CATL Accelerates Solid-State Battery Research Using AI-Driven Material Discovery

CATL is intensifying its use of AI technology to accelerate research and development activities for next-generation battery systems, as the company expands its technological leadership in the global EV battery industry. Wu Kai, chief scientist at CATL, explained during the opening ceremony of The 18th China International Battery Fair in Shenzhen that artificial intelligence is significantly transforming how advanced battery materials are discovered, tested, and optimized. According to Wu, AI-driven systems are now helping engineers overcome limitations associated with traditional trial-and-error development models by enabling faster and more targeted material design processes.

Wu stated that AI has demonstrated exceptional capabilities in optimizing large volumes of process parameters, in some cases outperforming conventional engineering analysis. The company is using advanced AI models to pre-screen massive databases of candidate battery materials in search of ideal chemical systems for future batteries. This approach has reportedly reduced the time required for new material screening by two orders of magnitude. The accelerated process is helping the company improve efficiency across multiple research domains while reducing dependency on slower empirical testing methods.

The AI-based research framework is currently being applied across several key battery material segments. These include cathode coating materials, high-voltage electrolytes, and novel perovskite passivators that are considered important for future high-performance battery technologies. CATL indicated that AI systems are enabling deeper exploration of material combinations and performance characteristics, allowing researchers to identify promising solutions faster than conventional laboratory workflows. The company believes this transition toward intelligent computational development will play a central role in shaping future battery innovation strategies.

AI is also supporting one of the industry’s most closely watched areas, namely solid-state battery development. Wu revealed that CATL’s research team identified seven target materials within just three months using AI-assisted development processes. The company additionally achieved improvements in ionic conductivity during the same period, highlighting how machine-learning systems are contributing not only to material discovery but also to performance optimization. These developments are considered significant because ionic conductivity remains one of the critical technical factors influencing the commercial viability of solid-state batteries.

Beyond material discovery, CATL is integrating AI technology into the optimization of complex manufacturing parameters. The company is using intelligent systems to improve production efficiency, enhance process consistency, and support precision control during battery manufacturing operations. Wu noted that despite the rapid progress achieved so far, current AI applications remain primarily focused on accelerating individual tasks rather than enabling fully autonomous end-to-end research systems. However, the company’s long-term objective is to establish large-model-driven autonomous laboratories capable of independently managing extensive research and verification workflows.

According to Wu, these highly automated laboratories are already capable of meeting approximately 70% to 80% of CATL’s material synthesis requirements. The autonomous systems can independently complete critical processes including material feeding, chemical reactions, separation, purification, and analytical testing. This level of automation is expected to improve development efficiency while reducing manual intervention across multiple stages of battery research. CATL believes future AI-integrated laboratories could significantly shorten innovation cycles for next-generation energy storage technologies and strengthen industrial scalability.

CATL Global EV Battery Market Position in Early 2026

CATL continues to maintain a dominant position in the global EV battery market. According to data from SNE Research, the company captured a 40.7% global market share during the first three months of 2026. In China, CATL’s power battery installation volume reached 29.06 GWh in April, securing the top position with a 46.64% market share based on figures published by the China Automotive Battery Innovation Alliance. The company’s growing investment in AI-powered battery development is expected to further reinforce its competitive advantage as demand for advanced electrification technologies continues to rise globally.

Frequently Asked Questions

How is CATL using AI in battery research?
CATL is using AI technology to accelerate the discovery and optimization of next-generation battery materials and manufacturing processes. The company applies AI models to pre-screen large datasets of candidate materials, helping researchers identify promising chemical systems much faster than traditional trial-and-error methods. AI is also supporting the development of solid-state batteries, improving ionic conductivity, and optimizing production parameters. Additionally, CATL is building autonomous laboratories capable of automating material synthesis, testing, purification, and analytical workflows to improve overall R&D efficiency.

What is CATL’s current position in the global EV battery market?
CATL currently holds a leading position in the global EV battery market with a 40.7% market share during the first quarter of 2026. The company also dominates the Chinese battery market, where its power battery installation volume reached 29.06 GWh in April 2026 with a 46.64% share. Strong investment in AI-driven research, manufacturing optimization, and next-generation battery technologies such as solid-state batteries is helping CATL maintain its competitive advantage in the rapidly expanding electrification industry.