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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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Section 6 of 8 sections comprising a bibliography on reactor fuel reprocessing and waste disposal is presented. The complete collection includes about 7000 abstracts, most of which were obtained from Nuclear Science Abstracts. Most of the material dates from the 1955 Geneva Conference to the present.
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Single crystals of an organic semiconductor undergo surface co-crystallization with phenol vapors, producing color and luminescence changes. The process is reversible, enabling solid-state sensing through hydrogen bonding at the crystal surface.
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Single photon quantum materials discovery based on large dataset synthetic data generation.
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The multi-scale features and latent space are connected by a nested autoencoder.
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PAL 2.0 provides an efficient discovery tool for advanced functional materials, ameliorating a major bottleneck to enabling advances in next-generation energy, health, and sustainability technologies.
Long-term space operations that require exposure of material to the low earth orbit (LEO) environment must take into account the effects of this highly oxidative atmosphere on material properties and the possible contamination of the spacecraft surroundings. Ground-based laboratory experiments at Los Alamos using a newly developed hyperthermal atomic oxygen (AO) source have shown that not only are hydrocarbon based materials effected but that inorganic materials such as MoS2 are also oxidized and that thin protective coatings such as Al2O3 can be breached, producing oxidation of the underlying substrate material. Gas-phase reaction products, such as SO2 from oxidation of MoS2 and CO and CO2 from hydrocarbon materials, have been detected and have consequences in terms of spacecraft contamination. Energy loss through gas-surface collisions causing spacecraft drag has been measured for a few select surfaces and has been found to be highly dependent on the surface reactivity.
Computational chemistry is an indispensable tool for understanding molecules and predicting chemical properties. However, traditional computational methods face significant challenges due to the difficulty of solving the Schrödinger equations and the increasing computational cost with the size of the molecular system. In response, there has been a surge of interest in leveraging artificial intelligence (AI) and machine learning (ML) techniques to in silico experiments. Integrating AI and ML into computational chemistry increases the scalability and speed of the exploration of chemical space. However, challenges remain, particularly regarding the reproducibility and transferability of ML models. This review highlights the evolution of ML in learning from, complementing, or replacing traditional computational chemistry for energy and property predictions. Starting from models trained entirely on numerical data, a journey set forth toward the ideal model incorporating or learning the physical laws of quantum mechanics. This paper also reviews existing computational methods and ML models and their intertwining, outlines a roadmap for future research, and identifies areas for improvement and innovation. Ultimately, the goal is to develop AI architectures capable of predicting accurate and transferable solutions to the Schrödinger equation, thereby revolutionizing in silico experiments within chemistry and materials science.
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Optimizedviamachine learning, extrusion printed thermoelectric materials (BiSbTe) achieve an ultrahighzTof 1.3 at room temperature.
A novel Bayesian approach significantly accelerates data collection for metal oxide reduction/re-oxidation thermodynamic fitting.