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Cognitive AI beyond prediction: toward reasoning and discovery
Jianliang Gong1, Han Zhou2, Shicheng Yu2
1Key Lab of Fluorine and Silicon for Energy Materials and Chemistry for the Ministry of Education, Jiangxi Normal University, Nanchang, 330022, China. ywchen@ncu.edu.cn.
Materials Horizons
|February 18, 2026
Summary
Artificial intelligence (AI) is advancing in materials discovery beyond property prediction to scientific reasoning. Cognitively enabled AI can act as research colleagues, enhancing battery science discoveries.
Area of Science:
- Materials Science
- Artificial Intelligence
- Battery Technology
Background:
- Modern artificial intelligence (AI) is crucial for materials investigation, particularly in battery research, accelerating the analysis of electrolytes, interfaces, and structural frameworks.
- Current AI excels at property prediction but struggles with fundamental scientific objectives like understanding, explanation, and adaptive reasoning.
Purpose of the Study:
- To propose that AI in materials discovery is evolving towards scientific reasoning capabilities.
- To outline a modular cognitive architecture for AI systems to address complex battery research challenges.
- To highlight the potential of AI as a collaborative tool for scientific discovery.
Main Methods:
- Leveraging recent advancements in neuro-symbolic reasoning, hypothesis generation, and autonomous systems.
- Developing a modular cognitive architecture integrating representation construction, mechanism inference, hypothesis formulation, experimentation, and belief revision.
- Applying these AI capabilities to address specific battery research issues like interfacial instability and electrolyte design under uncertainty.
Main Results:
- AI is progressing from property prediction to encompass scientific reasoning, including hypothesis generation and experimental design.
- A cognitive architecture can integrate diverse AI capabilities to tackle complex, uncertain problems in battery research.
- Cognitively enabled AI systems show promise as reasoning partners for scientists.
Conclusions:
- AI's evolution towards scientific reasoning is essential for deeper understanding and innovation in materials science.
- Integrating cognitive architectures into AI can unlock new approaches to challenging problems in battery development.
- Future AI systems can serve as invaluable collaborators, enhancing scientific discovery while preserving research integrity.
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