← The Index / ai
Scored by model · 2 samples, median · No human review
Rank 23 of 35 · clearer than 32% of the index, 0% of ai · Reviewed 2026-10-11
Discoveredmaterials
61
GRADE C
Ranked by what each change is worth on the composite — the gap to the next rung of the ladder times the dimension’s weight. Computed from the scores, not written by the model; the model’s sentence under each is the action.
Use a concrete product noun in the headline, e.g., 'Materials-discovery AI tools' or similar.
Include a verifiable metric tied to a deliverable, e.g., AI agents cut discovery cycle to two weeks.
Replace vague terms with buyer-facing wording like 'AI tools for materials discovery benchmark'.
All three together: 61 → 72. Every dimension one rung up: 76 (grade B). Ceiling is 95.
Built only from facts already on the page — not the company’s words. Shown so the gap between what was written and what could have been written is concrete rather than a number.
Five alternative headlines in five angles, a subhead, a call-to-action line, and a line-by-line edit for each of the five dimensions — written only from facts already on discoveredmaterials.com, gated like a rating (anything with a struck word is dropped, not sold). Delivered on a private page in about a minute.
Five dimensions on one ladder — 5 · 20 · 35 · 50 · 65 · 80 · 95 — weighted clarity 30%, specificity 20%, jargon 20%, proof 15%, call to action 15%. For each: the rung the page matched, the words that drove it, and what would reach the next rung. Full rubric →
The page communicates a general mission but does not name a concrete product category; uses abstraction like “lab-to-fab timeline.”
Discovered Materials — Accelerating the lab-to-fab timeline
Next rungUse a concrete product noun in the headline, e.g., 'Materials-discovery AI tools' or similar.
The page mentions outcomes like compressing work into days and a benchmark, but lacks concrete, checkable metrics tied to an offered product.
AI agents compress months of inter-disciplinary scientific work to days.
Next rungInclude a verifiable metric tied to a deliverable, e.g., AI agents cut discovery cycle to two weeks.
Multiple abstract terms (AI agents, benchmark, discovery) reduce clarity.
Material Discovery Bench, an open-source benchmark for AI-driven materials discovery.
Next rungReplace vague terms with buyer-facing wording like 'AI tools for materials discovery benchmark'.
No named customers or measurable usage metrics are provided; only investors are mentioned.
Backed by Lightspeed, Y Combinator and many more.
Next rungInclude a named customer or quantified result from using AI agents to validate impact.
The page includes a Join us call-to-action, but it isn’t tightly tied to a concrete product promise.
Join us
Next rungAdd a clear CTA tied to a concrete product benefit, such as 'Join us to accelerate materials discovery today'.
Discovered Materials — Accelerating the lab-to-fab timelineHeadline used to judge clarity and product naming.
AI agents compress months of inter-disciplinary scientific work to days.Provides a concrete-sounding outcome but lacks a measurable, verifiable claim tied to a product.
Discovered Materials — Accelerating the lab-to-fab timeline Accelerating the lab-to-fab timeline Backed by Lightspeed, Y Combinator and many more. Today, we introduce Discovered Materials: we build AI agents that discover new materials for semiconductor chips. Alongside this, we also open-source Material Discovery Bench , a benchmark we built to measure exactly this capability. We are backed by a $9M seed round led by Lightspeed, with participation from Y Combinator and Peak XV, and angels like Paul Graham, Gokul Rajaram and Thariq Shihipar. Intelligence has a heat problem AI chips have a major heat problem. 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 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. Autoresearch for the lab AI agents compress months of inter-disciplinary scientific work to days. Over the 3 months of our Y Combinator batch, we simulated, synthesized and tested ther
Strongest: Clarity 65. Weakest: Proof 35. The 2 passes agreed within 5 points.
Badges are issued to grades A and B. The share card for this page shows the headline with its jargon struck through; paste the URL anywhere that unfurls links.
Scored from the live homepage
on 2026-10-11 by gpt-5-nano,
2 independent passes, median taken.
Page snapshot b5804e5d442b.
Rubric 5abbca9a (current; what it says).
No person reviewed or edited this score. If the rubric and the score disagree, the rubric wins
and the page is re-scored — not hand-corrected.
Re-scored when the homepage text changes.