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Abstract Volatile threshold resistive switching and neuronal oscillations in phase‐change materials, specifically those undergoing ‘metal‐to‐insulator’ transitions, offer unique attributes such as fast and low‐field volatile switching, tunability, and stochastic dynamics. These characteristics are particularly promising for emulating neuronal behaviors and solving complex computational problems. In this review, we summarize recent advances in the development of volatile resistive switching devices and neuronal oscillators based on three representative materials with coincident electronic and structural phase transitions, at different levels of technological readiness: the well‐studied correlated oxide VO2, the charge‐density‐wave transition metal dichalcogenide 1T‐TaS2, and the emerging phase‐change complex chalcogenide BaTiS3. We discuss progresses from the perspective of materials development and device implementation. Finally, we emphasize the major challenges that must be addressed for practical applications of these phase‐change materials and provides outlook on the future research directions in this rapidly evolving field.
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Featureless adaptive optimization accelerates functional electronic materials design
Electronic materials that exhibit phase transitions between metastable states (e.g., metal-insulator transition materials with abrupt electrical resistivity transformations) are challenging to decode. For these materials, conventional machine learning methods display limited predictive capability due to data scarcity and the absence of features that impede model training. In this article, we demonstrate a discovery strategy based on multi-objective Bayesian optimization to directly circumvent these bottlenecks by utilizing latent variable Gaussian processes combined with high-fidelity electronic structure calculations for validation in the chalcogenide lacunar spinel family. We directly and simultaneously learn phase stability and bandgap tunability from chemical composition alone to efficiently discover all superior compositions on the design Pareto front. Previously unidentified electronic transitions also emerge from our featureless adaptive optimization engine. Our methodology readily generalizes to optimization of multiple properties, enabling co-design of complex multifunctional materials, especially where prior data is sparse.
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- Award ID(s):
- 1729303
- PAR ID:
- 10588243
- Publisher / Repository:
- American Institute of Physics
- Date Published:
- Journal Name:
- Applied Physics Reviews
- Volume:
- 7
- Issue:
- 4
- ISSN:
- 1931-9401
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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