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The development of next-generation energy storage systems relies on discovering new materials that support multivalent-ion transport. Transition metal oxides (TMOs) are promising due to their structural versatility, high ionic conductivity, and ability to accommodate multiple charge carriers. However, their vast compositional and structural diversity makes traditional exploration inefficient. This work presents a generative AI framework combining a crystal diffusion variational autoencoder (CDVAE) and a fine-tuned large language model (LLM) to discover porous oxide materials. Thousands of candidate structures are generated and screened for structural validity, thermodynamic stability, and electronic properties using a graph-based machine learning model and density functional theory (DFT) calculations. CDVAE identifies a broader variety of structures, including five novel TMO-based candidates, while LLM excels in generating highly stable structures near equilibrium. This approach demonstrates the power of generative AI in accelerating the discovery of advanced battery materials for multivalent-ion storage.
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Influence of redox engineering on the trade-off relationship between thermopower and electrical conductivity in lanthanum titanium based transition metal oxides
Discovery of new materials plays a critical role in developing advanced high-temperature thermoelectric (TE) applications. Transition metal oxides (TMOs) are one of the attractive candidates for hightemperature TE applications due to their thermal and chemical stability. However, the trade-off relationship between thermopower (S) and electrical conductivity (s) limits the maximum attainable power factor (PF), thereby hindering improvements in TE conversion efficiency. To overcome this tradeoff relationship, the emerging approach of the redox-driven metal exsolution in TMOs shows promise in improving both S and s. However, the effect of metal exsolution with different particle sizes and densities on S and s is still largely unexplored. This study demonstrates an unusually large enhancement in PF through the exsolution of Ni nanoparticles in epitaxial La0.7Ca0.2Ni0.25Ti0.75O3 (LCNTO) thin films. Metal exsolution leads to a decrease in the carrier concentration while increasing the carrier mobility due to energy filtering effects. In addition, the exsolved metal particles introduce high-mobility electron carriers into the low-mobility LCNTO matrix. Consequently, the exsolution of metal particles results in a significant enhancement in S along with a substantial increase in s, compared to the pristine film. Overall, the TE power factor of LCNTO is dramatically enhanced by up to 8 orders of magnitude owing to the presence of exsolved metal particles. This enhancement is attributed to the selective filtering of carriers caused by energy band bending at the metal–oxide interfaces and the high-mobility carriers from the exsolved Ni particles with a high Ni0 fraction. This study unequivocally demonstrates the impact of metal exsolution on oxide TE properties and provides a novel route to tailor the interconnected physical and chemical properties of oxides, leading to enhanced TE power output.
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- Award ID(s):
- 2340234
- PAR ID:
- 10612745
- Editor(s):
- Hagfeldt, Anders; Winter, Jessica
- Publisher / Repository:
- The Royal Society of Chemistry
- Date Published:
- Journal Name:
- Materials Advances
- Volume:
- 5
- Issue:
- 22
- ISSN:
- 2633-5409
- Page Range / eLocation ID:
- 9007 to 9017
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1039/d4ma00829d
