NanoForge AI raises $10.4 million to speed materials discovery

South Korean startup NanoForge AI has raised $10.4 million and is already testing AI-designed materials with Samsung Electronics and other manufacturers. The seed funding will help the 17-person company scale its electronic-structure model and autonomous lab as it races to cut the time needed to develop new materials for semiconductors, batteries and steel.

Why it matters: - NanoForge AI is targeting a slow, expensive step in industrial R&D: discovering materials for semiconductors, batteries and steel. - The startup says its approach could shorten years of trial-and-error experimentation by using AI to predict how materials behave at the electronic level. - The company is already at the industrial-testing stage with global manufacturers, which is rare for a startup founded less than a year ago. - South Korea depends heavily on advanced materials supplied from abroad, even though the country leads in memory chips and EV batteries.

What happened: - NanoForge AI raised $10.4 million in a seed round led by Mirae Asset Venture Investment. - DSC Investment, Korea Development Bank and EcoPro Partners also participated. - Existing investors FuturePlay and Mashup Ventures invested again. - The round brings NanoForge AI’s total funding to more than $11 million. - The company said it now has 17 employees in Korea. - NanoForge AI said it is testing or has completed proof-of-concept projects with more than five Korean and global manufacturers. - The company named KAIST professor Seunghwa Ryu as a technical adviser.

The details: - NanoForge AI is working with Samsung Electronics, a top battery maker and a major steelmaker. - The company’s collaborators include EcoPro BM, one of Korea’s largest makers of battery cathode materials. - EcoPro Partners, the venture arm of EcoPro, invested in the seed round. - NanoForge AI is also collaborating with researchers affiliated with Seoul National University, KAIST, Nanyang Technological University in Singapore and the University of California, Berkeley. - The company’s AI model predicts electronic structure, including energy bands and electron behavior. - Those properties help determine whether a material conducts electricity, absorbs light or holds a charge. - The model is meant to complement atomistic AI systems that mainly focus on forces between atoms. - NanoForge AI plans to use the new capital to scale the model and expand its autonomous laboratory. - The lab synthesizes and tests AI-designed materials in a closed-loop system. - Experimental results feed back into the model so NanoForge AI can choose what to test next. - The company says that process accelerates materials discovery. - Professor Ryu specializes in physics-based multiscale modeling. - Ryu leads KAIST’s AX department and its PRISM-AI research center for AI-driven design and manufacturing. - His advisory role will focus on manufacturing process optimization, multiscale materials modeling, industry partnerships and talent recruitment. - Chanyoung Park, a KAIST professor of industrial and systems engineering, has advised NanoForge AI since 2025. - Park works on graph machine learning and large language models for predicting molecular and material properties. - Park advises on core technology, R&D strategy and collaboration across industry and academia.

Between the lines: - NanoForge AI is pitching capital efficiency as a competitive edge in an increasingly crowded AI-for-science market. - The company has reached industrial validation with far less funding than bigger rivals that have raised hundreds of millions of dollars. - That contrast suggests the next phase of competition may hinge on whether startups can turn lab results into factory-floor materials quickly and cheaply. - The advisor additions also point to a push for tighter links between academic modeling and production-ready manufacturing.

What's next: - NanoForge AI will use the funding to expand both its AI model and autonomous experimentation pipeline. - The company will keep working with manufacturers to validate materials in real industrial settings. - The advisory team will support manufacturing optimization, research development and recruiting as NanoForge AI tries to turn early proof-of-concepts into commercial materials.

The bottom line: - NanoForge AI is betting that AI plus autonomous labs can make materials discovery faster, cheaper and more commercially useful than traditional R&D.