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DOE OSTI · 3030137

Structural constraint integration in a generative model for the discovery of quantum materials

Okabe, Ryotaro [Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)] (ORCID:0000000250955951)·Cheng, Mouyang [Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)] (ORCID:0009000170142464)·Chotrattanapituk, Abhijatmedhi [Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)] (ORCID:0000000162781138)·Mandal, Manasi [Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)] (ORCID:0000000215419678)·Mak, Kiran [Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)] (ORCID:000900027556783X)·Córdova Carrizales, Denisse [Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)]·Hung, Nguyen Tuan [Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States); Tohoku Univ., Sendai (Japan)] (ORCID:0000000341566230)·Fu, Xiang [Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)] (ORCID:0000000174806312)·Han, Bowen [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)]·Wang, Yao [Emory Univ., Atlanta, GA (United States)] (ORCID:0000000317360187)·Xie, Weiwei [Michigan State Univ., East Lansing, MI (United States)] (ORCID:0000000255008195)·Cava, Robert J. [Princeton Univ., NJ (United States)]·Jaakkola, Tommi S. [Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)] (ORCID:0000000221990379)·Cheng, Yongqiang [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)] (ORCID:0000000232634812)·Li, Mingda [Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)] (ORCID:0000000270556368)
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Abstract

Billions of organic molecules have been computationally generated, yet functional inorganic materials remain scarce due to limited data and structural complexity. Here, in this work, we introduce Structural Constraint Integration in a GENerative model (SCIGEN), a framework that enforces geometric constraints, such as honeycomb and kagome lattices, within diffusion-based generative models to discover stable quantum materials candidates. SCIGEN enables conditional sampling from the original distribution, preserving output validity while guiding structural motifs. This approach generates ten million inorganic compounds with Archimedean and Lieb lattices, over 10% of which pass multistage stability screening. High-throughput density functional theory calculations on 26,000 candidates shows over 95% convergence and 53% structural stability. A graph neural network classifier detects magnetic ordering in 41% of relaxed structures. Furthermore, we synthesize and characterize two predicted materials, TiPd 0.22 Bi 0.88 and Ti 0.5 Pd 1.5 Sb, which display paramagnetic and diamagnetic behaviour, respectively. Our results indicate that SCIGEN provides a scalable path for generating quantum materials guided by lattice geometry.

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36 MATERIALS SCIENCEAImagnetic properties and materialsquantum materials

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Okabe, Ryotaro [Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)] (ORCID:0000000250955951), Cheng, Mouyang [Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)] (ORCID:0009000170142464), Chotrattanapituk, Abhijatmedhi [Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)] (ORCID:0000000162781138), Mandal, Manasi [Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)] (ORCID:0000000215419678), Mak, Kiran [Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)] (ORCID:000900027556783X), Córdova Carrizales, Denisse [Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)], Hung, Nguyen Tuan [Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States); Tohoku Univ., Sendai (Japan)] (ORCID:0000000341566230), Fu, Xiang [Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)] (ORCID:0000000174806312), Han, Bowen [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)], Wang, Yao [Emory Univ., Atlanta, GA (United States)] (ORCID:0000000317360187), Xie, Weiwei [Michigan State Univ., East Lansing, MI (United States)] (ORCID:0000000255008195), Cava, Robert J. [Princeton Univ., NJ (United States)], Jaakkola, Tommi S. [Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)] (ORCID:0000000221990379), Cheng, Yongqiang [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)] (ORCID:0000000232634812), Li, Mingda [Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)] (ORCID:0000000270556368). 2025-09-22. Structural constraint integration in a generative model for the discovery of quantum materials. https://doi.org/10.1038/s41563-025-02355-y

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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.

36 MATERIALS SCIENCE↗