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AstralQ

Category: AI in Manufacturing

AI-powered end-to-end materials discovery platform featuring MLH and MLFF models with integrated automated synthesis lab (One Cloud Lab) AstralQ was founded in 2025. The company is led by Jeongju Cho (JJ Cho). Based in Framingham, Massachusetts, United States. Team size: 11-50. Latest round: Seed. Key investors include Korea Investment Accelerator; Bluepoint Partners; Schmidt; Smilegate Investment.

Founded
2025
Headquarters
Framingham, Massachusetts, United States
Team size
11-50

Value proposition

World's first end-to-end AI materials development cloud lab that compresses decades-long materials development into years, reducing costs to 1/20th and speeding up development 10-20x by integrating computation, experiment, and validation into a single system.

Products and solutions

One Cloud Lab (end-to-end materials design platform); MLH (Machine-learned Hamiltonian) model for large-scale electronic structure computation; MLFF (Machine-learned Force Field) model trained on proprietary DFT datasets; Automated inorganic synthesis lab for rapid AI prediction validation

Unique value

World's first MLH model capable of computing electronic structures at large scale; integrated end-to-end platform from AI prediction through automated physical synthesis; proprietary DFT datasets; team of world-class materials scientists from Samsung, MIT, Max-Planck Institute, Boston University

Target customer

Materials scientists, R&D labs in battery, semiconductor, energy, and chemical industries; companies needing new materials development without in-house lab or computational staff

Industries served

Advanced materials; Battery/energy storage; Semiconductors; Chemicals; Energy materials

Technology advantage

Proprietary MLH (Machine-learned Hamiltonian) model for electronic structure computation; MLFF model trained on proprietary DFT datasets; automated inorganic synthesis lab closing the prediction-to-synthesis loop; end-to-end cloud lab platform eliminating need for physical lab or specialized computational staff

How they differentiate

Only company with end-to-end capability from AI prediction through automated physical synthesis (not just simulation); world-first MLH model for large-scale electronic structure; team with deep industry experience at Samsung, LG Chem, A123 Systems; capital-efficient approach (raising less than competitors while having built full platform)

Main competitors

Orbital Materials; CuspAI; Citrine Informatics; Kebotix; MatNex; Nanoforge AI (Korea); Kairos Lab (Korea)

Key partnerships

Korea Investment Accelerator; Bluepoint Partners; Schmidt; Smilegate Investment; Selected for TIPS Global Track by Korean Ministry of SMEs and Startups (MSS)

Major milestones

Founded June 13, 2025; Developed world-first MLH model for large-scale electronic structure computation; Built MLFF model on proprietary DFT datasets; Established automated inorganic synthesis lab; Closed Seed round (May 2026); Selected for TIPS Global Track by Korean Ministry of SMEs and Startups

Growth metrics

Founded June 2025; Seed round closed May 2026; 68 LinkedIn followers; 3 employees (Korean entity as of March 2026); 11-50 total (LinkedIn company page)

Market positioning

Early-stage deep tech startup competing in the rapidly growing AI-driven materials discovery market alongside well-funded peers; differentiated by end-to-end physical synthesis capability and capital efficiency; strong Korean investor and government backing despite US incorporation

Geographic focus

US (headquarters in Framingham, MA) and South Korea (Seoul office); targeting global materials science market

About Jeongju Cho (JJ Cho)

Ex-Samsung Research (US) Advanced Materials Lab Head; Samsung SDI; A123 Systems; LG Chem. 30+ years in materials development. PhD from Korea Advanced Institute of Science and Technology (KAIST). Published in Science, Nature Synthesis, Nature Communications.

Official website: