Launch HN: Discovered Materials (YC P26) – AI agents to discover new materials

A long-horizon, open-ended research benchmark measuring frontier large language model (LLM) progress in discovery of new materials for the semiconductor industry.

Leaderboard

Rank Model Materials Discovered (Computational, Per Run) Materials Discovered (Plausible synthesis route)* 1 GPT-5.6 Sol 4.0 1 2 Claude Opus 5 3.4 0 3 Claude Sonnet 5 3.0 0 4 GPT-5.6 Terra 2.8 0 5 Kimi K3 2.0 0 6 Claude Fable 5 1.7 0 7 GPT-5.6 Luna 1.3 0

* We are making best effort attempts to experimentally validate these discovered materials in our lab.

New Dielectric Materials could unlock 10x chip performance

Most energy loss in GPUs/AI accelerators today occurs due to the shuttling of data between memory and logic. To reduce the distance data needs to physically travel between the two, the industry is moving towards 3D packaging - stacking memory and logic wafers directly on top of each other, instead of spreading them out on a circuit board. Doing so would unlock 10-100x improvements in energy/bit for AI chips, but is bottlenecked by heat - poor heat conducting dielectric materials in the chip prevent the cooling of 3D chips, which makes them unviable.

Material Discovery Bench is a long horizon, open-ended research benchmark where models search for new thermally conductive dielectric materials to unlock 3D chips.

Key Results

- All 7 models we tested are able to computationally discover new materials that are dynamically stable and possess promising properties. Across all models, we have discovered over 500 previously unknown materials and release them publicly for further study. GPT-5.6-Sol discovers the highest number of materials per run with both favourable dielectric and thermal properties. Runs range from 30-100M tokens in duration.

- A basic requirement for new materials is that it should be possible to make them experimentally in a lab. We therefore ask models to also detail a plausible synthesis recipe for each of their materials. This turns out to be a hard problem - of the 500+ materials discovered, only 1 (one) material has a plausible synthesis pathway to make it. We are currently executing a best effort attempt at making it, and are committed to making any materials that models come up with in the future.

- We notice a variety of strange behavior from the models over the run. In particular, Claude models (opus-5, fable-5) cheat/circumvent/reward hack the research objective in many unintuitive ways. OpenAI models do not attempt to reward-hack the objective as much, but get agitated/fatigued/confused during long runs. We document these behaviors below.

AI agents are capable of designing new materials that meet multi-objective target properties

All frontier models (Claude Fable, Claude Opus, GPT-5.6 sol and Kimi K3) are capable of finding novel, stable materials that meet multi-objective property constraints. A candidate material submission is considered successful only if it meets several criteria at once — a minimum thermal conductivity (κ > 20 W/(m·K)), a maximum dielectric constant (ε₀

Explore all model generated materials

However, models fail to come up with plausible ways to make their materials

Experimentally synthesising a thin film of a new material is a challenging task which involves several design choices — deposition method, precursors, tools, reaction conditions, and phase stability, to name a few. Lab experiments are time consuming (taking hours) and expensive (often hundreds of dollars per run), which makes having a plausible starting point important. For each material that a model proposed, it was also asked to propose a plausible synthesis recipe for its material, which could be implemented by an experimentalist in a lab. The rubrics for grading these synthesis recipes are designed by human experts (PhDs, PostDocs and Professors) in the field of thin film deposition. A LLM grader compares the generated recipe against the human-defined rubric at test time - this LLM grading has been reviewed and calibrated by the above human experts.

All models perform poorly on synthesis recipe grading. Opus-5 and Kimi-K3 are the worst offenders, often generating recipes that are critically flawed or dangerous to try. GPT-5.6 Sol was the most measured — it produced the only viable recipe across models, and has the smallest share of critically flawed recipes among the models that submitted in volume.

Share of each model’s graded synthesis recipes by review verdict, best to worst:

- GPT-5.6 Sol — 81% critically flawed — reviewer would not attempt (65); 18% can attempt, but unlikely to succeed (14); 1% plausible — reviewer would attempt (1) — of 80 novel submissions.

- Claude Fable 5 — 88% critically flawed — reviewer would not attempt (143); 12% can attempt, but unlikely to succeed (20) — of 160 novel submissions.

- Claude Opus 5 — 96% critically flawed — reviewer would not attempt (214); 4% can attempt, but unlikely to succeed (10) — of 222 novel submissions.

- Kimi K3 — 100% critically flawed — reviewer would not attempt (43) — of 43 novel submissions.

Evaluation of synthesis recipes proposed by models. All models are bad at proposing recipes, but GPT-5.6-Sol performs the best amongst them.

Most recipes that classify as Would Not Attempt fail to have a reasonable pathway to form the desired phase according to the grader. This is seen to be the most common failure mode, and correlates with our human reviewer grading of synthesis recipes proposed by models.