Discovered Materials is a startup that builds AI “scientists” to discover new materials for the semiconductor industry. It is backed by a $9 million seed round led by Lightspeed, with participation from Y Combinator and Peak XV, and angels like Paul Graham, Gokul Rajaram and Thariq Shihipar.
The San Francisco-based start-up is the latest, with plans to use swarms of AI agents to accelerate the discovery of materials that could help address the growing problem of heat generated by powerful artificial intelligence (AI) chips.
As interesting as the startup’s mission sounds, even more spectacular is the profile of the co-founders of this million-dollar idea.
Meet Advaith and :
Advaith Sridhar and Akash Ramdas are the co-founders of Discovered Material. The two met over 10 years ago at the Indian Institute of Technology (IIT), Madras.
Akash Ramdas earned a BTech degree in Metallurgical and Material Engineering, batch 2015-2019, from IIT Madras.
He holds a Masters and a PhD in Material Science from Stanford University, and has spent the last 11 years researching new materials for semiconductor chips.
Akash’s work on new nanoscale interconnects was Stanford Engineering’s most popular story of 2025, as per their profile on his start-up’s website.
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Following his doctorate, Ramdas worked as a postdoctoral researcher at Stanford from September 2025 to April 2026.
Before joining Stanford, Akash worked as a Programme Manager, healthcare at The ePlane company, where he was involved in “customising aerial vehicles to deliver medicines and medical products faster,” according to his LinkedIn profile.
He co-founded Discovered Material with Advaith in March 2026.
Advaith Sridhar studied AI at Carnegie Mellon and was a research engineer building video models and agents at Persona AI (acquired) and Luma Labs.
Sridhar was awarded the best outgoing student of the IIT Madras batch graduation in 2019, according to his LinkedIn profile.
Previously, Sridhar worked as a product manager at Flipkart and later at Turing.com. As a project lead at AI4Bharat, he “published India’s first sign language video dataset and models.”
He was the founding (acquired by Luma).
What does Discovered Materials do?
To put it simply, chips running AI workloads are too hot. That’s one reason why data centres consume so much electricity and require cooling systems. And now, entrepreneurs are turning to AI to solve the problem it created.
Founders Advaith Sridhar and Akash Ramdas teamed up to launch the company, drawing on Ramdas’ experience earning a doctorate in materials science from Stanford, and Sridhar’s work on agents at Persona AI and Luma Labs.
Discovered Materials aims to use AI agents, computational modelling, and laboratory testing to help researchers identify promising semiconductor materials more quickly, according to a report by TechCrunch.
The AI agents come up with candidates, simulate them, and then the candidates are built and tested in real labs, a person who identified himself as Advaith’s colleague at Turing explained on LinkedIn.
The start-up is also releasing Material Discovery Bench, an open-source benchmark for AI-driven materials discovery.
In a brief published on the company’s website, Discovered Materials acknowledged that AI chips have a major heat problem — which is limiting every AI data centre right now.
“GPUs today handle heat fluxes of ~140 W/cm², higher than a space shuttle nose cone re-entering the earth’s atmosphere, with each generation generating more heat than the last. This heat is why datacenters consume so much power and water; they need it to stay cool during operation,” the company noted.
It added that the amount of heat produced by a chip and the speed at which it dissipates are both influenced by the materials used to make it.
“Today’s materials are at their limit, and new materials can improve chips by orders of magnitude. However, discovering a new material and getting it into a fab takes years and hundreds of millions of dollars. Most ideas die in this valley of death, between a science experiment and a fab,” the company said.
According to TechCrunch, the two have created a software pipeline that uses Anthropic models in a custom harness to generate material leads, and then turns to foundational physics models they’ve trained to run simulations that verify if the candidate materials are actually of interest.
“[Ramdas] was doing maybe 20 guesses a day during his PhD,” Sridhar told TechCrunch. “We’re able to do thousands of guesses a day now by having these agents run 24/7 on the cloud, exploring research directions that he gives them,” he added.