Antimatter,
are you ready for a challenge?

Antimatter is an AI engine that combines atomistic predictive modeling with generative approaches. We screen billions of material compositions to predict structures and physical properties, zeroing in on the candidates most likely to yield successful experiments.

Downselection of Compositions

10B → 100

Atomistic Simulation Speedup vs. Serial Workflows

100x

Continual Closed-Loop Operation

24h7d

Time-to-Discovery

370x

  • Gallium Nitride

    • Chemical composition

      GaN

    • Category

      Wide-bandgap semiconductor

    • Properties

      High breakdown field

      High electron velocity

      ~3.4 eV bandgap

    • Applications

      LEDs

      Power electronics

      Lasers

      RF Components

    • Experimentally Observed

      Yes

Unlocking the holy grail of materials science.

Predict

We screen billions of material compositions to predict structures and physical properties, identifying the best experimental candidates.

Optimize

We optimize chemical synthesis by integrating computational adaptive experimentation, active learning, and self-guided scientific literature review.

Experiment

We optimize chemical synthesis by integrating computational adaptive experimentation, active learning, and self-guided scientific literature review.

Feedback

The entire process generates valuable data that we feed back into our prediction engine, closing the loop.

Robots

Executing predictions that push the boundaries of materials science.

Enter the lab