Mirror Physics provides an AI platform that learns physics from first‑principles quantum chemistry and materials data to deliver high‑fidelity predictions of reaction yields, material properties, and process conditions. The system integrates an active‑learning loop with high‑throughput laboratory automation and offers cloud‑native inference APIs with uncertainty quantification for pharmaceutical, specialty‑chemical, and advanced‑materials R&D teams.
Funding
Funding not disclosed
Founders
Product
Problem
Predicting experimental outcomes in chemistry and materials science remains computationally intensive and often inaccurate, limiting the speed of discovery and scale of industrial R&D. Existing simulation tools struggle to capture first‑principles physics at the throughput required for high‑volume experimentation. Consequently, companies face long development cycles and high costs when iterating on new compounds or materials.
Solution
Mirror Physics delivers a frontier‑scale AI platform that learns the underlying physics of chemical systems directly from first‑principles data. By training physics‑informed neural networks on massive quantum chemistry and materials datasets, the system generates high‑fidelity predictions of reaction yields, material properties, and process conditions. An active‑learning loop couples these predictions with high‑throughput laboratory automation, continuously refining the models against real experimental results. The platform exposes cloud‑native inference APIs, enabling R&D teams to query predictions on demand and integrate them into existing design workflows. This closed simulation‑experiment feedback cycle reduces the number of physical trials needed, accelerating discovery timelines while lowering material and labor costs.
Target Audience
Primary customers are pharmaceutical and specialty‑chemical companies, advanced materials manufacturers, and research laboratories that need rapid, accurate computational insight to guide synthesis and formulation decisions. The platform also serves AI‑enabled R&D groups within large industrial enterprises seeking to embed predictive modeling into their product development pipelines.
Features
- Physics‑informed transformer architectures trained on billions of quantum‑chemical calculations for sub‑percent error rates on reaction energetics
- Active‑learning pipeline that automatically selects high‑impact experiments for laboratory validation and feeds results back into model training
- Integrated lab‑automation interface supporting robotic synthesis, characterization, and data ingestion via standard OPC‑UA and REST endpoints
- Cloud‑native inference service with low‑latency GPU acceleration, supporting batch and real‑time query modes
- Built‑in uncertainty quantification and Bayesian calibration to flag predictions requiring experimental confirmation
- Multi‑scale property prediction covering electronic, mechanical, and thermodynamic metrics across organic, inorganic, and polymeric domains
- Compatibility with industry data standards (e.g., ELN, AnIML) and secure, encrypted data handling compliant with ISO 27001