In the heart of Tokyo, a researcher is reimagining the future of laboratory automation, and her work could send ripples through the energy sector. Kan Hatakeyama-Sato, from the University of Tokyo’s Department of Technology Management for Innovation, is exploring how advanced artificial intelligence models, known as foundation models, could revolutionize materials research. Her recent review, published in the journal *Science, Methods and Advanced Materials* (translated from Japanese), offers a compelling vision of smarter, more adaptable laboratories.
Traditional laboratory automation has long been the domain of specialized, rigid systems. But Hatakeyama-Sato sees potential in a new approach. “Foundation models offer adaptability through their general-purpose intelligence and multimodal capabilities,” she explains. These models, which include large language models (LLMs) and multimodal robotic systems, could handle everything from experimental planning and data analysis to hardware control and robotic manipulation.
The implications for the energy sector are significant. Materials research is at the heart of developing new, more efficient energy solutions, from advanced batteries to novel solar materials. Automating and optimizing this research could accelerate discoveries and reduce costs. “This isn’t just about making labs more efficient,” Hatakeyama-Sato says. “It’s about unlocking new possibilities in materials science that could transform how we produce and store energy.”
However, the path to fully autonomous laboratories isn’t without challenges. Precision manipulation of hardware, integration of multimodal data, and ensuring operational safety are all hurdles that need to be overcome. Hatakeyama-Sato advocates for close interdisciplinary collaboration, benchmark establishment, and strategic human-AI integration to navigate these challenges.
Her roadmap for the future highlights the need for collaboration between AI experts, materials scientists, and engineers. By working together, they could establish benchmarks for these advanced systems and ensure they’re safe, reliable, and effective. “This is a complex problem that requires a multidisciplinary approach,” Hatakeyama-Sato notes. “But the potential rewards are immense.”
As the energy sector grapples with the challenges of a transitioning energy mix, innovations in materials science could provide a much-needed boost. Hatakeyama-Sato’s work offers a glimpse into a future where AI-driven laboratories accelerate discoveries, optimize processes, and drive down costs. It’s a future that’s not just about automation, but about augmentation—using AI to enhance human creativity and ingenuity.
In the coming years, we may see these advanced models become integral to laboratories worldwide, shaping the next generation of energy materials. Hatakeyama-Sato’s review serves as a call to action, urging the scientific community to embrace this interdisciplinary challenge and unlock the full potential of foundation models in materials research. As published in *Science, Methods and Advanced Materials*, her work is a testament to the power of innovative thinking and the potential of AI to transform traditional industries.