The fastest and most efficient way to accurately simulate materials.
Unprecedented Performance
More than 10,000x faster performance compared to traditional quantum mechanics methods. This allows simulations that once took months to complete in just minutes.
Quantum-Level Accuracy
Model predictions are indistinguishable from Density Functional Theory with R² > 0.999 across energy, force, and stress predictions for diverse materials.
Proprietary Uncertainty Quantification (UQ)
Efficient per-atom uncertainty quantification enables identifying and correcting inaccurate model predictions for trustworthy results that can be used in engineering decisions.
Active Learning Engine
A self-tuning machine-learning engine that eliminates hallucinations.
An automated active learning framework is used to efficiently generate high-quality training data. Starting from a pretrained model, the model iteratively learns, flagging uncertain configurations using its built-in uncertainty quantification, and selectively generates additional data to refine its predictions.
This feedback loop continues until the model achieves high confidence across the sampled space. Using active learning reduces dataset size by 90% and training costs by 50%. The result is a data-efficient, self-improving model that requires virtually no manual intervention and achieves superior accuracy faster.
