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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Equilibrium-gated pattern formation: How molecular dissociation thermodynamics drive emergent behavior in dissipative polymeric systems

Emergent patterns in biological systems arise through dissipative processes that balance reaction and transport phenomena, producing highly functional properties from self-regulating mechanisms. Synthetic fabrication, by contrast, often relies on user-controlled, multistep methods that lack the self-organizing capabilities of natural systems. Inspired by nature, we sought chemical systems that integrate strongly coupled reaction and transport phenomena, identifying frontal ring-opening metathesis polymerization (FROMP) as a method capable of creating diverse forms and functions through reactive processing. By employing discrete molecular initiators, FROMP allows precise control of key reaction steps—inhibition, initiation, and propagation. Using an integrated computational and experimental framework, we uncover how near-equilibrium inhibition dynamics, coupled with far-from-equilibrium reaction kinetics, drive pattern formation in frontally polymerized synthetic materials. We propose the concept of equilibrium-gated pattern formation, demonstrating how initiator chemistry can be tuned to achieve programmable macroscale properties. Our study reveals a surprising insight: Emergent behavior in FROMP systems arises from the inhibition-dominated regime of resin composition, expanding prior observations that such behavior is confined to a narrow compositional space near the boundary between front quenching and uniform front propagation. We identify a broader compositional window, far from the quenching regime, where emergent behavior reliably manifests. This expanded design space significantly enhances the operational flexibility of reactive systems and their capacity for self-organization. Furthermore, these insights provide a roadmap for designing bioinspired materials with self-organizing capabilities, unlocking possibilities in synthetic manufacturing.

Architected Polymers↗
NASA NTRS2004

Properties of a Formal Method for Prediction of Emergent Behaviors in Swarm-based Systems

Autonomous intelligent swarms of satellites are being proposed for NASA missions that have complex behaviors and interactions. The emergent properties of swarms make these missions powerful, but at the same time more difficult to design and assure that proper behaviors will emerge. This paper gives the results of research into formal methods techniques for verification and validation of NASA swarm-based missions. Multiple formal methods were evaluated to determine their effectiveness in modeling and assuring the behavior of swarms of spacecraft. The NASA ANTS mission was used as an example of swarm intelligence for which to apply the formal methods. This paper will give the evaluation of these formal methods and give partial specifications of the ANTS mission using four selected methods. We then give an evaluation of the methods and the needed properties of a formal method for effective specification and prediction of emergent behavior in swarm-based systems.

Rouff, Christopher↗
NASA NTRS2004

Verification of Emergent Behaviors in Swarm-based Systems

The emergent properties of swarms make swarm-based missions powerful, but at the same time more difficult to design and to assure that the proper behaviors will emerge. We are currently investigating formal methods and techniques for verification and validation of swarm-based missions. The Autonomous Nano-Technology Swarm (ANTS) mission is being used as an example and case study for swarm-based missions to experiment and test current formal methods with intelligent swarms. Using the ANTS mission, we have evaluated multiple formal methods to determine their effectiveness in modeling and assuring swarm behavior. This paper introduces how intelligent swarm technology is being proposed for NASA missions, and gives the results of a comparison of several formal methods and approaches for specifying intelligent swarm-based systems and their effectiveness for predicting emergent behavior.

Rouff, Christopher↗
NASA NTRS2009

Evolutionary Computation for the Identification of Emergent Behavior in Autonomous Systems

Over the past several years the Center for Evolutionary Computation and Automated Design at the Jet Propulsion Laboratory has developed a technique based on Evolutionary Computational Methods (ECM) that allows for the automated optimization of complex computationally modeled systems. An important application of this technique is for the identification of emergent behaviors in autonomous systems. Mobility platforms such as rovers or airborne vehicles are now being designed with autonomous mission controllers that can find trajectories over a solution space that is larger than can reasonably be tested. It is critical to identify control behaviors that are not predicted and can have surprising results (both good and bad). These emergent behaviors need to be identified, characterized and either incorporated into or isolated from the acceptable range of control characteristics. We use cluster analysis of automatically retrieved solutions to identify isolated populations of solutions with divergent behaviors.

Terrile, Richard J.↗
DOE OSTI2024

Learning stochastic dynamics and predicting emergent behavior using transformers

We show that a neural network originally designed for language processing can learn the dynamical rules of a stochastic system by observation of a single dynamical trajectory of the system, and can accurately predict its emergent behavior under conditions not observed during training. We consider a lattice model of active matter undergoing continuous-time Monte Carlo dynamics, simulated at a density at which its steady state comprises small, dispersed clusters. We train a neural network called a transformer on a single trajectory of the model. The transformer, which we show has the capacity to represent dynamical rules that are numerous and nonlocal, learns that the dynamics of this model consists of a small number of processes. Forward-propagated trajectories of the trained transformer, at densities not encountered during training, exhibit motility-induced phase separation and so predict the existence of a nonequilibrium phase transition. Transformers have the flexibility to learn dynamical rules from observation without explicit enumeration of rates or coarse-graining of configuration space, and so the procedure used here can be applied to a wide range of physical systems, including those with large and complex dynamical generators.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Particle anisotropy tunes emergent behavior in active colloidal systems

Studies of active particle systems have demonstrated that particle anisotropy can impact the collective behavior of a system, motivating a systematic study. Here, we report a systematic computational investigation of the role of anisotropy in shape and active force director on the collective behavior of a two-dimensional active colloidal system. We find that shape and force anisotropy can combine to produce critical densities both lower and higher than those of disks. Furthermore, we demonstrate that changing particle anisotropy tunes what we define as a “collision efficiency” of inter-particle collisions in leading to motility-induced phase separation (MIPS) of the system. We use this efficiency to determine the relative critical density across systems. Additionally, we observe that local structure in phase-separated clusters is the same as the particle's equilibrium densest packing, suggesting a general connection between equilibrium behavior and non-equilibrium cluster structure of self-propelled anisotropic particles. In engineering applications for active colloidal systems, shape-controlled steric interactions such as those described here may offer a simple route for tailoring emergent behaviors.

42 ENGINEERING↗
DOE OSTI2023

Emergent Behaviors and the Limits of Large Language Model Generalization

Over the past couple years, large language models (LLMs) have rapidly grown in size and performance across wide numbers of tasks, leading some to herald them as machine learning models with truly “general” capabilities. We address some of the ways that the language used to describe these models can be both deceptive and a poor framing for making progress towards understanding their capabilities, and also review arguments on to what extent these models can represent and interact with the meaning of language. Emergent behaviors in these highly complex models are unpredictable and research into understanding them is still in early phases, which limits the strength of claims of generality about them. Moreover, while these models demonstrate surprising emergent reasoning capabilities on many tasks, there are still many limitations in generalization that overall high benchmark performance can hide. We review the literature showcasing limitations of LLMs as well as techniques to mitigate or overcome these challenges, while also highlighting how fundamental problems in model evaluation may prevent true claims of generality. We conclude with a practical section on using LLMs on real world problems while appropriately evaluating and validating these tasks to prevent or mitigate the downstream impacts of incorrect results.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2012

Anaphylaxis, Intra-Abdominal Infections, Skin Lacerations, and Behavioral Emergencies: A Literature Review of Austere Analogs for a near Earth Asteroid Mission

As space exploration is directed towards destinations beyond low-Earth orbit, the consequent new set of medical risks will drive requirements for new capabilities and more resources to ensure crew health. The Space Medicine Exploration Medical Conditions List (SMEMCL), developed by the Exploration Medical Capability element of the Human Research Program, addresses the risk of "unacceptable health and mission outcomes due to limitations of in-flight medical capabilities". It itemizes 85 evidence-based clinical requirements for eight different mission profiles and identifies conditions warranting further research and technology development. Each condition is given a clinical priority for each mission profile. Four conditions -- intra-abdominal infections, skin lacerations, anaphylaxis, and behavioral emergencies -- were selected as a starting point for analysis. A systematic literature review was performed to understand how these conditions are treated in austere, limited-resource, space-analog environments (i.e., high-altitude and mountain environments, submarines, military deployments, Antarctica, isolated wilderness environments, in-flight environments, and remote, resource-poor, rural environments). These environments serve as analogs to spaceflight because of their shared characteristics (limited medical resources, delay in communication, confined living quarters, difficulty with resupply, variable time to evacuation). Treatment of these four medical conditions in austere environments provides insight into medical equipment and training requirements for exploration-class missions.

Chough, Natacha G.↗
DOE OSTI2020

Emergent behavior of LaNiO 3 in short-periodic nickelate superlattices

Heterostructure engineering provides an efficient way to obtain several emergent phases of LaNiO 3 , as demonstrated in recent works. In this work, a new class of short periodic superlattices, consisting of LaNiO 3 and EuNiO 3 have been grown by pulsed laser interval deposition to investigate the effect of structural symmetry mismatch on the electronic and magnetic behaviors. Using synchrotron based soft and hard x-ray resonant scattering experiments, we have found that these heterostructures undergo simultaneous electronic and magnetic transitions. Most importantly, LaNiO 3 within these artificial structures exhibits a new antiferromagnetic, charge ordered insulating phase, which may be a potential candidate to achieve high temperature superconductivity.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Shape-driven, emergent behavior in active particle mixtures

Abstract Active particle systems can vary greatly from one-component systems of spheres to mixtures of particle shapes at different composition ratios. We investigate computationally the combined effect of anisotropy and stoichiometry on the collective behavior of two-dimensional active colloidal mixtures of polygons. We uncover three emergent phenomena not yet reported in active Brownian particle systems. First, we find that mixtures containing hexagons exhibit micro-phase separation with large grains of hexagonal symmetry. We quantify a measurable, implicit ‘steric attraction’ between the active particles as a result of shape anisotropy and activity. This calculation provides further evidence that implicit interactions in active systems, even without explicit attraction, can lead to an effective preferential attraction between particles. Next, we report stable fluid clusters in mixtures containing one triangle or square component. We attribute the fluidization of the dense cluster to the interplay of cluster destabilizing particles, which introduce grain boundaries and slip planes into the system, causing solid-like clusters to break up into fluid clusters. Third, we show that fluid clusters can coexist with solid clusters within a sparse gas of particles in a steady state of three coexisting phases. Our results highlight the potential for a wide variety of behavior to be accessible to active matter systems and establish a route to control active colloidal systems through simple parameter designs.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Data-driven particle dynamics: Structure-preserving coarse-graining for emergent behavior in non-equilibrium systems

Multiscale systems are ubiquitous in science and technology, but are notoriously challenging to simulate as short spatiotemporal scales must be appropriately linked to emergent bulk physics. When expensive high-dimensional dynamical systems are coarse-grained into low-dimensional models, the entropic loss of information leads to emergent physics which are dissipative, history-dependent, and stochastic. To machine learn coarse-grained dynamics from time-series observations of particle trajectories, we propose a framework using the metriplectic bracket formalism that preserves these properties by construction; most notably, the framework guarantees discrete notions of the first and second laws of thermodynamics, conservation of momentum, and a discrete fluctuation-dissipation balance crucial for capturing non-equilibrium statistics. We introduce the mathematical framework abstractly before specializing to a particle discretization. As labels are generally unavailable for entropic state variables, we introduce a novel self-supervised learning strategy to identify emergent structural variables. We validate the method on benchmark systems and demonstrate its utility on two challenging examples: (1) coarse-graining star polymers at challenging levels of coarse-graining while preserving non-equilibrium statistics, and (2) learning models from high-speed video of colloidal suspensions that capture coupling between local rearrangement events and emergent stochastic dynamics. We provide open-source implementations in both PyTorch and LAMMPS, enabling large-scale inference and extensibility to diverse particle-based systems.

Computational Engineering, Finance, and Science (c↗
DOE OSTI2022

Emergent Behavior at the Calcite–Water Interface during Reactive Transport in a Simple Microfluidic Channel

Geochemical reactive transport processes in natural mineral-fluid systems may produce a wide array of emergent phenomena that are difficult to predict from basic principles and to reproduce in model systems. In this study, we present experimental results obtained from a simple microfluidic system with which we explored the consequences of reacting the calcite (104) cleavage surface with an acidic Pb-bearing solution (pH = 3.5, [Pb] total = 5 mM) as a function of flow rate. This system is relevant to passive remediation systems for Pb-rich acid mine drainage. We observed periodic banding in the amounts of Pb sorption at flow velocities ≥ 926 μm s -1 , where the band spacing was spatially correlated with the amount of calcite dissolution and the development of micropyramidal topography on the calcite (104) surface. The equivalent coverage of Pb deposited in these Pb-rich bands was at least several monolayers per unit cell, yet there was no evidence for precipitation of any secondary Pb phase implying incorporation of Pb within the near-surface calcite lattice. We also observed spatial variations in nucleation and growth of euhedral secondary Pb-carbonate minerals hydrocerusite and cerussite at flow rates ≤ 278 μm s -1 ,. These findings demonstrate potential for exploiting the rich phenomenology afforded by the interplay among transport phenomena and chemical kinetics in experimental systems designed to yield deeper insights into geochemical self-organization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Dynamic, Adaptive, Systems and Materials: Complex, Simple and Emergent Behaviors

This program has been funded by DoE/BES for twenty years. It has moved into and out of various subjects as it has developed, but it has retained its focus on complexity and complex systems. The project has evolved in the following way: Self-Assembly and Biomimetic Self-Assembly: All self-assembling systems depend upon a minimum of two types of interaction: a repulsion and an attraction. For the familiar molecular systems, attractive interactions are typically hydrogen bonds and electrostatic interactions. Repulsive interactions include steric effects, hydrophobic effects (in biological systems), and charge-charge repulsion. We have expanded this repertoire to include surface interactions, magnetic interactions, and others. I list these systems in the order in which we have explored them: i) A key emphasis in current work is in understanding how the movement of ions in a magnetic field (the Lorentz effect) interacts with catalytic systems. We have demonstrated that an acceleration in rate of reduction of CO 2 to CO can be accomplished by applying an external magnetic field. This acceleration is largely due to the application of the Lorentz effect on mass transport at the catalyst’s surface. ii) We have also extensively explored the influence of electrostatics, as exhibited in self-assembling systems, by tribocharging. iii) Another key system involves surface tension effects; examples include interactions between heavy particles floating at a liquid-air interface, and interacting by changes in surface area; interactions of bubbles and bubble rafts, behaviors of bubble trains in microfluidic networks, and behaviors of microorganisms in constraining environments. iv) This work has intentionally de-emphasized biological systems; but it does include some work on protein-ligand interactions and interactions among microorganisms. v) We have also explored applications of some of these effects, these explorations include bubble rafts as diffraction gratings, exploration of the structures that can be obtained by tribocharging and uses of these structures in exploring nucleation and melting of crystals. vi) Although not a major focus of this work, several other topics have emerged and offer opportunities for future work. These include the behavior of bubble trains and bubble rafts in microfluidic systems. A particularly interesting example is the formation of bubble trains that repeat in the alteration of large and small bubbles according to rules we do not presently understand, but are uniquely large-period oscillating systems. These systems offer a new route into understanding the instabilities of the type represented by oscillations. vii) We have also begun exploratory projects on magnetic levitation (especially to determine molecular density), and information storage (in molecules). Magnetic Levitation: Self-assembling and biomimetic systems require both attraction and repulsion. We have used electrostatics (tribocharging), interfacial free-energies (surface tension and related forces) and others. Potential uses include reconfigurable diffraction gratings and liquid lenses; exploration of mechanisms in tribocharging; tunneling in EGaIn junctions; and bubble trains (especially in micro-fluidic systems). Examples of systems representing these topics is included in the following papers: Complexity: Disks rotating at a water-air interface; Benard-Marangoni effects; Vortex-Crystals from spinning magnetic disks (Marangoni effects); EGaIn Electrode to study quantum tunneling; Self-Assembly of electrostatically-charged metallic spheres (electrets); Dynamically reconfigurable lens; Using computational designs of ligands for enzymes; Electrostatic self-assembly by tribocharging; Monodisperse bubble trains in microchannel systems; Inverted dripping faucet; Flames; Printing of micro-organisms to regenerate the “ink” of printing device; Using micro-organisms to move loads (“microoxen”); Motion of bacterial swarms near surfaces; Making monodisperse particles in microfluidic systems; Coding/decoding of information stored in droplet trains in microfluidic networks; Magnetic levitation; and Information storage. i) Tribocharging. The change in focus of this work on electrets from the fundamentals of charging to applications of these materials in studying self-assembly using electrostatic interactions. ii) Bubbles in Microchannels. The realization that systems of bubbles in microchannels represented a major opportunity to study complexity in a very tractable system, and the development of a semi-quantitative theory of this subject. iii) Flames. The growth of “flames” remains an exploratory subject for the research, although their currently relatively little active work involving it ongoing. iv) Systems with Microorganisms. The removal of work in biological systems from this project. Based on work supported in this program, we now have a significant project on the development of microfluidic tools for studying C. elegans (a nematode), but this work was not appropriate for a program focused on complexity, and we developed separate support for it. (It is, however, an example of successful seeding of a new area by BES.) The work on electrets has gone through a period in which a part of the program was the subject of a MURI; the focus of this work was to develop materials that did not charge electrostatically on friction or contact. The MURI is now over, and the work on dynamic self-assembly (supported by BES) is the major focus. “Flames” has also enjoyed synergistic support, in terms of a project supported by DARPA on flame suppression (in the absence of extinguishing agents, using acoustic and electrostatic interactions). This work was helpful in understanding some of the basics of flames, but is entirely distinct from the BES focus in complexity. A growing interest is in the Lorentz effect. The Lorentz effect is the force exerted on charged particles (electrons, ions, charged molecules) when they move through a perpendicular magnetic field. The Lorentz effect is almost ubiquitous in modern technology: examples of applications include electric motors, dynamos, cathode ray tubes, many batteries, and most systems that control electrical currents with magnetic forces. We have begun to explore the Lorentz effect in electrochemical systems and heterogeneous catalytic systems involving charged organic species and inorganic ions. This work is still at an early stage, but initial studies that Lorentz effects can be large when ions move through magnetic fields, or magnetic fields move in the presence of ions.

36 MATERIALS SCIENCE↗
NASA NTRS2011

Evolutionary Computational Methods for Identifying Emergent Behavior in Autonomous Systems

A technique based on Evolutionary Computational Methods (ECMs) was developed that allows for the automated optimization of complex computationally modeled systems, such as autonomous systems. The primary technology, which enables the ECM to find optimal solutions in complex search spaces, derives from evolutionary algorithms such as the genetic algorithm and differential evolution. These methods are based on biological processes, particularly genetics, and define an iterative process that evolves parameter sets into an optimum. Evolutionary computation is a method that operates on a population of existing computational-based engineering models (or simulators) and competes them using biologically inspired genetic operators on large parallel cluster computers. The result is the ability to automatically find design optimizations and trades, and thereby greatly amplify the role of the system engineer.

Terrile, Richard J.↗
DOE OSTI2025

Shape-anisotropy inverses the behavior of emergent vortices in active chiral fluids

Active colloidal fluids exhibit spontaneous emergence of correlated states, characterized by complex collective dynamics and self-organization. In geometrically confined systems, activity modulations trigger robust polar state reversals of a macroscopic vortex formed by colloidal rollers. Here, we reveal that the shape anisotropy of dielectric rollers transforms the system into a chiral active fluid comprised of rollers of arbitrary handedness. The chiral rollers self-assemble into multiple freestanding vortices with a spontaneously selected sense of rotation. We demonstrate that upon reactivation of the system after a complete cessation of activity beyond all relevant timescales, the vortices simultaneously restore their previous chiral states in striking contrast to the chiral state reversals demonstrated by spherical rollers. The analysis reveals that shape-anisotropy modifies the collective state memory of the ensemble. The findings provide insights into the emergence of complex collective behavior in chiral colloidal fluids governed by an interplay between shape anisotropy, chiral motion, and activity modulations.

Colloids↗