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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Novel Ultrabright and Air-Stable Photocathodes Discovered from Machine Learning and Density Functional Theory Driven Screening

The high brightness, low emittance electron beams achieved in modern X-ray free-electron lasers (XFELs) have enabled powerful X-ray imaging tools, allowing molecular systems to be imaged at picosecond time scales and sub-nanometer length scales. One of the most promising directions for increasing the brightness of XFELs is through the development of novel photocathode materials. Whereas past efforts aimed at discovering photocathode materials have typically employed trial-and-error-based iterative approaches, this work represents the first data-driven screening for high brightness photocathode materials. Through screening over 74 000 semiconducting materials, a vast photocathode dataset is generated, resulting in statistically meaningful insights into the nature of high brightness photocathode materials. This screening results in a diverse list of photocathode materials that exhibit intrinsic emittances that are up to 4x lower than currently used photocathodes. In a second effort, multiobjective screening is employed to identify the family of M 2 O (M = Na, K, Rb) that exhibits photoemission properties that are comparable to the current state-of-the-art photocathode materials, but with superior air stability. This family represents perhaps the first intrinsically bright, visible light photocathode materials that are resistant to reactions with oxygen, allowing for their transport and storage in dry air environments.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Multi-catalytic active site biochar-based catalysts for glucose isomerized to fructose: Experiments and density functional theory study

In this study, this work provides an innovative method for preparing different isomerization catalysts by impregnating different proportions of MgCl 2 and AlCl 3 and combining different K compounds on cellulose-derived biochar, followed by pyrolysis. Results show MgO and Al(OH) 3 existing in 4 Mg- 1 Al-C catalyst can obtain better catalytic effect on glucose isomerization than the singe of Al presenting in 0 Mg- 1 Al-C catalyst. Moreover, the synergism effects of the multi-catalytic active sites such as β-, γ -Al(OH) 3 , KCl, MgO, and K 4 H 2 (CO 3 ) 3 in Mg-Al-KHCO 3 -C catalyst can further lead to an increase in glucose isomerization, compared to the 4 Mg- 1 Al-C catalyst. The X-ray diffraction results present that the value of O/Al in Mg-Al-KHCO 3 -C catalyst is as high as 13.38, which provides many unsaturated acidic catalysis sites and benefits the glucose isomerization. Simultaneously, the TPD results reveal that the main active sites (MgO, Al(OH) 3 , and K 4 H 2 (CO 3 ) 3 ) in Mg-Al-KHCO 3 -C catalyst can provide weakly acidic and basic sites and avoid strongly acidic and basic sites to excessively attack the glucose. Based on the DFT analysis, the results indicate that the MgO has a great effect on the ring-opening reaction to form acyclic glucose, while Al(OH) 3+ has a great effect on promoting acyclic glucose hydrogen transfer isomerized to form fructose. Compared to other carbon-based metal catalysts, the prepared Mg-Al-KHCO 3 -C has excellent catalytic performance, which gives a higher fructose yield (38.7%) and selectivity (87.72%) and glucose conversion (44.12%) at 100 °C in 30 min. In this study, we develop a highly efficient Mg-Al-K-biochar catalyst for glucose isomerization and provide an efficient method for cellulose valorization.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A machine learning approach for increased throughput of density functional theory substitutional alloy studies

In this study, a machine learning-based technique is developed to reduce the computational cost required to explore large design spaces of substitutional alloys. The first advancement is based on a neural network (NN) approach to predict the initial position of minority and majority ions prior to DFT relaxation. The second advancement is to allow the NN to predict the total energy for every possibility minority ion position and select the most stable configuration in the absence of relaxing each trial minority configuration. A bismuth oxide materials system, (Bi x La y Yb z ) 2 MoO 6 , was used as the model system to demonstrate the developed methods and quantify the resulting computational speedup. Compared to a brute force method that requires the calculation of every permutation of minority configuration and subsequent DFT relaxation, a 1.3× speedup was realized if the NN predicted the initial configuration of ions prior to relaxation. Implementation of the second advancement allowed the NN to predict the total energy for all possible trial configurations and downselect the most stable configurations prior to relaxation, resulting in a speedup of approximately 37×. Validation was done by comparing position and energy between the NN and DFT predictions. A maximum position vector mean squared error (MSE) of 1.6 × 10 -2 and a maximum energy MSE of 2.3 × 10 -7 was predicted for the worst case configuration. This method demonstrates a significant computational speedup, which has the potential for even greater computational savings for larger compositional design spaces.

36 MATERIALS SCIENCE↗