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| Module | Functionality | Notable Tech | |--------|---------------|--------------| | | Sketching molecules, reaction mapping, and auto‑balancing equations. | RDKit + custom graph‑neural networks. | | Chemal‑Predict | Predicting reaction yields, thermodynamics, and safety hazards. | Gradient‑boosted trees trained on Reaxys data. | | Chemal‑AI | Embeds LISA for natural‑language query handling and image generation. | LISA‑Chem fine‑tuned checkpoint. | | Chemal‑Lab | Integrates with electronic lab notebooks (ELNs) and automated synthesis robots. | RESTful API, Docker‑compose orchestration. | Never click on unverified blog links or unsecured
| Feature | Description | |---------|-------------| | | Transformer‑based encoder‑decoder with cross‑modal attention layers. | | Parameters | Approximately 1.5 billion trainable weights (base model) with optional fine‑tuned variants up to 6 B. | | Training Data | 1.2 TB of paired text‑image data plus a curated corpus of scientific papers (chemistry, materials science). | | Modalities | Text, static images (up to 1024 × 1024 px), and limited video‑frame input (single‑frame inference). | | Safety | Built‑in toxic‑content filter and a “chemistry‑aware” guardrail that flags potentially hazardous synthesis instructions. | | RDKit + custom graph‑neural networks