Launch HN：Discovered Materials（YC P26）——用于发现新材料的AI代理

Hey HN, we're Advaith and Akash from Discovered Materials ( https://discoveredmaterials.com/ ).We build AI agents that discover new materials for the semiconductor industry.GPUs today have a heat problem.Nvidia & AMD are almost doubling the TDP (Thermal Design Power) in every chip they release - the H100 (released 2022) has a TDP of 700W, Blackwell (2024) gives out 1.2 kW and Rubin (2026) gives out at 2.3 kW of heat.This trend is expected to continue, and getting rid of this heat is one of the major reasons datacenters consume so much power and water today - they need it to keep chips cool during operation.

The amount of heat produced by a chip and its ability to dissipate it are both influenced by the materials used to make it.For example, we could reduce the energy per bit required to move data between logic and memory by 10-50x by 3D packaging chips (placing HBM memory stacks directly on top of logic chips, instead of placing them beside logic on a 2D circuit board).However, we're unable to do this today because the dielectric material used in HBM (such as SiO2) is a very poor thermal conductor, trapping heat between logic and memory and causing drastic temperature rise during operation.

Similarly, there's many other materials in the GPU that are being re-evaluated today - 2 more examples are thermal interface materials and substrates.However, getting a new material into a fab takes years and hundreds of millions of dollars of research - the infamous "lab-to-fab valley of death".At Discovered Materials, we're optimistic that AI agents can reduce the timeline and cost required to introduce new materials into semiconductor chips.We're seeing glimpses of this already - we tested 7 models from Anthropic, OpenAI and Kimi, and found that they're all able to computationally discover new materials that are dynamically stable and possess promising properties.

This was surprising to us - it would generally take a PhD student a couple of weeks of work to discover the kind of materials that these models find over an 8 hour run!However, computational discovery is the easy part.A material discovery is only valid if the material can be made and tested in a lab (As an example, graphene’s properties were predicted in 1947 but it was made for the first time in 2004).Today’s models are not good at coming up with synthesis recipes to make materials in a lab.Even if they do get better at it, we're uncertain about how much that will help - making a new material is a highly empirical process involving trial and error over many experiments.

Human experts themselves cannot "one-shot" the task, but we expect that a highly capable model will reduce the number of experimental iterations required to make a new material.We’ve seen some evidence of this over the 3 months of our Y Combinator batch - we simulated, synthesized and tested thermal interface materials (TIMs) that match the performance of TIMs the world's largest chemical companies have guarded as tr