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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 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Accurate Prediction of HSE06 Band Structures for a Diverse Set of Materials Using Δ-Learning

Here we used machine learning (ML) to accurately predict eigenvalues of the hybrid HSE06 functional using eigenvalues computed by the less computationally expensive PBE functional and associated electronic features based on the k-point resolved atomic band character. The ML model was trained by using eigenvalues from only one k-point for each of the 168 compounds in the training set. The HSE06 eigenvalues across all k-points were then predicted for a separate set of 169 compounds with a mean absolute error (MAE) of 0.13 eV, representing a significant improvement over the error of PBE-computed eigenvalues relative to that of HSE06 (MAE = 0.96 eV). These accurately predicted eigenvalues result in remarkably accurate predictions for the band structures, projected density of states, and band gaps, even though the model was not explicitly trained on these other properties. Finally, we demonstrate that our ML model has a similar accuracy for both ternary and quaternary compounds well outside the initial training set and on systems with 112 and 160 atoms, demonstrating its potential to rapidly predict HSE06-quality electronic structures of complex materials that are practically unfeasible for HSE06.

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

NiAl–MoO 2 S 2 Nanoparticles: Structural Evolution and Mechanistic Insights into High-Performance Selenium Oxyanion Removal across Diverse pH Conditions

Advancing sorbent materials for the selective removal of toxic oxyanions from water requires synthetic control, tunable chemistry, and an atomic-level understanding of structure–function relationships. Here, we report the synthesis and detailed characterization of NiAl–MoO 2 S 2 , a novel layered double hydroxide (LDH) nanomaterial designed for the efficient sequestration of selenium oxoanions (SeO 3 2– and SeO 4 2– ) from complex aqueous environments. The material is synthesized through a room-temperature ion-exchange process, wherein interlayer NO 3 – anions in NiAl–LDH are replaced with MoO 2 S 2 2– clusters, forming high-surface-area, flower-like nanoparticles. Comprehensive structural analysis using the synchrotron X-ray pair distribution function, X-ray absorption spectroscopy, and X-ray photoelectron spectroscopy reveals a distinct chemical transformation of intercalated [MoO 2 S 2 ] 2– into [Mo 2 O 2 S 6 ] 2– -like clusters, generating redox-active interlayers that drive selenium capture. This tailored interfacial chemistry underpins the material’s exceptional sorption performance, achieving distribution coefficients (K d ) ≥ 10 6 mL/g and maximum capacities of 343 mg/g for SeO 4 2– and 514 mg/g for SeO 3 2– , outperforming state-of-the-art inorganic sorbents. Importantly, NiAl–MoO 2 S 2 maintains high selectivity and capacity across acidic, neutral, and alkaline pH, efficiently removing selenium from ppm to sub-10 ppb trace levels, even in the presence of competing ions typical of natural and industrial waters. The selenium uptake proceeds via reductive precipitation coupled with the oxidation of molybdenum and sulfide within the LDH framework. This study highlights the power of strategic synthetic modification and interlayer functionalization in LDHs to unlock new structural motifs and redox chemistries, offering a scalable route to advanced materials for environmental remediation.

Adsorption↗
DOE OSTI2025

Chemical Diversity of Oligomers in Biomass Fast Pyrolysis Oils, Part 2: Heavy Lignin-Derived Molecules and Highly Dehydrated Sugars from Dichloromethane-Insoluble Pyrolytic Lignin

Here, this paper, the second in a series, investigates the water-insoluble, dichloromethane (DCM)-insoluble residue of BTG pyrolysis oil, also known in the literature as high-molecular-weight pyrolytic lignin (HMW-PL). HMW-PL accounts for 9.2 wt % of the original bio-oil, which is known to increase upon aging and contribute to coke formation during bio-oil upgrading. Understanding its composition is crucial for managing pyrolysis oil during the processing. The HMW-PL was fractionated using a silica gel column with solvents of increasing polarity: ethyl acetate (EA), acetone (AC), isopropanol (ISO), and methanol (MeOH), yielding 49.2, 19.9, 8.2, and 8.1 wt %, respectively. Each subfraction was characterized by UV-fluorescence, FTIR, HSQC NMR, and (−)APCI-FT-Orbitrap MS. Analysis revealed that the EA subfraction primarily contained aromatic dimers (C 19 –C 20 ) and trimers (C 17 –C 13 ), while the MeOH subfraction was rich in oxygenated aliphatic monomers (C 8 –C 16 ) likely derived from sugar dehydration products. The resulting fractions were further separated via preparatory HPLC. Fifty-five candidate molecular structures were proposed based on the Orbitrap MS data, supported by UV-fluorescence, FTIR, and NMR results. This fractionation strategy defined four distinct subfractions, enabling the proposal of surrogate molecular structures and advancing the molecular-level understanding of the HMW-PL fraction in the pyrolysis bio-oil.

High-Molecular-Weight PL↗
DOE OSTI2024

Site Suitability and Air Pollution Impacts of Composting Infrastructure for California’s Organic Waste Diversion Law

The emerging field of polaritonic chemistry explores the behavior of molecules under strong coupling with cavity modes. Despite recent developments in ab initio polaritonic methods for simulating polaritonic chemistry under electronic strong coupling, their capabilities are limited, especially in cases where the molecule also features strong electronic correlation. To bridge this gap, we have developed a novel method for cavity QED calculations utilizing the Density Matrix Renormalization Group (DMRG) algorithm in conjunction with the Pauli–Fierz Hamiltonian. Our approach is applied to investigate the effect of the cavity on the S0–S1 transition of n-oligoacenes, with n ranging from 2 to 5, encompassing 22 fully correlated π orbitals in the largest pentacene molecule. Our findings indicate that the influence of the cavity intensifies with larger acenes. Additionally, we demonstrate that, unlike the full determinantal representation, DMRG efficiently optimizes and eliminates excess photonic degrees of freedom, resulting in an asymptotically constant computational cost as the photonic basis increases.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Structural Diversity and Tunable Emission in Hybrid Organic–Inorganic Copper(I) Bromides

Recently, hybrid organic−inorganic copper(I) metal halides have attracted global attention due to their intriguing optical properties and low-cost solution processability. In this work, we report three hybrid organic−inorganic copper(I) bromides, [TMPA] 2 [Cu 2 Br 4 ], [TMPA] 4 [Cu 6 Br 10 ], and [TMPA] 2 [Cu 4 Br 6 ], synthesized through a slow evaporation method using trimethylphenylammonium (TMPA + ) as the organic cation. By precise control of the CuBr and TMPABr precursors, different copper halide [Cu 2 Br 4 ] 2− , [Cu 6 Br 10 ] 4− , and [Cu 4 Br 6 ] 2− structural units can be obtained. [TMPA] 2 [Cu 2 Br 4 ], [TMPA] 4 [Cu 6 Br 10 ], and [TMPA] 2 [Cu 4 Br 6 ] demonstrate distinct blue, orange, and greenish-yellow light emission, respectively. The first two compounds have zero-dimensional (0D) crystal structures in centrosymmetric triclinic space group P-1 and monoclinic space group P2 1 /n. In contrast, [TMPA] 2 [Cu 4 Br 6 ] features a unique one-dimensional (1D) structure and crystallizes in the centrosymmetric monoclinic space group P2 1 /c. Consequently, the observed greenish-yellow emission of [TMPA] 2 [Cu 4 Br 6 ] is also unique, in contrast to the typical orange-red emission of 0D [Cu 4 Br 6 ]-based compounds. This work provides insights into the design of copper halide light emitters and emphasizes the influence of structural dimensionality on photoluminescence. The tunable optical properties suggest the potential of these materials for multicolor photopatterning, information encryption, and anticounterfeiting applications.

Anions↗
DOE OSTI2023

Two-Dimensional Energy Histograms as Features for Machine Learning to Predict Adsorption in Diverse Nanoporous Materials

A major obstacle for machine learning (ML) in chemical science is the lack of physically informed feature representations that provide both accurate prediction and easy interpretability of the ML model. In this work, we describe adsorption systems using novel two-dimensional energy histogram (2D-EH) features, which are obtained from the probe-adsorbent energies and energy gradients at grid points located throughout the adsorbent. The 2D-EH features encode both energetic and structural information of the material and lead to highly accurate ML models (coefficient of determination R2 ~ 0.94–0.99) for predicting single-component adsorption capacity in metal–organic frameworks (MOFs). Here, we consider the adsorption of spherical molecules (Kr and Xe), linear alkanes with a wide range of aspect ratios (ethane, propane, n-butane, and n-hexane), and a branched alkane (2,2-dimethylbutane) over a wide range of temperatures and pressures. The interpretable 2D-EH features enable the ML model to learn the basic physics of adsorption in pores from the training data. We show that these MOF-data-trained ML models are transferrable to different families of amorphous nanoporous materials. We also identify several adsorption systems where capillary condensation occurs, and ML predictions are more challenging. Nevertheless, our 2D-EH features still outperform structural features including those derived from persistent homology. The novel 2D-EH features may help accelerate the discovery and design of advanced nanoporous materials using ML for gas storage and separation in the future.

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

Gold-Standard Chemical Database 137 (GSCDB137): A Diverse Set of Accurate Energy Differences for Assessing and Developing Density Functionals

We present GSCDB137, a rigorously curated benchmark library of 137 data sets (8377 entries) covering main-group and transition-metal reaction energies and barrier heights, (intra- and intermolecular) noncovalent interactions, dipole moments, polarizabilities, electric-field response energies, and vibrational frequencies. Legacy data from GMTKN55 and MGCDB84 have been updated to today's best reference values; redundant or low-quality points were removed, and many new, property-focused sets were added. Testing 29 popular density functional approximations (DFAs) confirms the expected Jacob's-ladder hierarchy overall but also reveals notable exceptions: functional performance for frequencies and electric-field properties correlates poorly with that for other ground-state energetics. ωB97M-V and ωB97X-V are the most balanced hybrid meta-GGA and hybrid GGA, respectively; B97M-V and revPBE-D4 lead the meta-GGA and GGA classes. Double hybrids lower mean errors by about 30% versus their hybrid analogues but demand careful frozen-core, basis set, and spin contamination treatment. GSCDB137 offers a comprehensive, openly documented platform for rigorous validation of DFA and universal machine learning potentials, and training of the next generation of exchange-correlation functionals.

Liang, Jiashu [University of California, Berkeley,↗