This column focuses on the potential of AI in the advanced research stage of battery R&D.
Advanced research is the critical phase where target performance is defined and candidate materials are selected from vast sources of information.
Still, traditional methods rely on fragmented processes such as literature review, patent analysis, and experiment design. As a result, delays and information loss occur frequently.
This paper aims to explain how AI addresses these bottlenecks.
By integrating literature and patent analysis, simulation, experimental automation, and data interpretation into a single iterative loop, AI reduces uncertainty and accelerates development speed.
It also introduces how a multi-agent AI structure can divide tasks such as exploration, design, validation, and experimentation, and projects the possibility of an AI agent overseeing the entire battery advanced research process.
The paper demonstrates that beyond simple efficiency gains, AI can reshape early development strategies and enable companies to secure leadership in the next generation of battery technology.
<Contents>
1. Introduction
2. AI-Enhanced
Search and Validation Loop in Battery Advanced Research
3. AI Agents in
Advanced Research
4. Development
Speed and Cost Reduction Effects
5. Implementation
Roadmap and Risk Management
6. Implications