AI-Powered Material Discovery Aims to Solve Chip Efficiency Problem

Chips running AI workloads are too hot, consuming a significant amount of electricity and requiring expensive cooling systems. To address this issue, entrepreneurs are turning to AI to solve the problem it created.

AI Whack-a-Mole to Hunt Cooler Chips

Discovered Materials is the latest startup to tackle this challenge, using swarms of AI agents to find new materials that can be used to build more efficient integrated circuits. The startup recently closed a $9 million seed round from Lightspeed India Partners after emerging from Y Combinator, with investment from Peak XV Partners and angel investors Paul Graham, Gokul Rajaram, and Thariq Shihipar.

Founders Advaith Sridhar and Akash Ramdas 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. This approach allows them to explore thousands of research directions in a day, compared to Ramdas' 20 guesses during his PhD.

Examples of New Materials and a 'Material Discovery Bench'

Discovered Materials has released examples of hundreds of new materials today, as well as their 'Material Discovery Bench', which is designed to track how frontier models take on this challenge. Companies like MatNex, SandboxAQ, and CuspAI have all launched similar efforts, but Discovered Materials is betting that a laser-focus on the thermal problems of semiconductor materials is the path to success.

One challenge is the engineering trade-space: If they find a material that might reduce heat generation or improve dissipation, it might be too difficult to manufacture a chip out of it, or its electrical properties are compromised. Hemant Mohapatra, the Lightspeed partner who led this round, notes that 'it's a bit of playing whack-a-mole with atomic structures.'

Rapid Experimentation and Validation

Mohapatra expects that the business of predicting novel substances will be commoditized as models continue to improve. However, Discovered Materials' unique data and expertise will help them compete with deep-pocketed frontier labs. Sridhar acknowledges that the reality is that 'a lot of this will involve actually going into wet labs and like making things as well. And this is the process that cannot be sped up.'

While Sridhar believes that Discovered Materials' unique data and expertise will help the startup compete with deep-pocketed frontier labs, he acknowledged that the reality is that 'a lot of this will involve actually going into wet labs and like making things as well. And this is the process that cannot be sped up.'

However, for all the excitement, we still haven't seen any drugs or materials discovered by AI actually make a commercial impact. The closest is perhaps Insilico Medicine's Renterosib, the first drug discovered with generative AI to make it into a Phase II clinical trial. On the materials side, promising candidates have been found, like MatNex's rare-earth free permanent magnets or new semiconductor materials worked out by Panasonic and Citrine Informatics. But these haven't been commercially deployed at scale yet.

These techniques may be coming into their own now as AI continues to improve, but it's one reason why Mohapatra says that he doesn't believe finding more candidates is the hold-up for AI materials science; instead, 'filtering them correctly and synthesizing them is the bottleneck.'