According to Google DeepMind on X, the company upgraded its specialized reasoning mode Gemini 3 Deep Think to address complex science, research, and engineering problems, highlighting a real-world use case where Duke University’s Wang Lab applies the model to design new semiconductor materials. As reported by Google DeepMind, the upgrade targets systematic multi-step reasoning, enabling hypothesis generation, literature-grounded planning, and constraint-aware optimization for materials discovery workflows. According to the same source, the lab workflow integrates Gemini 3 Deep Think to propose candidate materials, assess properties against fabrication constraints, and iterate designs, indicating potential reductions in design cycles and improved researcher productivity in semiconductor R&D. As posted by Google DeepMind, this positions multimodal reasoning models as decision-support tools for labs seeking faster experimentation, with opportunities for industry partners to accelerate materials screening, process tuning, and yield optimization.

According to Demis Hassabis on Twitter, the AI for Science team has made significant progress in materials research, highlighting the potential of artificial intelligence to accelerate materials discovery and innovation (source: x.com/demishassabis/status/1982031487558951232). This development demonstrates how AI technologies are transforming scientific research by enabling faster analysis, prediction, and simulation of new materials. Businesses in the AI industry can leverage these advancements to create new solutions for sectors such as pharmaceuticals, energy, and advanced manufacturing, where rapid materials innovation offers a competitive edge.