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DOE OSTI · 2575084

Curiosity driven exploration to optimize structure–property learning in microscopy

Vatsavai, Aditya [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); University of North Carolina, Chapel Hill, NC (United States)]·Narasimha, Ganesh [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)] (ORCID:0000000331654050)·Liu, Yongtao [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)] (ORCID:0000000301521783)·Chowdhury, Jawad [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)]·Yang, Jan-Chi [National Cheng Kung Univ., Tainan City (Taiwan)] (ORCID:0000000235494392)·Funakubo, Hiroshi [Institute of Science Tokyo, Yokohama (Japan)]·Ziatdinov, Maxim A. [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)] (ORCID:0000000325704592)·Vasudevan, Rama [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)] (ORCID:0000000346928579)
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Abstract

Rapidly determining structure–property correlations in materials is an important challenge in better understanding fundamental mechanisms and greatly assists in materials design. In microscopy, imaging data provides a direct measurement of the local structure, while spectroscopic measurements provide relevant functional property information. Deep kernel active learning approaches have been utilized to rapidly map local structure to functional properties in microscopy experiments, but are computationally expensive for multi-dimensional and correlated output spaces. Here, we present an alternative lightweight curiosity algorithm which actively samples regions with unexplored structure–property relations, utilizing a deep-learning based surrogate model for error prediction. We show that the algorithm outperforms random sampling for predicting properties from structures, and provides a convenient tool for efficient mapping of structure–property relationships in materials science.

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36 MATERIALS SCIENCE

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Vatsavai, Aditya [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); University of North Carolina, Chapel Hill, NC (United States)], Narasimha, Ganesh [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)] (ORCID:0000000331654050), Liu, Yongtao [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)] (ORCID:0000000301521783), Chowdhury, Jawad [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)], Yang, Jan-Chi [National Cheng Kung Univ., Tainan City (Taiwan)] (ORCID:0000000235494392), Funakubo, Hiroshi [Institute of Science Tokyo, Yokohama (Japan)], Ziatdinov, Maxim A. [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)] (ORCID:0000000325704592), Vasudevan, Rama [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)] (ORCID:0000000346928579). 2025-07-10. Curiosity driven exploration to optimize structure–property learning in microscopy. https://doi.org/10.1039/d5dd00119f

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DOE OSTI2026

Cyclic moisture reactivation of calcium sorbents for long duration thermochemical energy storage

The transition to a flexible and reliable energy infrastructure, using electro-thermal energy generation technologies such as geothermal, concentrated solar power, and nuclear, usually demands simultaneous advancement of thermal energy storage (TES) to support on-demand electricity generation and industrial applications while mitigating the inherent intermittency of renewable energy sources and power outages from direct energy generation. Among TES technologies, thermochemical energy storage (TCES) based on calcium looping emerges as a compelling high-power energy storage candidate due to its high reaction enthalpy, compatibility with elevated operating temperatures, and abundance of low-cost materials. However, the long-term durability of calcium-based sorbents for TCES is hindered by surface sintering and particle aggregation, leading to performance degradation over repeated thermal cycles. This study explores a moisture hydration-based strategy to regenerate a degraded calcium sorbent and mitigate performance degradation for long duration TCES. The addition of moisture transforms calcium oxide into calcium hydroxide and produces intercalation water layers, associated with a regenerated surface area and reduced calcium oxide crystallite size. Both these effects are beneficial in restoring the sorbents' reactivity for carbonization. Additionally, an optimized hydration-assisted reactivation protocol balances the recovered energy storage capacity with heating penalty required for moisture removal from hydrated samples, resulting in an enhanced energy storage capacity up to 176% compared to benchmark sorbents that undergo cycling without reactivation after 60 cycles. In conclusion, these results highlight the potential of hydration-assisted reactivation to enhance the long-term performance of TCES, providing an effective pathway to advancing electro-thermal storage technologies.

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