2000

Evolutionary Cell Computing: From Protocells to Self-Organized Computing

On the path from inanimate to animate matter, a key step was the self-organization of molecules into protocells - the earliest ancestors of contemporary cells. Studies of the properties of protocells and the mechanisms by which they maintained themselves and reproduced are an important part of astrobiology. These studies also have the potential to greatly impact research in nanotechnology and computer science. Previous studies of protocells have focussed on self-replication. In these systems, Darwinian evolution occurs through a series of small alterations to functional molecules whose identities are stored. Protocells, however, may have been incapable of such storage. We hypothesize that under such conditions, the replication of functions and their interrelationships, rather than the precise identities of the functional molecules, is sufficient for survival and evolution. This process is called non-genomic evolution. Recent breakthroughs in experimental protein chemistry have opened the gates for experimental tests of non-genomic evolution. On the basis of these achievements, we have developed a stochastic model for examining the evolutionary potential of non-genomic systems. In this model, the formation and destruction (hydrolysis) of bonds joining amino acids in proteins occur through catalyzed, albeit possibly inefficient, pathways. Each protein can act as a substrate for polymerization or hydrolysis, or as a catalyst of these chemical reactions. When a protein is hydrolyzed to form two new proteins, or two proteins are joined into a single protein, the catalytic abilities of the product proteins are related to the catalytic abilities of the reactants. We will demonstrate that the catalytic capabilities of such a system can increase. Its evolutionary potential is dependent upon the competition between the formation of bond-forming and bond-cutting catalysts. The degree to which hydrolysis preferentially affects bonds in less efficient, and therefore less well-ordered, peptides is also critical to evolution of a non-genomic system. Based on these results, a new computational object called a "molnet" is defined. Like a neural network, it is formed of interconnected units that send "signals" to each other. Like molecules, neural networks have a specific function once their structure is defined. The difference between a molnet and traditional neural networks, is that input to molnets is not simply passed along and processed from input to output units, but rather it is utilized to form and break connections(bonds), and thus to form new structures. Molnets represent a powerful tool that can be used to understand the conditions under which chemical systems can form large molecules, such as proteins, and display ever more complex functions. This has direct applications, for example to the design of smart,synthetic fabrics. Additional information is contained in the original.

Colombano, Silvano↗

NASA NTRS2002

Diurnal Cycle of Convection in the East Pacific ITCZ during EPIC-2001

During the last three weeks of September 2001, the EPIC-2001 intensive field campaign focused on studies of deep convection in the ITCZ over the Mexican warm pool region (10N, 95W) of the East Pacific. This study focuses on the pronounced observed diurnal cycle of environmental and convective parameters within the experiment domain. Data from three primary sources are examined: the R/V Ronald H. Brown C-band weather radar, 4-hourly soundings from the Brown and the Global Atmospherics, Inc. National Lightning Detection Network (long range product). Satellite data from TRMM, GOES and OV-1 are also used. The domain boundary layer shows a robust daily evolution of moist enthalpy (as reflect by equivalent potential temperature, theta-e, or wet bulb potential temperature, theta-w), with contributions from changes in both dry and moist entropy. Peak theta-w is found after local nightfall; the average diurnal range of theta-w is approximately 1 deg C. A composite diurnal cycle of convective properties was derived from the C-band volume scans, sampled continuously through the experiment at 10 minute updates. Products derived from the volumetric data include a surface PPI, 15 and 30 dBZ echo top height, vertically integrated liquid, and 6 km (mixed phase region) reflectivity CAPPIs. For almost all products, the parameter means showed virtually no diurnal cycle. However, for the upper-level products, the parameter spectra showed a clear peak in the occurrence of deep/vigorous convection (the "tail end of the distribution") between 7-9 UTC (1-3 AM local), while overall frequency of occurrence peaked later, from 12-15 UTC (6-9 AM local). This represents a daily "outbreak" of isolated deep cells a couple of hours after sunset and subsequent growth, organization and decay through the nighttime hours. The coherence of the diurnal cycle of the convective spectrum is impressive given the wide variety of convective organization observed during the experiment, and given the modulation by passage of 3-5 day easterly waves. While earlier satellite OLR composites suggested an offshore coastal migration of storms into the domain at night, examination of the 150 km and 300 km range radar products showed little evidence of such organization; almost all convection developed "in-place" within the analysis domain. Consistent with the diurnal thermodynamic and microphysical evolution, a clear cycle in cloud-to-ground (CG) lightning occurrence was observed. The local CG diurnal cycle is significantly stronger than the satellite-derived tropical ocean diurnal cycle of total (IC+CG) lightning. Flash rates of 3-4 fl/min were often visually observed after nightfall; these are fairly 'healthy' flash rates for tropical ocean storms, and the domain was qualitatively noted to be unusually lightning-productive by the R/V Brown crew (also consistent with satellite-based climatologies).

Boccippio, Dennis J.↗

NASA NTRS2012

The Kinematic and Microphysical Control of Storm Integrated Lightning Flash Extent

The objective of this preliminary study is to investigate the kinematic and microphysical control of lightning properties, particularly those that may govern the production of nitrogen oxides (NOx) in thunderstorms, such as flash rate, type and extent. The mixed-phase region is where the noninductive charging (NIC) process is thought to generate most storm electrification during rebounding collisions between ice particles in the presence of supercooled water. As a result, prior radar-based studies have demonstrated that lightning flash rate is well correlated to kinematic and microphysical properties in the mixed-phase region of thunderstorms such as updraft volume, graupel mass, or ice mass flux. There is also some evidence that lightning type is associated with the convective state. Intracloud (IC) lightning tends to dominate during the updraft accumulation of precipitation ice mass while cloud-to-ground (CG) lightning is more numerous during the downdraft-driven descent of radar echo associated with graupel and hail. More study is required to generalize these relationships, especially regarding lightning type, in a wide variety of storm modes and meteorological conditions. Less is known about the co-evolving relationship between storm kinematics, microphysics, morphology and three-dimensional flash extent, despite its importance for lightning NOx production. To address this conceptual gap, the NASA MSFC Lightning Nitrogen Oxides Model (LNOM) is applied to North Alabama Lightning Mapping Array (NALMA) and Vaisala National Lightning Detection NetworkTM (NLDN) observations following ordinary convective cells through their lifecycle. LNOM provides estimates of flash type, channel length distributions, lightning segment altitude distributions (SADs) and lightning NOx production profiles. For this study, LNOM is applied in a Lagrangian sense to well isolated convective cells on 3 April 2007 (single cell and multi-cell hailstorm, non-severe multicell) and 6 July 2007 (non-severe multi-cell) over Northern Alabama. The LNOM lightning characteristics are compared to the evolution of updraft and precipitation properties inferred from dual-Doppler and polarimetric radar analyses applied to observations from a nearby Doppler radar network, including the UA Huntsville Advanced Radar for Meteorological and Operational Research (ARMOR, C-band, polarimetric). The LNOM estimated SAD and lightning NOx production profiles are placed in the context of radar derived profiles of vertical motion, precipitation types and amounts. Finally, these analyses are used to determine if storm integrated flash channel extent is as well correlated to volumetric updraft and precipitation ice characteristics in the mixed phase region as flash rate for these individual convective cells.

Carey, Lawrence↗

DOE OSTI2026

Measurement-informed Dynamic Aggregation of Distribution Systems

This paper proposes a measurement-informed dynamic aggregation methodology in order to create equivalent representations of distribution systems that are compatible with large-scale transmission analysis. By optimizing an equivalent feeder parameters using time-series measurements of active power, reactive power, and voltage at the Point of Interconnection (POI), the approach yields simplified yet dynamically accurate equivalents. Implemented in PSCAD with models of photovoltaic–battery systems, three-phase motors, and static loads, the method employs hybrid differential evolution and bounded least-squares optimization laying the foundation for for real-time state estimation and optimized sensor placement in distribution networks.

Ahmed, Kazi Ishrak [University of Tennessee, Knoxv↗

NASA NTRS2009

Emergence of Complexity in Protein Functions and Metabolic Networks

In modern organisms proteins perform a majority of cellular functions, such as chemical catalysis, energy transduction and transport of material across cell walls. Although great strides have been made towards understanding protein evolution, a meaningful extrapolation from contemporary proteins to their earliest ancestors is virtually impossible. In an alternative approach, the origin of water-soluble proteins was probed through the synthesis of very large libraries of random amino acid sequences and subsequently subjecting them to in vitro evolution. In combination with computer modeling and simulations, these experiments allow us to address a number of fundamental questions about the origins of proteins. Can functionality emerge from random sequences of proteins? How did the initial repertoire of functional proteins diversify to facilitate new functions? Did this diversification proceed primarily through drawing novel functionalities from random sequences or through evolution of already existing proto-enzymes? Did protein evolution start from a pool of proteins defined by a frozen accident and other collections of proteins could start a different evolutionary pathway? Although we do not have definitive answers to these questions, important clues have been uncovered. Considerable progress has been also achieved in understanding the origins of membrane proteins. We will address this issue in the example of ion channels - proteins that mediate transport of ions across cell walls. Remarkably, despite overall complexity of these proteins in contemporary cells, their structural motifs are quite simple, with -helices being most common. By combining results of experimental and computer simulation studies on synthetic models and simple, natural channels, I will show that, even though architectures of membrane proteins are not nearly as diverse as those of water-soluble proteins, they are sufficiently flexible to adapt readily to the functional demands arising during evolution.

Pohorille, Andzej↗

DOE OSTI2020

Acidification in the U.S. Southeast: Causes, Potential Consequences and the Role of the Southeast Ocean and Coastal Acidification Network

Coastal acidification in southeastern U.S. estuaries and coastal waters is influenced by biological activity, run-off from the land, and increasing carbon dioxide in the atmosphere. Acidification can negatively impact coastal resources such as shellfish, finfish, and coral reefs, and the communities that rely on them. Organismal responses for species located in the U.S. Southeast document large negative impacts of acidification, especially in larval stages. For example, the toxicity of pesticides increases under acidified conditions and the combination of acidification and low oxygen has profoundly negative influences on genes regulating oxygen consumption. In corals, the rate of calcification decreases with acidification and processes such as wound recovery, reproduction, and recruitment are negatively impacted. Minimizing the changes in global ocean chemistry will ultimately depend on the reduction of carbon dioxide emissions, but adaptation to these changes and mitigation of the local stressors that exacerbate global acidification can be addressed locally. The evolution of our knowledge of acidification, from basic understanding of the problem to the emergence of applied research and monitoring, has been facilitated by the development of regional Coastal Acidification Networks (CANs) across the United States. This synthesis is a product of the Southeast Coastal and Ocean Acidification Network (SOCAN). SOCAN was established to better understand acidification in the coastal waters of the U.S. Southeast and to foster communication among scientists, resource managers, businesses, and governments in the region. Here we review acidification issues in the U.S. Southeast, including the regional mechanisms of acidification and their potential impacts on biological resources and coastal communities. We recommend research and monitoring priorities and discuss the role SOCAN has in advancing acidification research and mitigation of and adaptation to these changes.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Attention Network Forecasts Time-to-Failure in Laboratory Shear Experiments

Rocks under stress deform by creep mechanisms that include formation and slip on small-scale internal cracks. Intragranular cracks and slip along grain contacts release energy as elastic waves termed acoustic emissions (AE). AEs are thought to contain predictive information that can be used for fault failure forecasting. Here, we present a method using unsupervised classification and an attention network to forecast labquakes using AE waveform features. Our data were generated in a laboratory setting using a biaxial shearing device with granular fault gouge intended to mimic the conditions of tectonic faults. Here, we analyzed the temporal evolution of AEs generated throughout several hundred laboratory earthquake cycles. We used a Conscience Self-Organizing Map (CSOM) to perform topologically ordered vector quantization based on waveform properties. The resulting map was used to interactively cluster AEs. We examined the clusters over time to identify those with predictive ability. Finally, we used a variety of LSTM and attention-based networks to test the predictive power of the AE clusters. By tracking cumulative waveform features over the seismic cycle, the network is able to forecast the time-to-failure (TTF) of lab earthquakes. Our results show that analyzing the data to isolate predictive signals and using a more sophisticated network architecture are key to robustly forecasting labquakes. In the future, this method could be applied on tectonic faults to monitor earthquakes and augment early warning systems.

58 GEOSCIENCES↗

DOE OSTI2023

Analysis of Neural Networks as Random Dynamical Systems

In this report we present our findings and outcomes of the NNRDS (analysis of Neural Networks as Random Dynamical Systems) project. The work is largely motivated by the analogy of a large class of neural networks (NNs) with a discretized ordinary differential equation (ODE) schemes. Namely, residual NNs, or ResNets, can be viewed as a discretization of neural ODEs (NODEs) where the NN depth plays the role of the time evolution. We employ several legacy tools from ODE theory, such as stiffness, nonlocality, autonomicity, to enable regularization of ResNets thus improving their generalization capabilities. Furthermore, armed with NN analysis tools borrowed from the ODE theory, we are able to efficiently augment NN predictions with uncertainty overcoming wellknown dimensionality challenges and adding a degree of trust towards NN predictions. Finally, we have developed a Python library QUiNN (Quantification of Uncertainties in Neural Networks) that incorporates improved-architecture ResNets, besides classical feed-forward NNs, and contains wrappers to PyTorch NN models enabling several major classes of uncertainty quantification methods for NNs. Besides synthetic problems, we demonstrate the methods on datasets from climate modeling and materials science.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Influence of the proton relay spacer on hydrogen electrocatalysis by cobalt hangman porphyrins

A cobalt hangman porphyrin system with a phenyl spacer between the porphyrin ring and an internal carboxylic acid group as well as its non-hangman analogue were synthesized and utilized for the study of the proton-coupled electron transfer (PCET) kinetics attendant to electrocatalytic hydrogen evolution. Cyclic voltammetry (CV) together with simulations show that a short distance between the proton relay and the redox active cobalt center as well as the increased proton donating strength results in superior catalytic activity. Here, the mechanism of hydrogen generation is at the nexus of proton transfer–electron transfer (PTET) and concerted proton–electron transfer (CPET), as opposed to an ETPT mechanism that is characteristic of hangman systems with longer proton relay networks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1999

Early animal evolution: emerging views from comparative biology and geology

The Cambrian appearance of fossils representing diverse phyla has long inspired hypotheses about possible genetic or environmental catalysts of early animal evolution. Only recently, however, have data begun to emerge that can resolve the sequence of genetic and morphological innovations, environmental events, and ecological interactions that collectively shaped Cambrian evolution. Assembly of the modern genetic tool kit for development and the initial divergence of major animal clades occurred during the Proterozoic Eon. Crown group morphologies diversified in the Cambrian through changes in the genetic regulatory networks that organize animal ontogeny. Cambrian radiation may have been triggered by environmental perturbation near the Proterozoic-Cambrian boundary and subsequently amplified by ecological interactions within reorganized ecosystems.

Non-NASA Center↗

NASA NTRS2003

Perspectives on Advanced Learning Technologies and Learning Networks and Future Aerospace Workforce Environments

An overview of the advanced learning technologies is given in this presentation along with a brief description of their impact on future aerospace workforce development. The presentation is divided into five parts (see Figure 1). In the first part, a brief historical account of the evolution of learning technologies is given. The second part describes the current learning activities. The third part describes some of the future aerospace systems, as examples of high-tech engineering systems, and lists their enabling technologies. The fourth part focuses on future aerospace research, learning and design environments. The fifth part lists the objectives of the workshop and some of the sources of information on learning technologies and learning networks.

Noor, Ahmed K.↗

NASA NTRS2006

Using a Network of Boundary Layer Profilers to Characterize the Atmosphere at a Major Spaceport

Space launch, landing, and ground operations at the Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) in east-central Florida are highly sensitive to mesoscale weather conditions throughout the year. Due to the complex land-water interfaces and the important role of mesoscale circulations, a high-resolution network of five 915-MHz Doppler Radar Wind Profilers (DRWP) and 44 wind towers was installed over the KSC/CCAFS area. By using quality-controlled 915-MHz DRAT data along with the near-surface tower observations, the Applied Meteorology Unit and KSC Weather Office have studied the development and evolution of various mesoscale phenomena across KSC/CCAFS such as sea and land breezes, low-level jets, and frontal passages. This paper will present some examples of mesoscale phenomena that can impact space operations at KSC/CCAFS, focusing on the utility of the 915-MHz DRWP network in identifying important characteristics of sea/land breezes and low-level jets.

Case, Jonathan L.↗

DOE OSTI2022

Long-time integration of parametric evolution equations with physics-informed DeepONets

Ordinary and partial differential equations (ODEs/PDEs) play a paramount role in analyzing and simulating complex dynamic processes across all corners of science and engineering. In recent years machine learning tools are aspiring to introduce new effective ways of simulating such equations, however existing approaches are not able to reliably return stable and accurate predictions across long temporal horizons. We aim to address this challenge by introducing an effective framework for learning evolution operators that map random initial conditions to associated ODE/PDE solutions within a short time interval. Such operators can be parametrized by deep neural networks that are trained in an entirely self-supervised manner without requiring one to generate any paired input-output observations. Global long-time predictions across a range of initial conditions can be then obtained by iteratively evaluating the trained model using each prediction as the initial condition for the next evaluation step. Here, this introduces a new approach to temporal domain decomposition that is shown to be effective in performing accurate long-time simulations for a wide range of parametric ODE and PDE systems, from wave propagation, to reaction-diffusion dynamics and stiff chemical kinetics, introducing a new way of rapidly emulating non-equilibrium processes in science and engineering.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Longitudinal proteomics analysis in the immediate microenvironment of islet allografts during progression of rejection

The applicability and benefits of pancreatic islet transplantation are limited due to various issues including the need to avoid immune-mediated rejection. In the present study, we used our experimental platform of allogeneic islet transplant in the anterior chamber of the eye (ACE-platform) to longitudinally monitor the progress of rejection in mice and obtain aqueous humor samples representative of the microenvironment of the graft for accurately-timed proteomic analyses. LC-MS/MS-based proteomics performed on such mass-limited samples (~5 μL) identified a total of 1296 proteins. Various analyses revealed distinct protein patterns associated with the mounting of the inflammatory and immune responses and their evolution with the progression of the rejection. Pathway analyses indicated predominant changes in cytotoxic functions, cell movement, and innate and adaptive immune responses. Furthermore, network prediction analyses revealed transition from humoral to cellular immune response and exacerbation of pro-inflammatory signaling. One of the proteins identified by this localized proteomics as a candidate biomarker of islet rejection, Cystatin 3, was further validated by ELISA in the aqueous humor. This study provides (1) experimental evidence demonstrating the feasibility of longitudinal localized proteomics using small aqueous humor samples and (2) proof-of-concept for the discovery of biomarkers of impending immune attack from the immediate local microenvironment of ACE-transplanted islets.

59 BASIC BIOLOGICAL SCIENCES↗