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Phase Behavior of Salt-Doped A/B/AB Ternary Polymer Blends: The Role of Homopolymer Distribution
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Additively Manufactured Silicone Polymer Composite with High Hydrogen Getter Content and Hydrogen Absorption Capacity
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Ultrasensitive label-free optical recording of bioelectric potentials using dioxythiophene-based electrochromic polymers
Not provided.
HydraGNN_Predictive_GFM_2026 - Ensemble of predictive graph foundation models for atomistic materials modeling
This release contains data and parameters of HydraGNN-based graph foundation models trained as a result of the work published in the pre-print "Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data" by M. Lupo Pasini et al. (https://arxiv.org/abs/2604.15380). We jointly train on 16 open first-principles datasets (544+ million structures covering 85+ elements) using a multi-task architecture with per-dataset heads and a scalable ADIOS2/DDStore data pipeline. On Frontier, we execute six large-scale DeepHyper hyperparameter optimization campaigns in FP64 and promote the top-performing message-passing models to sustained 2,048-node training, yielding a PaiNN-based lead model. The version of HydraGNN used to generate the outputs provided in this release is HydraGNN v5.0 (https://github.com/ORNL/HydraGNN/releases/tag/v5.0) The list of datasets used for the training of the graph foundation model is the following: 1) Alexandria [1] 2) ANI1x [2] 3) MPTrj [3] 4) Open Catalyst 2020 (OC20) [4] 5) Open Catalyst 2022 (OC22) [5] 6) Open Catalyst 2025 (OC25) [6] 7) Open Direct ir Capture 2023 (ODAC23) [7] 8) Open Materials 2024 (OMat24) [8] 9) Open Molecules 2025 (OMol25) [9] 10) OMol25-neutral (subset of OMol25 that contains only molecules with zero total charge) 11) OMol25-non-neutral (subset of OMol25 that contains only molecules with non-zero total charge) 12) Open Polymers 2026 (OPoly2026) [10] 13) Nabla2DFT [11] 14) QCML [12] 15) QM7X [reference 13] 16) transition1x [14] Dataset references: [1] J. Schmidt et al., “A dataset of 175k stable and metastable materials calculated with the PBEsol and SCAN functionals,” Scientific Data, vol. 9, p. 64, 2022. [2] J. S. Smith et al., “The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules,” Scientific Data, vol. 7, p. 134, 2020. [Online]. Available: https: //www.nature.com/articles/s41597-020-0473-z [3] A. Jain et al., “Commentary: The Materials Project: A materials genome approach to accelerating materials innovation,” APL Materials, vol. 1, no. 1, p. 011002, 07 2013. [Online]. Available: https://doi.org/10.1063/1.4812323 [4] L. Chanussot et al., “Open catalyst 2020 (oc20) dataset and community challenges,” ACS Catalysis, vol. 11, no. 10, pp. 6059–6072, 2021. [Online]. Available: https://doi.org/10.1021/acscatal.0c04525 [5] K. Tran et al., “Open catalyst 2022 (oc22) dataset and challenges for oxidation electrocatalysts,” ACS Catalysis, vol. 13, no. 5, pp. 3066–3084, 2023. [Online]. Available: https://doi.org/10.1021/acscatal.2c05426 [6] S. J. Sahoo et al., “The open catalyst 2025 (oc25) dataset and models for solid-liquid interfaces,” arXiv preprint arXiv:2509.17862, 2025. [Online]. Available: https://arxiv.org/abs/2509.17862 [7] A. Sriram et al., “The open DAC 2023 dataset and challenges for sorbent discovery in direct air capture,” ACS Central Science, vol. 10, no. 5, pp. 923–941, 2024. [8] L. Barroso-Luque et al., “Open materials 2024 (omat24) inorganic materials dataset and models,” 2024. [Online]. Available: https://arxiv.org/abs/2410.12771 [9] D. S. Levine et al., “The open molecules 2025 (OMol25) dataset, evaluations, and models,” 2025. [Online]. Available: https://arxiv.org/abs/2505.08762 [10] D. S. Levine et al., The open polymers 2026 (OPoly26) dataset and evaluations,” arXiv preprint arXiv:2512.23117, 2025. [Online]. Available: https://arxiv.org/abs/2512.23117 [11] K. Khrabrov et al., “Nabla2dft: A universal quantum chemistry dataset of drug-like molecules and a benchmark for neural network potentials,” in NeurIPS 2024 Datasets and Benchmarks Track, 2024. [Online]. Available: https://openreview.net/forum?id=ElUrNM9U8c [12] S. Ganscha et al., “The QCML dataset, quantum chemistry reference data from 33.5M DFT and 14.7B semi-empirical calculations,” Scientific Data, vol. 12, p. 406, 2025. [13] J. Hoja et al., “QM7-X, a comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules,” Scientific Data, vol. 8, p. 43, 2021. [Online]. Available: https://www.nature.com/articles/s41597-021-00812-2 [14] M. Schreiner et al., “Transition1x - a dataset for building generalizable reactive machine learning potentials,” Scientific Data, vol. 9, p. 779, 2022. The folder "datasets_ADIOS2_format" contains the set of pre-processed datasets in Adaptable I/O System (ADIOS) format (https://www.exascaleproject.org/research-project/adios/) that have been used for the development and training of GFMs in this work. The "datasets_ADIOS2_format" directory contains 2 sub-directories, one for the version "v1" of the datasets and one for the version "v2" of the datasets. The version "v1" of the datasets provides values of the total energy as they are extracted from the original data as it was released by the respective institutions. The version "v2" of the datasets provides values of the energy that have been realigned. The realignment was performed by training a linear regression model that predicts the total energy as a function of the chemical composition of the atomistic structure, and then subtract such prediction from the original value of the total energy. Both folders "v1" and "v2" contain 16 sub-directories, each corresponding to an ADIOS2-formatted dataset The folder "DeepHyper-results" contains the configurational files and model's parameters for all the 186 HPO trials that were successfully completed by the scalable hyperparameter optimization (HPO) runs on Frontier. The content of the folder "DeepHyper-results" I structured as follows: 1) task-list.txt: list of mpnn name, jobid, and deephyper task id 2) gfm_${MPNN}_${JOBID}_0.${TASKID}: run directory with checkpoint files 3) gfm_${MPNN}: deephyper summary directory (*.csv) for each specific MPNN type 4) deephyper-experiment-${JOBID}: output and error logs for each job The file "deephyper-sorted.csv" contains the details of each HydraGNN model built and tested by HPO, obtained by merging the (*.csv) filed from each HPO run executed. Out of all the HPO trials, we selected 10 to continue the training of the respective HydraGNN models. Due to limited computational budget available in the LRN070 allocation we could not complete the training till convergence for all these 10 selected models. The folder "models" contains multiple sub-folders, one per each HydraGNN model trained. Each model sub-folder contains the parameters of each HydraGNN model, with multiple checkpoint-restarts. The list of sub-folders are as follows: 1) multidataset_hpo-BEST1-fp64 2) multidataset_hpo-BEST2-fp64 3) multidataset_hpo-BEST3-fp64 4) multidataset_hpo-BEST4-fp64 5) multidataset_hpo-BEST5-fp64 6) multidataset_hpo-BEST6-fp64 7) multidataset_hpo-BEST7-fp64 8) multidataset_hpo-BEST8-fp64 9) multidataset_hpo-BEST9-fp64 10) multidataset_hpo-BEST10-fp64 Within each one of these folders, additional auxiliary log files are provided with descriptions about how the training proceeded. The lead PaiNN-model is contained inside "multidataset_hpo-BEST6-fp64". The file "mlp_branch_weights" contains the parameters of the multi-layer perceptron (MLP) used to reconcile the predictions of the 16 output decoding heads of the HydragNN architectures. The MLP takes in input the chemical composition of the atomistic structure and predicts averaging weights to linearly mix the predictions of each output decoding head toward consolidating them into a single one. The folder "1.1billion-structure-inference" contains 1.1 billion atomistic structures randomly generated. Each structures is associated with energy and forces predicted with the lead-PaiNN model combined with the MLP model for reconciliation of the multi-branch predictions generated by the 16 output decoding heads. The folder "1.1billion-structure-inference" contains 9,300 (*.tar.gz) subdirectories, one per Frontier compute node used to execute the inference at exascale. Once uncompressed, each (*.tar.gz) subdirectory contains an ADIOS2 (*.bp) file container, where each atomistic structure is stored as a PyTorch-Geometric Data object. The file "export_dataset_environment_variables.sh" contains the environment variables that need to be set before running the HydraGNN code to reproduce the results provided in this dataset release. The code that can be used to load the ADIOS2 files, load HydraGNN models, and run inference is available at: https://github.com/ORNL/HydraGNN/releases/tag/v5.0
Scattering evidence of positional charge correlations in polyelectrolyte complexes
Polyelectrolyte complexation plays an important role in materials science and biology. The internal structure of the resultant polyelectrolyte complex (PEC) phase dictates properties such as physical state, response to external stimuli, and dynamics. Small-angle scattering experiments with X-rays and neutrons have revealed structural similarities between PECs and semidilute solutions of neutral polymers, where the total scattering function exhibits an Ornstein–Zernike form. In spite of consensus among different theoretical predictions, the existence of positional correlations between polyanion and polycation charges has not been confirmed experimentally. Here, we present small-angle neutron scattering profiles where the polycation scattering length density is matched to that of the solvent to extract positional correlations among anionic monomers. The polyanion scattering functions exhibit a peak at the inverse polymer screening radius of Coulomb interactions, q* ≈ 0.2 Å –1 . This peak, attributed to Coulomb repulsions between the fragments of polyanions and their attractions to polycations, is even more pronounced in the calculated charge scattering function that quantifies positional correlations of all polymer charges within the PEC. Screening of electrostatic interactions by adding salt leads to the gradual disappearance of this correlation peak, and the scattering functions regain an Ornstein–Zernike form. Experimental scattering results are consistent with those calculated from the random phase approximation, a scaling analysis, and molecular simulations.
Nanotechnology Concepts at MSFC: Engineering Directorate
Nanotechnology is the art and science of building materials and devices at the ultimate level of finesse: atom by atom. Our nation's space program has needs for miniaturization of components, minimization of weight and maximization of performance, and nanotechnology will help us get there. MSFC - Engineering Directorate (ED) is committed to developing nanotechnology that will enable MSFC missions in space transportation, space science and space optics manufacturing. MSFC-ED has a dedicated group of technologists who are currently developing high pay-off nanotechnology concepts. This poster presentation will outline some of the concepts being developed at this time including, nanophase structural materials, carbon nanotube reinforced metal and polymer matrix composites, nanotube temperature sensors and aerogels. The poster will outline these concepts and discuss associated technical challenges in turning these concepts into real components and systems.
Biomimetic Rebuilding of Multifunctional Red Blood Cells: Modular Design Using Functional Components
The design and synthesis of artificial materials that mimic the structures, mechanical properties, and ultimately functionalities of biological cells remains a current holy grail of materials science. Here based on a silica cell bio-replication approach, we report the design and construction of synthetic rebuilt red blood cells (RRBCs) that fully mimic the broad properties of native RBCs: size, biconcave shape, deformability, oxygen carrying capacity, long circulation time. Four successive nano-scale processing steps: RBC bioreplication, layer-by-layer polymer deposition, and precision silica etching, followed by RBC ghost membrane vesicles fusion are employed for RRBC construction. A panel of physicochemical analyses including zeta potential measurement, fluorescence microscopy, and antibody-mediated agglutination assay proved the recapitulation of RBC shape, size, and membrane structure. Flow-based deformation studies carried out in a microfluidic blood capillary model confirrned the ability of RRBCs to deform and pass through small slits and reconstitute themselves in a manner comparable to native RBCs. Circulation studies of RRBCs conducted ex-ovo in a chick embryo and in vivo in a mouse model demonstrated the requirement of both deformability and native cell membrane surface to achieve long-term circulation. To confer additional non-native functionalities to RRBCs, we developed modular procedures in which to load functional cargos such as hemoglobin, drugs, magnetic nanoparticles, and ATP biosensors within the RRBC interior to enable various functions, including oxygen delivery, therapeutic drug delivery, magnetic manipulation, and toxin biosensing and detection. Taken together, RRBCs represent a class of long circulating RBC-inspired artificial hybrid materials with a broad range of potential applications.
Evidence of plasma‐driven decomposition of common plastics exposed to an atmospheric nonthermal discharge
Abstract A nonthermal, pulsed spark discharge is applied to three polymer powders in Ar and Ar– gas mixtures. Hydrogen is introduced to assess plasma‐driven decomposition. Gaseous decomposition products, including methane, acetylene, and ethylene, are observed with Fourier‐transform infrared (FTIR). Surface modifications are observed on the residual polymer via attenuated total internal reflection‐FTIR. Time‐averaged rotational, vibrational, and excitation temperatures are characterized in the discharge. The plasma density is found to be around , with rotational and vibrational temperatures ranging from 1500 to 2200 K and an excitation temperature of 1–2 eV. While spark properties did not change with either gas composition or polymer composition, it was determined that the addition of hydrogen promoted higher concentrations of gaseous phase products (promoting hydrogenolysis).
Fracture toughness and crack propagation in LX-17 and PBX 9502 insensitive high explosives
High explosive materials, and in particular insensitive high explosive (IHE) materials, are of significant interest. TATB-based LX-17 and PBX 9502 are two IHE’s and mechanical properties are an important metric of performance. In this work we present, for the first time, a study of fracture as a function of temperature and displacement rate and starter crack length on billet pressed IHE materials using a standard three point bend technique. We demonstrated that the technique can yield consistent results over time by measuring samples from the same lots 4 years later and observed that the fracture toughness of LX-17 and PBX 9502 are similar. We also determined the temperature dependence for different starter crack lengths and found that the fracture toughness generally decreases with larger starter crack sizes and temperatures. For the LX-17, where a wide temperature range of -60 C to 50 C was investigated, the impact of temperature on fracture toughness between -20 C and 23 C was less than expected. For the PBX 9502 we observed the difference in fracture toughness values between -20 C and 23 C did depend on the particular lot, and also there was a tendency of the fracture toughness to scale with the tensile strength. We also investigated the effect of a lower displacement rate and observed that while the impact on the fracture toughness was relatively small, the samples surprisingly did not break (fail) after exceeding the peak load. We took advantage of this to perform x-ray computed tomography (CT) of the samples to image crack propagation in the intact samples. We observed that cracks were generally straight and unaffected by the microstructure in the LX-17, in contrast to the PBX 9502 where the direction of the cracks would frequently change and were likely influenced by the underlying microstructure.
Chemically and Mechanically Recyclable Vitrimers from Carbon Dioxide-Based Polycarbonates
Designing thermoset materials with dynamic crosslinks is an important strategy to mitigate rising global carbon dioxide emission levels. The development of polymers from sustainable feedstocks, with efficient manufacturing methods, for high-value applications, and with circular end-of-use solutions is essential for advancing material technologies. One approach involves exploiting carbon dioxide itself as feedstock to create high performance, sustainable materials, by enchaining 50 mol% CO2 via copolymerization with epoxides to yield polycarbonates. This work describes the synthesis, end-functionalization, and curing of poly(propylene carbonate) (PPC) and poly(cyclohexene carbonate) (PCHC) into beta-hydroxy ester vitrimers. These vitrimers demonstrate the ability to be mechanically reprocessed up to 3 times with retention of the material’s properties through dynamic transesterification exchange reactions. The polycarbonate vitrimers with gel fractions exceeding 90 % exhibit high tensile strength (> 50 MPa) and Young’s modulus (> 2 GPa), achieved by varying repeat unit structure in the polymer backbone from the low Tg PPC to the more rigid high Tg PCHC structures. Owing to an entropically favorable chain back-biting mechanism, the network chains can be cleaved and depolymerized into cyclic small molecules. In the case of PCHC, this process enables repolymerization back to polycarbonates with 69 wt.% CO2 retention through life-cycles. The promising mechanical performance and recyclability of these CO2-based polycarbonate vitrimers indicate their potential for sustainable, high-performance materials, paving the way for future innovations in circular polymer technologies and carbon capture utilization.
Effect of sticker clustering on the dynamics of associative networks
Exploration of effect of sticker clustering on dynamics of associative polymer networks showed trends in rheological relaxation and diffusion with clustering are different than for uniformly distributed stickers.
Photo-Crosslinkable Naphthalene Diimide Polymer for Solution Processed n-i-p Perovskite Solar cells
Lead-halide perovskite has attracted tremendous attention due to its extraordinary optoelectronic properties.1 Charge transport layers play an important role in both solar cell device efficiency and long-term stability.2 Here we used a photo-crosslinkable polymer for n-i-p perovskite solar cells. Compared to the non-crosslinkable counterpart, we see an overall improved open-circuit voltage and efficiency in mixed-cation mixed-halide perovskite solar cell using the crosslinked naphthalene diimide polymer (NDI-CL) as cathode modification layer.
Porous Organic Cage Membranes for Molecular Gas Separations (Final Technical Report for DE-SC0021357)
This proposal aims at demonstrating the development of a novel family of membranes, composed of porous organic cages (POC) which offer the possibility of displaying high separation performance for challenging molecular gas separations relevant to natural gas purification, and olefin/paraffin separation. The proposed POCs synthesized in membrane form will display the most desirable properties of polymers (facile processability and flexibility) and inorganic materials (hierarchically ordered pores with molecular sieving properties) leading to highly selective and permeable membranes. POCs should display distinctive structural, compositional, adsorption and transport properties than those of conventional porous materials, opening the doors for a new research direction in membrane science, and gas separations. Our preliminary results demonstrate the feasibility of preparing POC crystals with controlled size, and continuous POC membranes with remarkable high permeances, and separation ability for CO 2 /CH 4 , N 2 /CH 4 and C 3 H 6 /C 3 H 8 separations serving as a solid foundation for our proposed work. Fundamentally, this proposal aims at elucidating separation mechanisms of different gas mixtures related to natural gas composition, and olefin/paraffin separation over porous organic cage membranes. The proposed research will result in fundamental understanding of adsorption and transport properties of industrially relevant gas molecules through novel microporous membranes, and may lead to the development of a cost effective membrane technology for natural gas purification, and olefin/paraffin separation surpassing the conventional benchmark technology distillation. Furthermore, we aim at demonstrating selective water transport through POC membranes, which can be positively impactful in numerous industrial applications in which water is present. The ability to fabricate thin, chemically and mechanically stable POC membranes for societal relevant gas separations constitute a new and distinctive direction in membrane science. Our proposed work aims at addressing some of the challenges recognized in the Research Agenda for Transforming Separation Science . Specifically: (a) advancing understanding of complex mixtures on separation performance; (b) exploring thermodynamic and kinetic mechanisms through the elucidation of separation mechanisms, and (c) study potential stability issues of the membranes to be assessed by evaluating the long term membrane stability and performance at various temperatures and pressures. The team is uniquely qualified to execute the proposed work. The PI has solid expertise in the rational molecular engineering design of porous crystalline membranes for molecular gas separations. The PNNL collaborator has extensive experience in the synthesis, characterization, and functional applications of microporous crystals, with particular emphasis on gas adsorption.
Scalable and Quench-Free Processing of Metal Halide Perovskites in Ambient Conditions
With the rise of global warming and the growing energy crisis, scientists have pivoted from typical resources to look for new materials and technologies. Perovskite materials hold the potential for making high-efficiency, low-cost solar cells through solution processing of Earth-abundant materials; however, scalability, stability, and durability remain key challenges. In order to transition from small-scale processing in inert environments to higher throughput processing in ambient conditions, the fundamentals of perovskite crystallization must be understood. Classical nucleation theory, the LaMer relation, and nonclassical crystallization considerations are discussed to provide a mechanism by which a gellan gum (GG) additive—a nontoxic polymeric saccharide—has enabled researchers to produce quality halide perovskite thin-film blade coated in ambient conditions without a quench step. Furthermore, we report on the improved stability and durability properties inherent to these films, which feature improved morphologies and optoelectronic properties compared to films spin-coated in a glovebox with antisolvent. We tune the amount of GG in the perovskite precursor and study the interplay between GG concentration and processability, morphological control, and increased stability under humidity, heat, and mechanical testing. The simplicity of this approach and insensitivity to environmental conditions enable a wide process window for the production of low-defect, mechanically robust, and operationally stable perovskites with fracture energies among the highest obtained for perovskites.