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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 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Realizing mechanical frustration at the nanoscale using DNA origami

Structural designs inspired by physical and biological systems have been previously utilized to develop mechanical metamaterials with enhanced properties based on clever geometric arrangement of constituent building blocks. Here, we use the DNA origami method to realize a nanoscale metastructure exhibiting mechanical frustration, a counterpart of the well-known phenomenon of magnetic frustration. By selectively actuating reconfigurable struts, it adopts either frustrated or non-frustrated states, each characterized by distinct free energy profiles. While the non-frustrated state distributes the strain homogeneously, the frustrated mode concentrates it at a specific location. Molecular dynamics simulations reconcile the contrasting behaviors and provide insights into underlying mechanics. We explore the design space further by tailoring responses through structural modifications. Our work combines programmable DNA self-assembly with mechanical design principles to overcome engineering limitations encountered at the macroscale to design dynamic, deformable nanostructures with potential applications in elastic energy storage, nanomechanical computation, and allosteric mechanisms in DNA-based nanomachinery.

DNA nanostructures↗
DOE OSTI2023

Machine learning unifies flexibility and efficiency of spinodal structure generation for stochastic biomaterial design

Abstract Porous biomaterials design for bone repair is still largely limited to regular structures (e.g. rod-based lattices), due to their easy parameterization and high controllability. The capability of designing stochastic structure can redefine the boundary of our explorable structure–property space for synthesizing next-generation biomaterials. We hereby propose a convolutional neural network (CNN) approach for efficient generation and design of spinodal structure—an intriguing structure with stochastic yet interconnected, smooth, and constant pore channel conducive to bio-transport. Our CNN-based approach simultaneously possesses the tremendous flexibility of physics-based model in generating various spinodal structures (e.g. periodic, anisotropic, gradient, and arbitrarily large ones) and comparable computational efficiency to mathematical approximation model. We thus successfully design spinodal bone structures with target anisotropic elasticity via high-throughput screening, and directly generate large spinodal orthopedic implants with desired gradient porosity. This work significantly advances stochastic biomaterials development by offering an optimal solution to spinodal structure generation and design.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Challenges of conventional iterative all-atom and coarse-grained multiscale molecular dynamics

In this work, we evaluate the biomolecular dynamics behaviors when conventionally iterating between all-atom (AA) and coarse-grained (CG) molecular dynamics (MD) simulations over multiple cycles. We implemented the workflow to iterate between AA and CG in OpenMM, namely the iterative multiscale MD (iMMD) simulation workflow. In particular, we aim to identify practical applications for iterating between AA and CG simulations in a conventional manner without any constraints or model modifications. We evaluate the iMMD workflow on four representative systems, spanning folding of two soluble proteins and protein-protein as well as protein-lipid interactions of two membrane proteins. We observe that iteration between AA and CG representations could help the soluble proteins exit undesirable metastable states to fold, resulting from random protein structural distortions due to cycling. Consequently, the most reliable use of iterative AA and CG simulations appears to be to accelerating complex lipid mixing for membrane-bound protein systems rather than sampling protein conformational space. Our work explores the practical usages and limitations for iterative AA and CG simulations using readily available AA and CG force fields. The evaluated iMMD workflow in OpenMM is made available at https://github.com/lanl/iMMD.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Integrating machine learning and multiscale modeling—perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences

Fueled by breakthrough technology developments, the biological, biomedical, and behavioral sciences are now collecting more data than ever before. There is a critical need for time- and cost-efficient strategies to analyze and interpret these data to advance human health. The recent rise of machine learning as a powerful technique to integrate multimodality, multifidelity data, and reveal correlations between intertwined phenomena presents a special opportunityin this regard. However, classical machine learning techniques often ignore the fundamental laws of physics and result in ill-posed problems or non-physical solutions. Multiscale modeling is a successful strategy to integrate multiscale, multiphysics data and uncover mechanisms that explain the emergence of function. However, multiscale modeling alone often fails to efficiently combine large data sets from different sources and different levels of resolution. Here we demonstrate that machine learning and multiscale modeling can naturally complement each other to create robust predictive models that integrate the underlying physics to manage ill-posed problems and explore massive design spaces. We critically review the current literature, highlight applications and opportunities, address open questions, and discuss potential challenges and limitations in four overarching topical areas: ordinary differential equations, partial differential equations, data-driven approaches, and theory-driven approaches. Towards these goals, we leverage expertise in applied mathematics, computer science, computational biology, biophysics, biomechanics, engineering mechanics, experimentation, and medicine. Our multidisciplinary perspective suggests that integrating machine learning and multiscale modeling can provide new insights into disease mechanisms, help identify new targets and treatment strategies, and inform decision making for the benefit of human health.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Cuboidal Liquid Crystal Phases Under Multiaxial Geometrical Frustration.

Cuboidal liquid crystal phases - the so-called blue phases - consist of a network of topological defects arranged into a cubic symmetry. They exhibit striking optical properties, including Bragg reflection in the visible range and fast response times. Confining surfaces can interfere with the packing of such a network, leading to structures that have not been explored before. In this work, a Landau-de Gennes free energy formalism for the tensor alignment field Q is used to investigate the behavior of chiral liquid crystals under non-isotropic confinement. The underlying free energy functional is solved by relying on a Monte Carlo method that facilitates efficient exploration of configuration space. The results of simulations are expressed in terms of phase diagrams as a function of chirality and temperature for three families of spheroids: oblate, spherical, and prolate. Upon deformation, blue phases adapt and transform to accommodate the geometrical constraints, thereby resulting in a wider range of thermal stability. For oblate spheroids, confinement interferes with the development of a full blue phase structure, resulting on a combination of half skyrmions. For prolate spheroids, the blue phases are hybridized and exhibit features of blue phases I and II. More generally, it is shown that mechanical deformation provides an effective means to control, manipulate and stabilize blue phases and cholesterics confined in tactoids.

Palacio-Betancur, Viviana↗
DOE OSTI2022

Data-driven design of novel halide perovskite alloys

The great tunability of the properties of halide perovskites presents new opportunities for optoelectronic applications as well as significant challenges associated with exploring combinatorial chemical spaces. Here, in this work, we develop a framework powered by high-throughput computations and machine learning for the design and prediction of mixed cation halide perovskite alloys. In a chemical space of ABX 3 perovskites with a selected set of options for A, B, and X species, pseudo-cubic structures with B-site mixing are simulated using density functional theory (DFT) and several properties are computed, including stability, lattice constant, band gap, vacancy formation energy, refractive index, and optical absorption spectrum, using both semi-local and hybrid functionals. Neural networks (NN) are used to train predictive models for every property using tabulated elemental properties of A, B, and X site species as descriptors. Starting from a DFT dataset of 229 points, we use the trained NN models to predict the structural, energetic, electronic, and optical properties of an extensive dataset of 17955 compounds, and perform high-throughput screening in terms of stability, band gap, and defect tolerance, to obtain 392 promising compounds that are ranked as potential absorbers according to their photovoltaic figure of merit. Compositional trends in the screened set of attractive mixed cation halide perovskites are revealed and additional computations are performed on selected compounds. The data-driven design framework developed here is promising for designing novel mixed compositions and can be extended to a wider perovskite chemical space in terms of A, B, and X species, different kinds of mixing at the A, B, or X sites, non-cubic phases, and other properties of interest.

36 MATERIALS SCIENCE↗
DOE OSTI2021

The non-adiabatic nanoreactor: towards the automated discovery of photochemistry

The ab initio nanoreactor has previously been introduced to automate reaction discovery for ground state chemistry. In this work, we present the nonadiabatic nanoreactor, an analogous framework for excited state reaction discovery. We automate the study of nonadiabatic decay mechanisms of molecules by probing the intersection seam between adiabatic electronic states with hyper-real metadynamics, sampling the branching plane for relevant conical intersections, and performing seam-constrained path searches. We illustrate the effectiveness of the nonadiabatic nanoreactor by applying it to benzene, a molecule with rich photochemistry and a wide array of photochemical products. Our study confirms the existence of several types of S 0 /S 1 and S 1 /S 2 conical intersections which mediate access to a variety of ground state stationary points. We elucidate the connections between conical intersection energy/topography and the resulting photoproduct distribution, which changes smoothly along seam space segments. The exploration is performed with minimal user input, and the protocol requires no previous knowledge of the photochemical behavior of a target molecule. We demonstrate that the nonadiabatic nanoreactor is a valuable tool for the automated exploration of photochemical reactions and their mechanisms.

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

Sequence-defined structural transitions by calcium-responsive proteins

Biopolymer sequences dictate their functions, and protein-based polymers are a promising platform to establish sequence–function relationships for novel biopolymers. To efficiently explore vast sequence spaces of natural proteins, sequence repetition is a common strategy to tune and amplify specific functions. This strategy is applied to repeats-in-toxin (RTX) proteins with calcium-responsive folding behavior, which stems from tandem repeats of the nonapeptide GGXGXDXUX in which X can be any amino acid and U is a hydrophobic amino acid. To determine the functional range of this nonapeptide, we modified a naturally occurring RTX protein that forms β-roll structures in the presence of calcium. Sequence modifications focused on calcium-binding turns within the repetitive region, including either global substitution of nonconserved residues or complete replacement with tandem repeats of a consensus nonapeptide GGAGXDTLY. Some sequence modifications disrupted the typical transition from intrinsically disordered random coils to folded β rolls, despite conservation of the underlying nonapeptide sequence. Proteins enriched with smaller, hydrophobic amino acids adopted secondary structures in the absence of calcium and underwent structural rearrangements in calcium-rich environments. In contrast, proteins with bulkier, hydrophilic amino acids maintained intrinsic disorder in the absence of calcium. In conclusion, these results indicate a significant role of nonconserved amino acids in calcium-responsive folding, thereby revealing a strategy to leverage sequences in the design of tunable, calcium-responsive biopolymers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Generative AI for design of nanoporous materials: review and future prospects

Generative artificial intelligence (AI) is emerging as a powerful tool for advancing the design of nanoporous materials such as metal–organic frameworks, covalent–organic frameworks, and zeolites. These materials have potential application in important areas such as carbon capture, catalysis, gas storage, chemical separation, and drug delivery due to their modular, tunable structures, and their performance in these areas depends on precise control over their structure, chemical functionalities, and properties. Herein, we provide a review of generative AI algorithms that are emerging as powerful tools for the design of nanoporous materials, namely generative adversarial networks, variational autoencoders, diffusion models, genetic algorithms, reinforcement learning, and large language models. Some models are particularly good at generating diverse and high-quality designs, while others excel at exploring large design spaces or optimizing materials with desired properties. Certain algorithms also allow for efficient transitions between different designs, and some offer versatility in generating materials based on textual input. We discuss the advantages, limitations, and applications of these algorithms in porous material design and emphasize the future potential of integrating AI with experimental workflows to accelerate the development and validation of AI-generated materials.

36 MATERIALS SCIENCE↗
DOE OSTI2026

A divergent synthetic route to functional copolymer libraries via modular polymers

High-throughput polymer synthesis enables rapid exploration of chemical space but remains limited by batch-to-batch inconsistencies that can obscure structure–property relationship trends. To address this challenge, we developed a synthetic approach to produce multifunctional copolymers using post-polymerization modification of activated ester modular polymers with commercially available amines. Easily derivitized parent polymers—poly(tetrafluorophenyl acrylate) and poly(tetrafluorophenyl styrene sulfonate)—were synthesized by RAFT polymerization to yield single polymer batches containing highly reactive tetrafluorophenyl esters or sulfonate esters on each repeat unit. Tuning post-polymerization modification reaction conditions enabled the addition of sub-stoichiometric amounts of amines (relative to the repeat unit) to yield partially functionalized intermediates that could then be further derivatized. Reaction monitoring by 19 F NMR spectroscopy confirmed good control over these sequential post-polymerization modifications. This synthetic route produced a variety of copolymers with defined comonomer ratios while preserving the underlying polymer structure (degree of polymerization, dispersity, tacticity) for both the acrylate and styrene sulfonate backbones. We further applied this approach in a divergent manner to create a small library of structurally distinct copolymers from a single parent batch in three synthetic steps. This modular, divergent synthesis demonstrates a general route to structurally consistent copolymer libraries that enable systematic studies of structure–property relationships and can accelerate functional materials discovery.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

New search for mirror neutron regeneration

The possibility of relatively fast neutron oscillations into a mirror neutron state is not excluded experimentally when a mirror magnetic field is considered. Direct searches for the disappearance of neutrons into mirror neutrons in a controlled magnetic field have previously been performed using ultracold neutrons, with some anomalous results reported. We describe a technique using cold neutrons to perform a disappearance and regeneration search, which would allow us to unambiguously identify a possible oscillation signal. An experiment using the existing General Purpose-Small Angle Neutron Scattering instrument at the High Flux Isotope Reactor at Oak Ridge National Laboratory will have the sensitivity to fully explore the parameter space of prior ultracold neutron searches and confirm or refute previous claims of observation. This instrument can also conclusively test the validity of recently suggested oscillation-based explanations for the neutron lifetime anomaly.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2011

Origin of anomeric effect: A density functional steric analysis

The anomeric effect (the tendency of heteroatomic substituents adjacent to a heteroatom within the cyclohexane ring to prefer the axial orientation instead of the sterically less hindered equatorial position) is traditionally explained through either the dipole moment repulsion or the hyperconjugation effect. In this work, by employing our recent work in density functional steric analysis, we provide a novel two-component explanation, which is consistent with the common belief in chemistry that the effect has a stereoelectronic origin. With α-D-glucopyranose as the prototype, we systematically explore its conformational space and generate 32 isomers, leading to a total of 80 axial–equatorial conformation pairs. The energy difference analysis of these pairs shows that while statistically speaking the tendency is valid, the anomeric effect is not always true and can be violated. Three energy components, exchange–correlation, classical electrostatic, and density functional steric, are found to be directly proportional to the total energy difference between axial and equatorial isomers. We also found that the total dipole moment change, not the hyperconjugation effect, is a reasonable indicator of the total energy difference. However, all these correlations alone are not strong enough to provide a compellingly convincing explanation for the general validity of the effect. With the help of strong correlations between energy components, an explanation with two energy components, steric and electrostatic, was proposed in this work. We show that the axial–equatorial energy difference in general, with the anomeric effect as a special case, is dictated by two factors of the stereoelectronic origin, steric hindrance and classical electrostatic interactions, synchronously working together. Another explanation in terms of exchange–correlation and electrostatic interactions has also been obtained in this work.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2013

Heavy photon search experiment at JLAB

The Heavy Photon Search (HPS) experiment in Hall-B at Jefferson Lab will search for new heavy vector boson(s), aka 'heavy photons', in the mass range of 20 MeV/c{sup 2} to 1000 MeV/c{sup 2} using the scattering of high energy, high intensity electron beams off a high Z target. The proposed measurements will cover the region of parameter space favored by the muon g-2 anomaly, and will explore a significant region of parameter space, not only at large couplings (α′/α > 10{sup −7}), but also in the regions of small couplings, down to α′/α∼10{sup −10}. The excellent vertexing capability of the Si-tracker uniquely enables HPS to cover the small coupling region. Also, HPS will search for heavy photons in an alternative to the e{sup +}e{sup −} decay mode, in the heavy photon's decay to μ{sup +}μ−.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

Comparative atomic-scale hydration of the ceramide and phosphocholine headgroup in solution and bilayer environments

Previous studies have used neutron diffraction to elucidate the hydration of the ceramide and the phosphatidylcholine headgroup in solution. These solution studies provide bond-length resolution information on the system, but are limited to liquid samples. The work presented here investigates how the hydration of ceramide and phosphatidylcholine headgroups in a solution compares with that found in a lipid bilayer. This work shows that the hydration patterns seen in the solution samples provide valuable insight into the preferential location of hydrating water molecules in the bilayer. There are certain subtle differences in the distribution, which result from a combination of the lipid conformation and the lipid-lipid interactions within the bilayer environment. The lipid-lipid interactions in the bilayer will be dependent on the composition of the bilayer, whereas the restricted exploration of conformational space is likely to be applicable in all membrane environments. The generalized description of hydration gathered from the neutron diffraction studies thus provides good initial estimation for the hydration pattern, but this can be further refined for specific systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Semi-analytic model of plasma-jet-driven magneto-inertial fusion

A semi-analytic model for plasma-jet-driven magneto-inertial fusion is presented here. Compressions of a magnetized plasma target by a spherically imploding plasma liner are calculated in one dimension (1D), accounting for compressible hydrodynamics and ionization of the liner material, energy losses due to conduction and radiation, fusion burn and alpha deposition, separate ion and electron temperatures in the target, magnetic pressure, and fuel burn-up. Results show 1D gains of 3–30 at spherical convergence ratio <15 and 20–40 MJ of liner energy, for cases in which the liner thickness is 1 cm and the initial radius of a preheated magnetized target is 4 cm. Some exploration of parameter space and physics settings is presented. The yields observed suggest that there is a possibility of igniting additional dense fuel layers to reach high gain.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

Discovery of blue singlet exciton fission molecules via a high-throughput virtual screening and experimental approach

Singlet exciton fission is a mechanism that could potentially enable solar cells to surpass the Shockley-Queisser efficiency limit by converting single high-energy photons into two lower-energy triplet excitons with minimal thermalization loss. The ability to make use of singlet exciton fission to enhance solar cell efficiencies has been limited, however, by the sparsity of singlet fission materials with triplet energies above the bandgaps of common semiconductors such as Si and GaAs. Here, we employ a high-throughput virtual screening procedure to discover new organic singlet exciton fission candidate materials with high-energy (>1.4 eV) triplet excitons. After exploring a search space of 4482 molecules and screening them using time-dependent density functional theory, we identify 88 novel singlet exciton fission candidate materials based on anthracene derivatives. Subsequent purification and characterization of several of these candidates yield two new singlet exciton fission materials: 9,10-dicyanoanthracene (DCA) and 9,10-dichlorooctafluoroanthracene (DCOFA), with triplet energies of 1.54 eV and 1.51 eV, respectively. These materials are readily available and low-cost, making them interesting candidates for exothermic singlet exciton fission sensitization of solar cells. However, formation of triplet excitons in DCA and DCOFA is found to occur via hot singlet exciton fission with excitation energies above ~3.64 eV, and prominent excimer formation in the solid state will need to be overcome in order to make DCA and DCOFA viable candidates for use in a practical device.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Machine learning for nonintrusive model order reduction of the parametric inviscid transonic flow past an airfoil

Fluid flow in the transonic regime finds relevance in aerospace engineering, particularly in the design of commercial air transportation vehicles. Computational fluid dynamics models of transonic flow for aerospace applications are computationally expensive to solve because of the high degrees of freedom as well as the coupled nature of the conservation laws. While these issues pose a bottleneck for the use of such models in aerospace design, computational costs can be significantly minimized by constructing special, structure-preserving surrogate models called reduced-order models. In this work, we propose a machine learning method to construct reduced-order models via deep neural networks and we demonstrate its ability to preserve accuracy with a significantly lower computational cost. In addition, our machine learning methodology is physics-informed and constrained through the utilization of an interpretable encoding by way of proper orthogonal decomposition. Application to the inviscid transonic flow past the RAE2822 airfoil under varying freestream Mach numbers and angles of attack, as well as airfoil shape parameters with a deforming mesh, shows that the proposed approach adapts to high-dimensional parameter variation well. Notably, the proposed framework precludes the knowledge of numerical operators utilized in the data generation phase, thereby demonstrating its potential utility in the fast exploration of design space for diverse engineering applications. Comparison against a projection-based nonintrusive model order reduction method demonstrates that the proposed approach produces comparable accuracy and yet is orders of magnitude computationally cheap to evaluate, despite being agnostic to the physics of the problem.

97 MATHEMATICS AND COMPUTING↗