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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Inter-ligand cross-links determine secondary building unit formation in oligoMOFs

OligoMOFs are a class of metal–organic frameworks (MOFs) that feature ligands covalently cross-linked into oligomers as intrinsic structural components. A recent study has demonstrated that the size and flexibility of the tether moieties can result in the formation of oligoMOFs with different isomeric structures via geometrical distortions in the secondary building unit (SBU). In this work, it is demonstrated that tethered dimeric ligands with slightly different carbon chain lengths can direct the formation of oligoMOFs with SBUs of different structure and nuclearity. Specifically, inter-ligand cross links are used as a sensitive probe of the phase landscape of MOFs with the composition [Zn 2 (R-bdc) 2 (bpy)] (bdc = 1,4-benzenedicarboxylate, bpy = 4,4′-bipyridine), showing that a hexyl tether results in a framework structure identical to MOF-508, based on dinuclear {Zn 2 } “paddlewheel” SBUs, while heptyl and octyl tethers result in a “honeycomb-like” framework structure based on an infinite rod-like Zn 2+ SBU bridged by carboxylate ligands. Furthermore, these results are the first examples of tether length influencing SBU chemistry in MOFs and help understand the interplay between the MOF lattice and the geometric constraints imposed by inter-ligand cross-links in oligoMOFs.

MOFs↗
DOE OSTI2022

Neutron scattering maps the higher-order assembly of NADPH-dependent assimilatory sulfite reductase

Precursor molecules for biomass incorporation must be imported into cells and made available to the molecular machines that build the cell. Sulfur-containing macromolecules require that sulfur be in its S2- oxidation state before assimilation into amino acids, cofactors, and vitamins that are essential to organisms throughout the biosphere. In α-proteobacteria, NADPH-dependent assimilatory sulfite reductase (SiR) performs the final six-electron reduction of sulfur. SiR is a dodecameric oxidoreductase composed of an octameric flavoprotein reductase (SiRFP) and four hemoprotein metalloenzyme oxidases (SiRHPs). SiR performs the electron transfer reduction reaction to produce sulfide from sulfite through coordinated domain movements and subunit interactions without release of partially reduced intermediates. Efforts to understand the electron transfer mechanism responsible for SiR’s efficiency are confounded by structural heterogeneity arising from intrinsically disordered regions throughout its complex, including the flexible linker joining SiRFP’s flavin-binding domains. As a result, high-resolution structures of SiR dodecamer and its subcomplexes are unknown, leaving a gap in the fundamental understanding of how SiR performs this uniquely large-volume electron transfer reaction. In this work, we use deuterium labeling, in vitro reconstitution, analytical ultracentrifugation (AUC), small-angle neutron scattering (SANS), and neutron contrast variation (NCV) to observe the relative subunit positions within SiR’s higher-order assembly. AUC and SANS reveal SiR to be a flexible dodecamer and confirm the mismatched SiRFP and SiRHP subunit stoichiometry. NCV shows that the complex is asymmetric, with SiRHP on the periphery of the complex and the centers of mass between SiRFP and SiRHP components over 100 Å apart. SiRFP undergoes compaction upon assembly into SiR’s dodecamer and SiRHP adopts multiple positions in the complex. The resulting map of SiR’s higher-order structure supports a cis/trans mechanism for electron transfer between domains of reductase subunits as well as between tightly bound or transiently interacting reductase and oxidase subunits.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Antiviral Activity and Crystal Structures of HIV-1 gp120 Antagonists

As part of our effort to discover drugs that target HIV-1 entry, we report the antiviral activity and crystal structures of two novel inhibitors in a complex with a gp120 core. NBD-14204 showed similar antiviral activity against all the clinical isolates tested. The IC50 values were in the range of 0.24–0.9 µM with an overall mean of 0.47 ± 0.03 µM, showing slightly better activity against the clinical isolates than against the lab-adapted HIV-1 HXB2 (IC 50 = 0.96 ± 0.1 µM). Moreover, the antiviral activity of NBD-14208 was less consistent, showing a wider range of IC 50 values (0.66–5.7 µM) with an overall mean of 3 ± 0.25 µM and better activity against subtypes B and D (Mean IC 50 2.2–2.5 µM) than the A, C and Rec viruses (Mean IC 50 2.9–3.9 µM). SI of NBD-14204 was about 10-fold higher than NBD-14208, making it a better lead compound for further optimization. In addition, we tested these compounds against S375Y and S375H mutants of gp120, which occurred in some clades and observed these to be sensitive to NBD-14204 and NBD-14208. These inhibitors also showed modest activity against HIV-1 reverse transcriptase. Furthermore, we determined the crystal structures of both inhibitors in complexes with gp120 cores. As expected, both NBD-14204 and NBD-14208 bind primarily within the Phe43 cavity. It is noteworthy that the electron density of the thiazole ring in both structures was poorly defined due to the flexibility of this scaffold, suggesting that these compounds maintain substantial entropy, even when bound to the Phe43 cavity.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Constitutive Modeling of the Cross-Linked Polymers During the Trio-Aging Mechanism: Effects of Water, Oxygen, UV

Abstract The synergized effects of diverse environmental conditions such as humidity, temperature, UV, and oxygen, are considered through a generic model by combining the concepts of network alteration, hygrothermal, and Photo-thermo oxidation Models [1–4]. Hygrothermal and Photo-oxidation are considered dual aging mechanisms which are due to the synergized effects of moisture-oxygen, and UV-oxygen, respectively at elevated temperatures. Here Trio-aging is considered as the synergized effects of Hygrothermal, and Photo-oxidation which the main environmental elements involved in polymer degradation are oxygen, water, and UV radiation where each of which has a different micro-structural impact on the polymer matrix. While oxygen imposes chain scission and cross-link formation, water, and UV radiation mainly cause chain scission. Similar to our recent models, the dual network hypothesis of Topolosky is utilized and as a result, the strain energy of the polymer matrix is written in terms of two states namely unaged and infinity. The infinity state network, itself, is decomposed into three networks, namely brittle, flexible, and dead networks. While the brittle network models the cross-link formation as sub-structural phenomena, the flexible and dead networks are utilized to represent the cross-link reduction and chain scission. The contribution of these networks in the infinity state is based on the amount of relevant environmental elements. The model is developed for a domain with plenty of oxygen and holds true for a gradual aging process that happens in super-thin samples.

Bahrololoumi, Amir↗
DOE OSTI2023

Structural insights into redox signal transduction mechanisms in the control of nitrogen fixation by the NifLA system

NifL is a conformationally dynamic flavoprotein responsible for regulating the activity of the σ 54 -dependent activator NifA to control the transcription of nitrogen fixation (nif) genes in response to intracellular oxygen, cellular energy, or nitrogen availability. The NifL-NifA two-component system is the master regulatory system for nitrogen fixation. NifL serves as a sensory protein, undergoing signal-dependent conformational changes that modulate its interaction with NifA, forming the NifL–NifA complex, which inhibits NifA activity in conditions unsuitable for nitrogen fixation. While NifL-NifA regulation is well understood, these conformationally flexible proteins have eluded previous attempts at structure determination. In work described here, we advance a structural model of the NifL dimer supported by a combination of scattering techniques and mass spectrometry (MS)-coupled structural analyses that report on the average structure in solution. Using a combination of small angle X-ray scattering-derived electron density maps and MS-coupled surface labeling, we investigate the conformational dynamics responsible for NifL oxygen and energy responses. Our results reveal conformational differences in the structure of NifL under reduced and oxidized conditions that provide the basis for a model for modulating NifLA complex formation in the regulation of nitrogen fixation in response to oxygen in the model diazotroph,Azotobacter vinelandii.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Machine Learned Force Field Modeling of Metal Organic Frameworks for CO2 Direct Air Capture

Direct air capture (DAC) is a method for removing CO2 directly from air. Metal organic frameworks (MOFs) have been studied as DAC sorbent materials because of their structural and chemical diversity. Thermodynamic calculations using classical force fields are often used to evaluate MOFs for their performance in separations such as CO2 capture. Machine-learned force fields (MLFFs) can use machine learning to form quantitative relationships between a material’s chemical structure and the forces and energies predicted by more accurate quantum mechanical calculations, such as dispersion-corrected density functional theory (DFT). These descriptions of forces and energies can be used to improve the accuracy of adsorption calculations. In this work, classical models were used to pre-screen MOFs for CO2 capture. DFT calculations were then used to examine the adsorption mechanism. Next, MLFF models were developed for MOFs to achieve DFT-level accuracy for the forces and energies associated with MOF flexibility and CO2 adsorption. These methods were parametrized based on thousands of DFT calculations of CO2 in flexible MOFs and used to predict MOF structural properties as well as CO2 adsorption properties.

Findley, John↗
DOE OSTI2024

Accelerating structural dynamics simulations with localised phenomena through matrix compression and projection‐based model order reduction

In this work, a novel approach is introduced for accelerating the solution of structural dynamics problems in the presence of localised phenomena, such as cracks. For this category of problems, conventional projection-based Model Order Reduction (MOR) methods are either limited with respect to the range of system configurations that can be represented or require frequent solutions of the Full Order Model (FOM) to update the low-dimensional spaces, in which solutions are represented. In the proposed approach, low-dimensional spaces, constructed for the healthy structure, are enriched with appropriately selected columns of the flexibility matrix of the system. It can be shown that these spaces contain the solution to the original problem for the static case, while their dimension is much smaller. In order to allow their online construction for arbitrary localised features, the full flexibility matrix of the system should be available. To this end, a hierarchical representation is used for the matrices involved, allowing to compute the flexibility matrix efficiently and with reduced memory requirements. The resulting method offers significant speedups, without sacrificing the flexibility and accuracy of the full order model. The performance and limitations of the approach are studied through a series of examples in structural dynamics.

fracture mechanics↗
DOE OSTI2025

Neural chaos: A spectral stochastic neural operator

Building surrogate models for operators with uncertainty quantification capabilities is essential for many engineering applications where randomness–such as variability in material properties, boundary conditions, and initial conditions–is unavoidable. Polynomial Chaos Expansion (PCE) is widely recognized as a go-to method for constructing stochastic surrogates in both intrusive and non-intrusive ways, and it has recently been used in the context of operator learning. However, its application becomes challenging for complex or high-dimensional processes, as achieving accuracy requires higher-order polynomials, which can increase computational demand and/or the risk of overfitting. Furthermore, PCE requires specialized treatments to manage random variables that are not independent, and these treatments may be problem-dependent or may fail with increasing complexity. Here, in this work, we adopt the same formalism as the spectral expansion used in PCE; however, we replace the classical polynomial basis functions with neural network (NN) basis functions to leverage their expressivity. To achieve this, we propose an algorithm that identifies NN-parameterized basis functions in a purely data-driven manner, without any prior assumptions about the joint distribution of the random variables involved, whether independent or dependent, or about their marginal distributions. The proposed algorithm identifies each NN-parameterized basis function sequentially, ensuring they are orthogonal with respect to the data distribution. The basis functions are constructed directly on the joint stochastic variables without requiring a tensor product structure or assuming independence of the random variables. This approach may offer greater flexibility for complex stochastic models, while simplifying implementation compared to the tensor product structures typically used in PCE to handle random vectors. This is particularly advantageous given the current state of open-source packages, where building and training neural networks can be done with just a few lines of code and extensive community support. We demonstrate the effectiveness of the proposed scheme through several numerical examples of varying complexity and provide comparisons with classical PCE.

Polynomial chaos expansion↗
DOE OSTI2024

Predicting RNA structure and dynamics with deep learning and solution scattering

Advanced deep learning and statistical methods can predict structural models for RNA molecules. However, RNAs are flexible, and it remains difficult to describe their macromolecular conformations in solutions where varying conditions can induce conformational changes. Small-angle x-ray scattering (SAXS) in solution is an efficient technique to validate structural predictions by comparing the experimental SAXS profile with those calculated from predicted structures. There are two main challenges in comparing SAXS profiles to RNA structures: the absence of cations essential for stability and charge neutralization in predicted structures and the inadequacy of a single structure to represent RNA’s conformational plasticity. We introduce a solution conformation predictor for RNA (SCOPER) to address these challenges. This pipeline integrates kinematics-based conformational sampling with the innovative deep learning model, IonNet, designed for predicting Mg 2+ ion binding sites. Validated through benchmarking against 14 experimental data sets, SCOPER significantly improved the quality of SAXS profile fits by including Mg 2+ ions and sampling of conformational plasticity. We observe that an increased content of monovalent and bivalent ions leads to decreased RNA plasticity. Therefore, carefully adjusting the plasticity and ion density is crucial to avoid overfitting experimental SAXS data. SCOPER is an efficient tool for accurately validating the solution state of RNAs given an initial, sufficiently accurate structure and provides the corrected atomistic model, including ions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Effect of layer bending on montmorillonite hydration and structure from molecular simulation

Conceptual models of smectite hydration include planar (flat) clay layers that undergo stepwise expansion as successive monolayers of water molecules fill the interlayer regions. However, X-ray diffraction (XRD) studies indicate the presence of interstratified hydration states, suggesting non-uniform interlayer hydration in smectites. Additionally, recent theoretical studies have shown that clay layers can adopt bent configurations over nanometer-scale lateral dimensions with minimal effect on mechanical properties. Therefore, in this study we used molecular simulations to evaluate structural properties and water adsorption isotherms for montmorillonite models composed of bent clay layers in mixed hydration states. Results are compared with models consisting of planar clay layers with interstratified hydration states (e.g. 1W–2W). The small degree of bending in these models (up to 1.5 Å of vertical displacement over a 1.3 nm lateral dimension) had little or no effect on bond lengths and angle distributions within the clay layers. Except for models that included dry states, porosities and simulated water adsorption isotherms were nearly identical for bent or flat clay layers with the same averaged layer spacing. Similar agreement was seen with Na- and Ca-exchanged clays. In conclusion, while the small bent models did not retain their configurations during unconstrained molecular dynamics simulation with flexible clay layers, we show that bent structures are stable at much larger length scales by simulating a 41.6×7.1 nm 2 system that included dehydrated and hydrated regions in the same interlayer.

58 GEOSCIENCES↗
DOE OSTI2021

Orbital Angular Momentum from Self‐Assembled Concentric Nanoparticle Rings

Abstract Ring‐shaped nanostructures can focus, filter, and manipulate electromagnetic waves, but are challenging to incorporate into devices using standard nanofabrication techniques. Directed self‐assembly (DSA) of block copolymers (BCPs) on lithographically patterned templates has successfully been used to fabricate concentric rings and spirals as etching masks. However, this method is limited by BCP phase behavior and material selection. Here, a straightforward approach to generate ring‐shaped nanoparticle assemblies in thin films of supramolecular nanocomposites is demonstrated. DSA is used to guide the formation of concentric rings with radii spanning 150–1150 nm and ring widths spanning 30–60 nm. When plasmonic nanoparticles are used, ring nanodevice arrays can be fabricated in one step, and the completed devices produce high‐quality orbital angular momentum (OAM). Nanocomposite DSA simplifies and streamlines nanofabrication by producing metal structures without etching or deposition steps; it also introduces interparticle coupling as a new design axis. Detailed analysis of the nanoparticle ring assemblies confirms that the supramolecular system self‐regulates the spatial distribution of its components, and thus exhibits a degree of flexibility absent in DSA of BCPs alone, where structures are determined by polymer‐pattern incommensurability. The present studies also provide guidelines for developing self‐regulating DSA as an alternative to incommensurability‐driven methods.

Vargo, Emma↗
DOE OSTI2022

kynema-fmb [SWR-23-07]

Kynema-FMB (FKA: Kynema) is an open-source performance portable flexible multibody (FMB) dynamics solver designed for time-domain simulations. While originally tailored for wind turbine structural dynamics, the formulation and implementation are those of a general flexible-multidbody dynamics solver that can readily be applied to a wide range of systems. Kynema was designed with a narrow focus, namely to provide a lightweight, fast, accurate FMD solver for coupling to computational-fluid-dynamics (CFD) codes, especially the CFD codes in the Kynema suite, for fluid-structure-interaction (FSI) simulations. Kynema-FMB is equipped to model systems that can be represented as a collection of beams and rigid bodies that are connected through constraints. Degrees of freedom are defined in the inertial/global frame of reference and include displacements and rotations (formally as rotation matrices, but stored as quaternions). The underlying formulation is built on a Lie-group time integrator designed for index-3 differential-algebraic equations, which is second-order accurate in time (Bruls et al., 2012). Beam models are based on geometrically exact beam theory and are discretized as high-order spectral finite elements similar to those in BeamDyn (Wang et al., 2017). The governing equations for a FMD system like a wind turbine constitute a highly nonlinear system of constrained partial-differential equations. Kynema-FMB uses analytical Jacobians in the nonlinear-system solves in each time step. Linear systems use sparse storage and several third-party sparse-linear-system solvers are enabled. Ill conditioning of linear systems is mitigated with preconditioning described in Bottasso et al, 2008. Kynema-FMB is integrated with a simple open-source controller (ROSCO). There is an application programming interface (API) for coupling to geometry-resolved CFD (like that in Sharma et al., 2023) and actuator-force CFD (like that in Kuhn et al., 2025). In the latter, for actuator-line models, Kynema-FMB includes an internal blade-element solver that depends on user-provided lookup tables for coefficients of lift and drag, i.e., aerodynamic polars. Kynema-FMB is written in C++ and leverages Kokkos and Kokkos-Kernels (KokkosEcosystem) as its performance portability layer enabling simulations on both CPU and GPU systems. The repository is equipped with extensive automated testing at the unit and regression/system levels. The following describes the high-level development objectives conceived for Kynema: *Kynema will follow modern software development best practices, including test-driven development (TDD), version control, hierarchical automated testing, and continuous integration (CI) for a robust development environment. *The core data structures are memory efficient and enable vectorization and parallelization at multiple levels. *Data structures are data-oriented to exploit methods for accelerated computing including high utilization of chip resources (e.g., single instruction multiple data (SIMD) instruction sets) and parallelization using GP-GPUs. *The computational algorithms incorporate robust open-source libraries for mathematical operations, resource allocation, and data management. *The API design considers multiple stakeholder needs and ensure integration with existing and future ecosystems for data science, machine learning, and AI. *Kynema-FMB is written in modern C++ and leverages Kokkos as its performance-portability library with inspiration from the kynema stack.

Sprague, MichaelA.↗
DOE OSTI2020

Crystallizing Atomic Xenon in a Flexible MOF to Probe and Understand Its Temperature-Dependent Breathing Behavior and Unusual Gas Adsorption Phenomenon

Flexible metal–organic frameworks (MOFs) hold great promise as smart materials for specific applications such as gas separation. These materials undergo interesting structural changes in response to guest molecules, which is often associated with unique adsorption behavior not possible for rigid MOFs. Understanding the dynamic behavior of flexible MOFs is crucial yet challenging as it involves weak host–guest interactions and subtle structural transformation not only at the atomic/molecular level but also in a nonsteady state. Here we report an in-depth study on the adsorbate- and temperature-dependent adsorption in a flexible MOF by crystallizing atomic gases into its pores. Mn(ina)2 shows an interesting temperature-dependent response toward noble gases. Its nonmonotonic, temperature-dependent adsorption profile results in an uptake maximum at a temperature threshold, a phenomenon that is unusual. Full characterization of Xe-loaded MOF structures is performed by in situ single-crystal and synchrotron X-ray diffraction, IR spectroscopy, and molecular modeling. The X-ray diffraction analysis offers a detailed explanation into the dynamic structural transformation and provides a convincing rationalization of the unique adsorption behavior at the molecular scale. The guest and temperature dependence of the structural breathing gives rise to an intriguing reverse of Xe/Kr adsorption selectivity as a function of temperature. The presented work may provide further understanding of the adsorption behavior of noble gases in flexible MOF structures.

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

Characterization of Deformational Isomerization Potential and Interconversion Dynamics with Ultrafast X-ray Solution Scattering

Dimeric complexes composed of d 8 square planar metal centers and rigid bridging ligands provide model systems to understand the interplay between attractive dispersion forces and steric strain in order to assist the development of reliable methods to model metal dimer complexes more broadly. [Ir 2 (dimen) 4 ] 2+ (dimen = para-diisocyanomenthane) presents a unique case study for such phenomena, as distortions of the optimal structure of a ligand with limited conformational flexibility counteract the attractive dispersive forces from the metal and ligand to yield a complex with two ground state deformational isomers. Here, in this work, we use ultrafast X-ray solution scattering (XSS) and optical transient absorption spectroscopy (OTAS) to reveal the nature of the equilibrium distribution and the exchange rate between the deformational isomers. The two ground state isomers have spectrally distinct electronic excitations that enable the selective excitation of one isomer or the other using a femtosecond duration pulse of visible light. We then track the dynamics of the nonequilibrium depletion of the electronic ground state population—often termed the ground state hole—with ultrafast XSS and OTAS, revealing a restoration of the ground state equilibrium in 2.3 ps. This combined experimental and theoretical study provides a critical test of various density functional approximations in the description of bridged d 8 –d 8 metal complexes. The results show that density functional theory calculations can reproduce the primary experimental observations if dispersion interactions are added, and a hybrid functional, which includes exact exchange, is used.

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

Fibrous anisotropy and mineral gradients within the radula stylus of chiton: Controlled stiffness and damage tolerance in a flexible biological composite

Over hundreds of millions of years, organisms have evolved architected structures via precise control over hierarchically assembled components, including the integration of dissimilar materials. One such example is found in the radula system of chitons, intertidal mollusks that feed on algae growing on the rock. Their radula consists of multiple rows of ultrahard teeth, each integrated with a foldable belt-like substrate via a stiff, yet flexible stylus, which is essential for efficient rasping during the feeding process. Here, we investigate the nano and micro-scale components and architectures as well as regional mechanical properties of the stylus, and their subsequent role during the rasping of Cryptochiton stelleri. Three important factors were determined to contribute to the regio-specific stiffness of the stylus: the presence of mineral components, highly oriented chitinous fibers, and a chemically cross-linked protein matrix. All these factors are varied throughout the stylus. There is a high mineral content on the trailing edge close to the tooth and a cross-linked matrix on the leading edge, both with orientational specific oriented chitin fibers that provide force transduction to the tooth. Conversely, there is a significant lack of mineral or cross-linked matrix in the proximal end as well as a low degree of fiber orientation, resulting in a flexible region that can accommodate torsion and flexure during rasping. Understanding the graded composite structure of the stylus and applying this unique design to various engineering fields such as soft robotics, biotechnology, and the medical industry, can inspire the production of high-performance materials.

Lee, Jung-Eun↗
DOE OSTI2022

Physics Discovery in Nanoplasmonic Systems via Autonomous Experiments in Scanning Transmission Electron Microscopy

Abstract Physics‐driven discovery in an autonomous experiment has emerged as a dream application of machine learning in physical sciences. Here, this work develops and experimentally implements a deep kernel learning (DKL) workflow combining the correlative prediction of the target functional response and its uncertainty from the structure, and physics‐based selection of acquisition function, which autonomously guides the navigation of the image space. Compared to classical Bayesian optimization (BO) methods, this approach allows to capture the complex spatial features present in the images of realistic materials, and dynamically learn structure–property relationships. In combination with the flexible scalarizer function that allows to ascribe the degree of physical interest to predicted spectra, this enables physical discovery in automated experiment. Here, this approach is illustrated for nanoplasmonic studies of nanoparticles and experimentally implemented in a truly autonomous fashion for bulk‐ and edge plasmon discovery in MnPS 3 , a lesser‐known beam‐sensitive layered 2D material. This approach is universal, can be directly used as‐is with any specimen, and is expected to be applicable to any probe‐based microscopic techniques including other STEM modalities, scanning probe microscopies, chemical, and optical imaging.

42 ENGINEERING↗
DOE OSTI2023

Photo-actuators via epitaxial growth of microcrystal arrays in polymer membranes

Photomechanical crystals composed of three-dimensionally ordered and densely packed photochromes hold promise for high-performance photochemical actuators. However, bulk crystals with high structural ordering are severely limited in their flexibility, resulting in poor processibility and a tendency to fragment upon light exposure, while previous nano- or microcrystalline composites have lacked global alignment. Here we demonstrate a photon-fuelled macroscopic actuator consisting of diarylethene microcrystals in a polyethylene terephthalate host matrix. These microcrystals survive large deformations and show a high degree of three-dimensional ordering dictated by the anisotropic polyethylene terephthalate, which critically also has a similar stiffness. Overall, these ordered and compliant composites exhibit rapid response times, sustain a performance of over at least hundreds of cycles and generate work densities exceeding those of single crystals. In conclusion, our composites represent the state-of-the-art for photochemical actuators and enable properties unattainable by single crystals, such as controllable, reversible and abrupt jumping (photosalient behaviour).

36 MATERIALS SCIENCE↗
DOE OSTI2022

Permutation invariant polynomial neural network based diabatic ansatz for the ( E + A ) × ( e + a ) Jahn–Teller and Pseudo-Jahn–Teller systems

In this work, the permutation invariant polynomial neural network (PIP-NN) approach is employed to construct a quasi-diabatic Hamiltonian for system with non-Abelian symmetries. It provides a flexible and compact NN-based diabatic ansatz from the related approach of Williams, Eisfeld, and co-workers. The example of $H^3_+$ is studied, which is an (E + A) × (e + a) Jahn–Teller and Pseudo-Jahn–Teller system. The PIP-NN diabatic ansatz is based on the symmetric polynomial expansion of Viel and Eisfeld, the coefficients of which are expressed with neural network functions that take permutation-invariant polynomials as input. This PIP-NN-based diabatic ansatz not only preserves the correct symmetry but also provides functional flexibility to accurately reproduce ab initio electronic structure data, thus resulting in excellent fits. Further, the adiabatic energies, energy gradients, and derivative couplings are well reproduced. A good description of the local topology of the conical intersection seam is also achieved. Therefore, this diabatic ansatz completes the PIP-NN based representation of DPEM with correct symmetries and will enable us to diabatize even more complicated systems with complex symmetries.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗