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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Crystallization of TiO 2 polymorphs from RF-sputtered, amorphous thin-film precursors

Crystalline TiO 2 films of anatase, brookite, and rutile are reproducibly made from amorphous precursors deposited by RF magnetron sputtering, producing large-area, single phase films of uniform thickness. Sputtered amorphous TiO 2 precursor thin films follow the general behavior observed for amorphous precursor thin films generated by pulsed laser deposition, namely, that oxygen deficiency is necessary for the formation of brookite and rutile. We quantify the oxygen deficiency and correlate it with the long wavelength optical absorption. We find that the precursor deposition rate is also a contributing factor to phase selection and that brookite and rutile form from films deposited more rapidly and anatase from films deposited more slowly. Sputtered and pulsed laser deposited amorphous precursor films prepared with similar oxygen deficiency and similar thickness result in the same final state after annealing, but the rate for sputtered precursors is slower.

14 SOLAR ENERGY↗
DOE OSTI2025

Formation of distinctive nanostructured metastable polymorphs mediated by kinetic transition pathways in germanium

High-pressure β -Sn germanium may transform into diverse metastable allotropes with distinctive nanostructures and unique physical properties via multiple pathways under decompression. However, the mechanism and transition kinetics remain poorly understood. Here, we investigate the formation of metastable phases and nanostructures in germanium via controllable transition pathways of β -Sn Ge under rapid decompression at different rates. High-resolution transmission electron microscopy reveals three distinct metastable phases with the distinctive nanostructures: an almost perfect st12 Ge crystal, nanosized bc8/r8 structures with amorphous boundaries, and amorphous Ge with nanosized clusters (0.8–2.5 nm). Fast in situ x-ray diffraction and x-ray absorption measurements indicate that these nanostructured products form in certain pressure regions via distinct kinetic pathways and are strongly correlated with nucleation rates and electronic transitions mediated by compression rate, temperature, and stress. This work provides deep insight into the controllable synthesis of metastable materials with unique crystal symmetries and nanostructures for potential applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Explainable Synthesizability Prediction of Inorganic Crystal Polymorphs Using Large Language Models

Abstract We evaluate the ability of machine learning to predict whether a hypothetical crystal structure can be synthesized and explain those predictions to scientists. Fine‐tuned large language models (LLMs) trained on a human‐readable text description of the target crystal structure perform comparably to previous bespoke convolutional graph neural network methods, but better prediction quality can be achieved by training a positive‐unlabeled learning model on a text‐embedding representation of the structure. An LLM‐based workflow can then be used to generate human‐readable explanations for the types of factors governing synthesizability, extract the underlying physical rules, and assess the veracity of those rules. These explanations can guide chemists in modifying or optimizing non‐synthesizable hypothetical structures to make them more feasible for materials design.

Kim, Seongmin [Department of Chemical and Biologic↗
DOE OSTI2025

Explainable Synthesizability Prediction of Inorganic Crystal Polymorphs Using Large Language Models

Abstract We evaluate the ability of machine learning to predict whether a hypothetical crystal structure can be synthesized and explain those predictions to scientists. Fine‐tuned large language models (LLMs) trained on a human‐readable text description of the target crystal structure perform comparably to previous bespoke convolutional graph neural network methods, but better prediction quality can be achieved by training a positive‐unlabeled learning model on a text‐embedding representation of the structure. An LLM‐based workflow can then be used to generate human‐readable explanations for the types of factors governing synthesizability, extract the underlying physical rules, and assess the veracity of those rules. These explanations can guide chemists in modifying or optimizing non‐synthesizable hypothetical structures to make them more feasible for materials design.

Kim, Seongmin [Department of Chemical and Biologic↗
DOE OSTI2023

Photocatalytic degradation of orange II dye via high pore volume polymorph zirconium-doped titania under visible light irradiation

A series of mixed-phase Zr-doped TiO 2 was utilized for the photocatalytic degradation of orange II dye. These materials were synthesized through a sol–gel technique by varying the amounts of zirconium in the photocatalysts. These materials were characterized using X-ray diffraction (XRD), transmission electron microscopy (TEM), X-ray fluorescence (XRF), Raman spectroscopy, thermogravimetry analysis (TGA), and X-ray photoelectron spectroscopy (XPS) among others. XRF and XPS data showed that the elemental composition of the photocatalysts consists of zirconium, titanium, and oxygen. In addition, XRD and Raman spectroscopy confirmed the existence of anatase and rutile phases of TiO 2 . The TGA analyses showed that the undoped TiO 2 was the most stable, with no significant weight loss during the heating range. Furthermore, the materials can effectively degrade orange II dye solution up to 7 cycles through asymmetric cleavage of the azo bond.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Machine learning enabled discovery of superhard and ultrahard carbon polymorphs

The demand for multifunctional materials has motivated the move from near-equilibrium materials to metastable i.e. out-of-equilibrium phases that can meet several desired target properties. The search for such metastable phases with exotic properties is non-trivial and often serendipitous. Inverse design approaches based on evolutionary search have been powerful tools, but such traditional searches have focused on identifying primarily stable and metastable materials with the lowest enthalpy. The inverse design of materials, with a focus on a desired property such as, for example, hardness is a challenging task because of the expensive computational cost involved in sampling multiple structures. The recent advances in machine learning have brought new powerful AI techniques to the forefront which can potentially revolutionize the inverse design and discovery of materials, especially metastable phases capable of meeting multifunctionality. Here, in this work, we develop and apply an automated reinforcement learning workflow for inverse design that integrates first principles physics and atomistic simulations with machine learning (ML), and high-performance computing to allow rapid exploration of the superhard and ultrahard metastable phases of Carbon. We demonstrate an automatic machine learning based inverse design workflow to map new undiscovered metastable states ranging from near equilibrium to those far-from-equilibrium that satisfy multiple property objectives, specifically bulk moduli, shear moduli and hardness. We create a comprehensive library of carbon stable and metastable phases with varying hardness and subsequently shortlist 10 top performing candidate carbon structures, including two newly reported phases, based on their hardness and characterize their temperature dependent mechanical properties. A neural network model is built using featurization of allotropes of carbon to predict the quasi-harmonic Gibbs free energies. The Gibbs free energies of the top performing phases are analyzed to get an estimate of the experimental synthesizability of these superhard and ultrahard carbon phases. In general, we show using machine learning based inverse design approaches how hitherto inaccessible metastable states can be identified and potentially synthesized to meet the demand for multifunctional materials.

Balasubramanian, Karthik [Univ. of Illinois, Chica↗