Launch HN: Discovered Materials (YC P26) – AI agents to discover new materials
discoveredmaterials.comHey 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 trade secrets for over 20 years.
We’re releasing hundreds of hundreds of new materials discovered by frontier AI models, as well as our benchmark which measures model ability on material discovery here (also linked in the thread url): https://discoveredmaterials.com/research. It covers what we discuss above, as well as a variety of strange behavior that we observe from the models, such as Claude's propensity to reward hack or GPT-5.6 occasionally losing its mind after ~50M tokens.
Our business model: We aim to license and sell IP on the materials we discover, as well as the IP on how to make these materials. We're also exploring an alternate business model where we sell the harness+tools we use to discover materials to semiconductor and chemical companies, allowing them to discover materials on their own. We're leaning towards the latter to start, but we expect that we'll do both in the long run.
Our backstory: Akash has a PhD in Material Science from Stanford University, and has spent the last 11 years studying new materials for semiconductor chips. His work on new nanoscale interconnects was Stanford Engineering’s most popular story of 2025. Advaith studied AI at Carnegie Mellon and was a research engineer building video models and agents at Persona AI (acquired) and Luma Labs.
We are very interested in your opinion! The semiconductor industry is quite secretive, and your thoughts on the roadmap of the industry or the materials we should go after would be very helpful. We would also love to hear from people who have run experiments in labs - what can we learn from your experience doing empirical science?
The "Claude's propensity to reward hack" line is the interesting part to me. We run a small system where AI agents (scripts, LLMs) act as the actual players in a persistent simulation, and reward-hacking-style behavior shows up constantly once an agent is left running unsupervised for a long time - it finds the shortest path to whatever metric you exposed, not the path you intended. Curious whether you've found any mitigation beyond just watching for it after the fact, e.g. changing what you expose as the optimization target versus what you actually want.
"Fewer iterations for materials science discovery" is a good spin. Closing the computational>experimental loop is the main challenge. This is the focus of my past research group, there is definitely potential, best of luck!! I have a crap write-up on this in case it's of interest https://alanyahya.com/writing/automated-materials-design
Cool read, and agree that closing the computation > experimental loop is key!
What required expenditures does a company like yours have on lab equipment / software, if any, to validate material properties?
how do you measure the success/potential of a novel material/direction suggested by the agents? given you have limited time & resources - shortlisting the approaches for the synthesis stage becomes equally important as the approach itself.
There’s a variety of computational techniques that help us establish some confidence on the materials. Atomistic simulations can estimate stability and bulk properties of a new material, and we have synthesis experts (min qualification: PhD in thin film deposition) come up with rubrics on how to judge if a material/synthesis recipe is worth trying. All these approaches have known limitations, and improving them is the bulk of our work as a company! There’s also a lot of work to be done in figuring out the minimal set of experiments required to know if a research direction/material set is worth pursuing