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Results for “fine tuning”

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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Finely tuning the microporosity in phosphoric acid doped triptycene-containing polybenzimidazole membranes for highly permselective helium and hydrogen recovery

High-performance polymer membranes with well-defined microporosity and size-sieving ability are especially attractive for helium and hydrogen recovery. Here, in this study, we report novel macromolecular engineering of polybenzimidazole (PBI) membranes that integrate hierarchical triptycene units for high permeability and polyprotic acid doping for size sieving via controllable manipulation of microporous architecture. The triptycene moieties disrupt chain packing and introduce additional configurational free volume, leading to significantly boosted He and H 2 permeabilities compared to previously reported PBI membranes. The acid doping resulted in crosslinked PBI membranes via hydrogen bonding and proton transfer with dramatically enhanced gas selectivities. Via adjusting the H 3 PO 4 -doping level, triptycene-based polybenzimidazole (TPBI) composite membranes (TPBI-(H 3 PO 4 ) x ) exhibit the highest gas selectivities for He enrichment (i.e., α(He/CH 4 ) = 7052 ± 156) and H 2 purification (i.e., α(H 2 /CH 4 ) = 5128 ± 110) among existing polymeric gas separation membranes. Additionally, under mixed-gas conditions at 150 °C, the TPBI-(H 3 PO 4 ) 0.98 membrane displays a H 2 permeability of 46.7 Barrer and a H 2 /CO 2 selectivity of 16, far beyond the Robeson's 2008 upper bound for H 2 /CO 2 separation. The facile and diverse tunability and excellent gas separation performance make TPBI-(H 3 PO 4 ) x membranes highly attractive for helium and hydrogen separation.

42 ENGINEERING↗
DOE OSTI2021

Synergy between Photoluminescence and Charge Transport Achieved by Finely Tuning Polymeric Backbones for Efficient Light-Emitting Transistor

The lack of design principle for developing high-performance polymer materials displaying strong fluorescence and high ambipolar charge mobilities limited their performance in organic light-emitting transistors (OLETs), electrically pumped organic laser, and other advanced electronic devices. Here, a series of semiladder polymers by copolymerization of weak acceptors (TPTQ or TPTI) and weak donors (fluorene (F) or carbazole (C)) have been developed for luminescent and charge transporting properties. It was found that enhanced planarity, high crystallinity, and a delicate balance in interchain aggregation obtained in the new copolymer, TPTQ-F, contributed to high ambipolar charge mobilities and photoluminescent quantum yield. TPTQ-F showed excellent performance in solution-processed multilayered OLET devices with an external quantum efficiency (EQE) of 5.3%.

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

Robust control of the minima of high-order harmonics by fine-tuning the alignment of CO 2 molecules for shaping attosecond pulses and probing molecular alignment

In a recent paper, we reported that the position and depth of the minima in the harmonic spectra of CO 2 molecules can be dramatically manipulated by a slight change of the degree of alignment, thus providing a convenient method of shaping attosecond pulses which could lead to the splitting of a typical single attosecond burst into two. Here, we demonstrate that harmonic minima can also be dramatically controlled by changing the pump-probe angles to shape attosecond pulses. In the meanwhile, since the pump-probe angle-dependent harmonic spectra are very sensitive to the degree of alignment, harmonic spectra can also be used to calibrate the alignment distribution of molecules. In this work we identify such robust control of harmonic generation is due to the optical property of CO 2 and the coherent harmonic emission from the oriented molecules. The photoionization transition dipoles (PITDs) of CO 2 exhibit deep minima that change rapidly with molecular orientations accompanied by large phase changes of π in PITDs, thus leading to strong interference when the dipoles are added up coherently.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2021

Salt Stress Induces Paramylon Accumulation and Fine-Tuning of the Macro-Organization of Thylakoid Membranes in Euglena gracilis Cells

The effects of salt stress condition on the growth, morphology, photosynthetic performance, and paramylon content were examined in the mixotrophic, unicellular, flagellate Euglena gracilis. We found that salt stress negatively influenced cell growth, accompanied by a decrease in chlorophyll (Chl) content. Circular dichroism (CD) spectroscopy revealed the changes in the macro-organization of pigment-protein complexes due to salt treatment, while the small-angle neutron scattering (SANS) investigations suggested a reduction in the thylakoid stacking, an effect confirmed by the transmission electron microscopy (TEM). At the same time, the analysis of the thylakoid membrane complexes using native-polyacrylamide gel electrophoresis (PAGE) revealed no significant change in the composition of supercomplexes of the photosynthetic apparatus. Salt stress did not substantially affect the photosynthetic activity, as reflected by the fact that Chl fluorescence yield, electron transport rate (ETR), and energy transfer between the photosystems did not change considerably in the salt-grown cells. We have observed notable increases in the carotenoid-to-Chl ratio and the accumulation of paramylon in the salt-treated cells. We propose that the accumulation of storage polysaccharides and changes in the pigment composition and thylakoid membrane organization help the adaptation of E. gracilis cells to salt stress and contribute to the maintenance of cellular processes under stress conditions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Enhancing ChatPORT with CUDA-to-SYCL Kernel Translation Capability

Large Language Models (LLMs) have shown strong capabilities in general code translation. However, code translation involving parallel programming models remains largely unexplored. This work enhances the capabilities of code LLMs in CUDA-to-SYCL kernel translation with parameter-efficient fine-tuning. The resultant fine-tuned LLM, called ChatPORT, is an effort to provide high-fidelity translations from one programming model to another. We describe the preparation of datasets from heterogeneous computing benchmarks for model fine-tuning and testing, the parameter-efficient fine-tuning of 19 open-source code models ranging in size from 0.5 to 34 billion parameters and evaluate the correctness rates of the SYCL kernels by the fine-tuned models. The experimental results show that most code models fail to translate CUDA codes to SYCL correctly. However, fine-tuning these models using a small set of CUDA and SYCL kernels can enhance the capabilities of these models in kernel translation. Depending on the sizes of the models, the correctness rate ranges from 19.9% to 81.7% for a test dataset of 62 CUDA kernels.

Jin, Zheming [ORNL] (ORCID:000000027197780X)↗
DOE OSTI2022

Practical galaxy morphology tools from deep supervised representation learning

Astronomers have typically set out to solve supervised machine learning problems by creating their own representations from scratch. We show that deep learning models trained to answer every Galaxy Zoo DECaLS question learn meaningful semantic representations of galaxies that are useful for new tasks on which the models were never trained. We exploit these representations to outperform several recent approaches at practical tasks crucial for investigating large galaxy samples. The first task is identifying galaxies of similar morphology to a query galaxy. Given a single galaxy assigned a free text tag by humans (e.g. ‘#diffuse’), we can find galaxies matching that tag for most tags. The second task is identifying the most interesting anomalies to a particular researcher. Our approach is 100 per cent accurate at identifying the most interesting 100 anomalies (as judged by Galaxy Zoo 2 volunteers). The third task is adapting a model to solve a new task using only a small number of newly labelled galaxies. Models fine-tuned from our representation are better able to identify ring galaxies than models fine-tuned from terrestrial images (ImageNet) or trained from scratch. We solve each task with very few new labels; either one (for the similarity search) or several hundred (for anomaly detection or fine-tuning). This challenges the longstanding view that deep supervised methods require new large labelled data sets for practical use in astronomy. To help the community benefit from our pretrained models, we release our fine-tuning code zoobot. Zoobot is accessible to researchers with no prior experience in deep learning.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Sr(Ag 1− x Li x ) 2 Se 2 and [Sr 3 Se 2 ][(Ag 1− x Li x ) 2 Se 2 ] Tunable Direct Band Gap Semiconductors

Abstract Synthesizing solids in molten fluxes enables the rapid diffusion of soluble species at temperatures lower than in solid‐state reactions, leading to crystal formation of kinetically stable compounds. In this study, we demonstrate the effectiveness of mixed hydroxide and halide fluxes in synthesizing complex Sr/Ag/Se in mixed LiOH/LiCl. We have accessed a series of two‐dimensional Sr(Ag 1− x Li x ) 2 Se 2 layered phases. With increased LiOH/LiCl ratio or reaction temperature, Li partially substituted Ag to form solid solutions of Sr(Ag 1− x Li x ) 2 Se 2 with x up to 0.45. In addition, a new type of intergrowth compound [Sr 3 Se 2 ][(Ag 1− x Li x ) 2 Se 2 ] was synthesized upon further reaction of Sr(Ag 1− x Li x ) 2 Se 2 with SrSe. Both Sr(Ag 1− x Li x ) 2 Se 2 and [Sr 3 Se 2 ][(Ag 1− x Li x ) 2 Se 2 ] exhibit a direct band gap, which increases with increasing Li substitution ( x ). Therefore, the band gap of Sr(Ag 1− x Li x ) 2 Se 2 can be precisely tuned via fine‐tuning x that is controlled by only the flux ratio and temperature.

Zhou, Xiuquan↗
DOE OSTI2021

$R^2$/Higgs inflation and the hierarchy problem

We analyse Starobinsky inflation in the presence of the Brout Englert Higgs (BEH) boson with a non-minimal coupling to the Ricci scalar, $R$. The latter induces a coupling of the massive scaleron associated with the $R^2$ term to the BEH boson and this leads to a radiative correction to the BEH mass that must be fine tuned to keep the scalar light. For the case of $R^2$ driven inflation this requires a high level of fine tuning of order 1 part in $10^{8}$; for the case of Higgs inflation it is very much greater. We consider a scale invariant extension of the $R^2$/Higgs model and find that for $R^2$ driven inflation but not for Higgs inflation the required fine tuning is significantly reduced to one part in $10^{3-4}$. We consider the vacuum stability of the fine tuned model and its reheating and dilaton abundance after inflation. We also discuss possible gravitational wave signals associated with the model and the constraint on the mass of scalar or fermion dark matter candidates if they are produced by the gravitational couplings of the scalaron.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

HydraGNN_OPF_GFM_2026 - Ensemble of predictive graph foundation models for power grid applications

This dataset supports research on graph foundation models for optimal power flow (OPF) on electric grids using HydraGNN. It contains heterogeneous graph representations of PGLib-OPF cases spanning systems from 14 to 13,659 buses, together with packed HDF5 datasets for pretraining, feasibility classification, and N-1 contingency analysis. The release includes OPF solution data, downstream fine-tuning datasets, pretrained HeteroSAGE and HeteroHEAT model checkpoints, hyperparameter-optimization summaries across multiple heterogeneous GNN architectures, and aggregated fine-tuning results for sample-efficiency studies. The dataset is designed to enable scalable training, evaluation, and transfer-learning studies for OPF surrogate modeling, including node-level AC-OPF solution prediction, graph-level prediction, feasibility classification, operating-condition generalization, and contingency-response tasks.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Autogenerating a Domain-Specific Question-Answering Data Set from a Thermoelectric Materials Database to Enable High-Performing BERT Models

We present a method for autogenerating a large domain-specific question-answering (QA) dataset from a thermoelectric materials database. We show that a small language model, BERT, once fine-tuned on this automatically generated dataset of 99,757 QA pairs about thermoelectric materials, affords better performance in the field of thermoelectric materials compared to a BERT model fine-tuned on the generic English-language QA data set, SQuAD-v2. We further show that mixing the two data sets (ours and SQuAD-v2), which have significantly different syntactic and semantic scopes, allows the BERT model to achieve even better performance. The best-performing BERT model fine-tuned on the mixed data set outperforms the models fine-tuned on the other two data sets by scoring an exact match of 67.93% and an F1 score of 72.29% when evaluated on our test data set. This has important implications as it demonstrates the ability to realize high-performing small language models, with modest computational resources, empowered by domain-specific materials data sets which can be generated according to our method.

biological databases↗
DOE OSTI2023

A Modified Sequence-to-point HVAC Load Disaggregation Algorithm

This paper presents a modified sequence-to-point (S2P) algorithm for disaggregating the heat, ventilation, and air conditioning (HVAC) load from the total building electricity consumption. The original S2P model is convolutional neural network (CNN) based, which uses load profiles as inputs. We propose three modifications. First, the input convolution layer is changed from 1D to 2D so that normalized temperature profiles are also used inputs to the S2P model. Second, a drop-out layer is added to improve adaptability and generalizability so that the model trained in one area can be transferred to other geographical areas without labelled HVAC data. Third, a fine-tuning process is proposed for areas with a small amount of labelled HVAC data so that the pre-trained S2P model can be fine-tuned to achieve higher disaggregation accuracy (i.e., better transferability) in other areas. The model is first trained and tested using smart meter and sub-metered HVAC data collected in Austin, Texas. Then, the trained model is tested on two other areas: Boulder, Colorado and San Diego, California. Simulation results show that the proposed modified S2P algorithm outperforms the original S2P model and the support-vector machine based approach in accuracy, adaptability, and transferability.

Ye, Kai↗