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Toward Intelligent Sensing Systems: Non-Equilibrium Materials as Platforms for AI-Enabled Autonomous Discovery
Ashutosh Tiwari1, Gitanjali Mishra1, Jagdish Narayan2
1Department of Materials Science and Engineering, University of Utah, Salt Lake City, UT 84112, USA.
Sensors (Basel, Switzerland)
|May 27, 2026
Summary
Intelligent sensing systems integrate signal processing within materials, moving beyond traditional separated architectures. This approach enhances adaptability and efficiency for dynamic environments using functional materials and artificial intelligence.
Area of Science:
- Materials Science and Engineering
- Artificial Intelligence
- Sensor Technology
Background:
- Conventional sensing systems utilize sequential architectures, separating signal acquisition, processing, and decision-making.
- This separation leads to limitations in latency, energy efficiency, and adaptability, especially in data-intensive and dynamic environments.
Purpose of the Study:
- To discuss an emerging framework for intelligent sensing systems that integrate sensing functions within materials.
- To explore how non-equilibrium functional materials, coupled with artificial intelligence, can enable in-sensor information transformation.
Main Methods:
- Examining key material platforms with intrinsic properties like nonlinearity and memory for in-sensor processing.
- Investigating architectural strategies that integrate sensing and processing functionalities.
- Exploring opportunities for closed-loop autonomous discovery in intelligent sensing systems.
Main Results:
- Non-equilibrium materials enable in-sensor information transformation through nonlinearity, memory, and temporal dynamics.
- AI-powered material capabilities support sensing platforms for encoding, processing, and interpreting data at the measurement point.
- The convergence of materials science and AI facilitates adaptive, low-latency, and energy-efficient sensing.
Conclusions:
- Emerging intelligent sensing systems leverage functional materials and AI to overcome limitations of conventional architectures.
- This integrated approach enables sensing technologies that are adaptive, energy-efficient, and operate with reduced latency.
- Future directions include addressing challenges in material variability, scalability, and system integration for practical applications.
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