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MaterIQ

The future of Materials Science starts here.
We’re building something big. Stay tuned.

No spam — just signal when it’s time.

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Core Technology

Multiple Orders of Magnitude Faster.

MaterIQ replaces expensive computer simulations that take hours or days with physics-grounded AI predictions in under a second — without sacrificing fidelity.

100,000×

speed-up over traditional physics simulation

Simulation demo — GIF coming soon

Simulation of a reactive material:

Traditional Physics Modeling
Hours

Numerically solving complex differential equations per simulation run

Physics-Naïve AI
Minutes

Faster, but learns statistical patterns — cannot generalize beyond training data

Physics-Aware AI — PARC
< 1 sec

Physics embedded in the architecture — high-fidelity prediction, any regime

Why MaterIQ

Standard AI Cannot Solve Physics.

The correct solution requires both physics-embedded architecture and domain specificity.

Wrong tool
Traditional (Statistical) ML
  • Interpolates within training distribution only
  • Cannot enforce physical laws or constraints
  • Produces physically impossible predictions
  • Requires massive labeled datasets
✕ Wrong tool for physics
Not sufficient
Large Foundation Models
  • Better generalization to unseen tasks
  • Still learns physics as data patterns
  • Physics equations not in architecture
  • Can hallucinate unphysical predictions
▲ Better, but still not suitable
Most viable
Physics-Aware Deep Learning
  • Physics equations ARE the architecture
  • Generalizes to unseen physical regimes
  • Physically impossible outputs structurally prevented
  • Data-efficient—physics knowledge reduces learning burden
✓ Most viable choice

The Founding Team

Built by the Scientists Who Invented the Field.

Our founders spent a decade pioneering physics-aware deep learning at leading research universities. MaterIQ is the direct commercialization of that work — validated in peer-reviewed literature before a single line of product code was written.

Stephen Baek
Stephen Baek, Ph.D.
Interim CEO · Co-Founder
  • Physics-Aware Deep Learning
  • Scientific Machine Learning
  • Computational Geometry & Vision
H.S. Udaykumar
H.S. Udaykumar, Ph.D.
Chief Scientific Officer · Co-Founder
  • Computational Mechanics
  • Reactive Flow Dynamics
  • Scientific Machine Learning
Joseph Choi
Joseph Choi, Ph.D.
Director of R&D · Co-Founder
  • Physics-Aware Deep Learning
  • Scientific Machine Learning
  • Software Engineering

Contact

Partner with MaterIQ

Working on problems in defense materials, aerospace, or advanced manufacturing where simulation speed and fidelity matter? We want to hear from you.