83 Sciences GTM brain

83 Sciences works with industrial R&D teams and academic labs to find novel materials using AI and experimental data.

category

AI materials discovery

market profile

  • text: The company seeks to structure and analyze unpublished experimental records, including failed runs and raw instrument output.
  • text: Its stated focus includes magnetic materials, critical mineral extraction and semiconductor packaging.
  • text: Materials R&D platforms compete to use scarce experimental data to guide candidate selection and product development.

personas

  • title: VP of materials R&D
    pains:
    • Discovery involves costly experiments and repeated dead ends.
    • New materials must meet performance requirements at an economically viable cost.
  • title: Principal investigator in materials science
    pains:
    • Unpublished and failed experiments sit in notebooks or instrument output instead of informing new hypotheses.
    • Lab members need usable data tools and collaborators who understand experimental chemistry.
  • title: Semiconductor packaging R&D lead
    pains:
    • Finding materials that satisfy packaging constraints takes iterative testing.
    • A candidate has to be validated in experiments and ultimately fit manufacturing requirements.

icp

  • text: Industrial research teams in batteries, semiconductors, membranes, catalysts or pharma seeking improved material performance or cost; firm size is unspecified.
  • text: University materials and chemistry labs with substantial unpublished experimental records are prospective data and research partners.

message pillars

  • text: Ground discovery in real experimental measurements.
  • text: Pursue materials relevant to critical mineral extraction.
  • text: Apply research to semiconductor packaging.
  • text: Frame the work around novel materials.

objections

  • objection: Our historical experimental data is sparse and inconsistent.
    response: Scope a specific dataset and compare predicted candidates with a prospective laboratory validation; do not assume discovery is guaranteed.
  • objection: Who owns the raw data and any resulting IP?
    response: Agree confidentiality, permitted uses, publication and intellectual property terms before sharing records.
  • objection: Will a promising material meet production constraints?
    response: Define target properties and manufacturing constraints upfront, then validate the candidate experimentally.

competitors

  • name: Citrine Informatics
    website: https://citrine.io/solution/
    why: Materials informatics platform uses R&D data and AI to guide development decisions.
  • name: MaterialsZone
    why: Offers materials informatics for industrial materials research.
  • name: ChemPilot
    website: https://chempilot.ai/
    why: Applies deep learning to molecular discovery, including specialized materials.

influencers

  • name: Ian Naccarella
    url: https://www.linkedin.com/in/ian-naccarella
    why: CEO has publicly invited industry and academic research partnerships.
  • name: Eric Riesel
    url: https://www.ycombinator.com/founders/eric-riesel
    why: CTO combines experimental chemistry and machine learning research.

keywords

  • AI materials discovery
  • materials informatics
  • unpublished experimental data
  • magnetic materials discovery
  • critical mineral extraction materials
  • semiconductor packaging materials
  • AI experimental chemistry

voice

  • Scientific and concise
  • Ambitious
  • Research-led

signals

  • text: An industrial R&D group is seeking improved cost or performance in semiconductor, battery, membrane or catalyst materials.
  • text: A lab has accumulated unpublished experimental data and wants to extract further research value.

hiring

  • Founding AI Engineer
  • Head of Data Partnerships

pricing

model: unknown

motion

sales_led

channels

  • LinkedIn
  • academic partnerships

confidence

medium

Founders

  • name: Ian Naccarella
    title: Founder/CEO
    role: CEO
    bio: Founder at 83 Sciences developing the future of scientific discovery. Previously a fellow at MIT focused on commercializing IP out of the ecosystem, a strategy manager at Sila Nanotechnologies (battery materials unicorn), and a consultant at Boston Consulting Group. Hold a BS and MS in Chemical Engineering from Stanford and MBA from Harvard Business School.
  • name: Eric Riesel
    title: Founder/CTO
    role: CTO
    bio: Founder at 83 Sciences. Experimental chemist and ML researcher with a PhD in inorganic chemistry from MIT, where I built machine learning systems to analyze complex materials data in minutes instead of days. My research spans quantum materials, generative models, and experimental data analysis, including one of the first generative approaches for chemical experimental data. BA in Chemistry from Columbia.
  • name: Yankang Yang
    title: Founder/COO
    role: COO
    bio: Founder at 83 Sciences. Former BCG Principal; led the firm's AI program scaling tools and AI practices to 30,000+ users globally. Harvard CS & EE undergrad. Past robotics research (Wyss Institute) and biosignals research (Roswell Park) experience

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