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83 Sciences: Materials discovery from the ~90% of experimental data that never gets published

We train materials discovery models on experimental data to help industry improve manufacturing performance at scale and help academics publish more.

Details

External ID
108805
Source
YC
Company
83 Sciences
Product
83 Sciences: Materials discovery from the ~90% of experimental data that never gets published
Website domain
83sciences.ai
Launched
Aug. 3, 2026
Cohort
Summer 2026
Upvotes
16
Upvotes percentile
0.5172413793103449
Tags
Hard Tech, Advanced Materials, AI
Fetched at
Sept. 30, 2026, 5 p.m.
Updated at
Sept. 30, 2026, 5 p.m.

Enrichment

Theme
embodied AI and robotics platforms
Vertical
Manufacturing
Function
Analytics & BI
Audience
B2B
AI stance
AI-native
Project type
Commercial product
Normalized one-liner
materials discovery from experimental data
Manually corrected
False

Could you build this?

No Extracting insights from unpublished dark scientific data requires deep materials science domain knowledge, custom scientific document/data parsers, and specialized graph/geometric neural networks for chemistry and crystallography.

What it would actually take: Building a real platform requires specialized multimodal parsers capable of interpreting messy instrument outputs (XRD, NMR, SEM, mass spec) and lab notebooks into standardized materials databases (CIF files, IUPAC nomenclature). It also demands custom graph neural networks (GNNs) or equivariant diffusion models (e.g., SchNet, Crystal-GFN) trained on physical properties, guided by teams with PhD-level materials science and crystallography expertise.

Competitors

Other products that read as similar to this one — 54 launches clear the similarity bar, closest 8 shown.

Attention rank: #32 of 55 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 270 days after the earliest competitor.

Other launches for this product

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