IIT Madras and Stanford Researcher Builds $9 Million AI Startup to Solve Chip Overheating
Ramdas and Advaith Sridhar, who met at IIT Madras, have co-founded Discovered Materials, an AI startup that aims to speed up the discovery of new materials for computer chips. The company has raised $9 million to expand its research and develop AI-powered tools.
An Indian Institute of Technology (IIT) Madras graduate is taking his research beyond university laboratories to address one of the biggest challenges in modern computing. Akash Ramdas, who later pursued higher studies at Stanford University, has co-founded an AI startup focused on discovering materials that could help improve semiconductor chips.
The startup, Discovered Materials, was co-founded by Ramdas and Advaith Sridhar. The two first met at IIT Madras more than a decade ago and later developed expertise in different areas, including materials science and artificial intelligence.
The company recently announced a $9 million seed funding round led by Lightspeed India Partners. Y Combinator and Peak XV Partners also participated, along with angel investors Paul Graham, Gokul Rajaram and Thariq Shihipar.
In a LinkedIn post announcing the funding, Sridhar wrote, “Today we’re excited to announce our $9M seed round, led by Lightspeed India, with participation from Y Combinator and Peak XV Partners, and angels like Paul Graham, Gokul Rajaram and Thariq Shihipar”.
The funding will help the startup expand its team, laboratory capabilities and AI research agents. According to its announcement, the company is working to make the discovery of new semiconductor materials faster and reduce the time required to move scientific research towards practical applications.
It was reported on October 19 that Ramdas is using his research experience to build AI-based tools for materials discovery, with the aim of addressing the growing problem of heat generated by powerful computer chips.
How AI Can Help Solve The Chip Heat Problem
Modern computer chips, particularly those used for artificial intelligence, generate considerable heat. As computing demands increase, managing this heat becomes more difficult. Data centres also require substantial electricity and cooling resources to keep their systems running.
Finding new materials that can manage heat more effectively is one way to address this challenge. However, researchers typically need to study numerous possibilities, run simulations and conduct laboratory experiments before identifying a material that works.
Discovered Materials aims to speed up this process using AI agents. These systems can suggest potential materials and help researchers assess their properties through computer simulations and scientific models.
The company says its AI agents can explore thousands of potential materials in a day. This is a significant increase compared with the roughly 20 candidates Ramdas examined daily during his doctoral research.
The startup has also introduced Material Discovery Bench, a benchmark designed to assess how well AI systems perform on real-world materials discovery problems related to semiconductor chips.
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From IIT Madras To Stanford University
Ramdas completed his BTech in Metallurgical and Materials Engineering at IIT Madras in 2019. He then pursued a master’s degree in Materials Science and Engineering at Stanford University, followed by a PhD in Materials Engineering, which he completed in June 2025.
His doctoral research focused on computational methods for discovering materials for nanoscale electronic devices. He subsequently worked as a postdoctoral researcher at Stanford before co-founding Discovered Materials.
His co-founder, Advaith Sridhar, studied at IIT Madras and Carnegie Mellon University, where he developed expertise in artificial intelligence. He also worked on AI systems at Persona AI and Luma Labs.
With its new funding, Discovered Materials plans to expand its research and develop its AI systems further. Its long-term progress will depend on whether the materials it identifies can deliver reliable results beyond computer simulations.
