Revolutionizing Materials Discovery with AI
Synth utilizes advanced artificial intelligence to predict novel molecular combinations and assess their viability, accelerating the pace of materials research. This enables scientists to identify promising compounds quickly and accurately, streamlining development processes and fostering innovation.
Transform your research with AI-powered molecular discovery.
Harness the power of Synth to rapidly identify viable molecular combinations, streamline your workflow, and make informed decisions faster. Designed for professionals seeking efficiency and accuracy in materials development.
Features
- Property-First Prediction – Start with desired effects (superconductivity, stability, optics, quantum behavior) and work backward to viable candidates.
- Peer-Reviewed Science – Predictions built on trusted toolkits (pymatgen, matminer, scikit-learn, MEGNet/M3GNet/CrabNet).
- 3D Bonding Simulations – Visualize how atoms arrange and bond in candidate structures.
- Interactive Models – Manipulate browser-ready 3D molecular models in real time.
- Multi-Layer Diagrams – Generate molecular, structural, and classical schematics.
- Eₕᵤₗₗ Stability – Color-coded convex-hull analysis to triage synthesizability.
- GPT-5 Analysis – Expert-level reasoning from physicist, chemist, and materials scientist perspectives.
- Self-Learning 3D Models – Structural predictions improve continuously with new data.
- Confidence Indicators – Every property comes with uncertainty and reliability scores.
Leverage AI to forecast new molecular combinations and their feasibility.
Streamline your materials research with AI-driven insights and predictions.
How it works under the hood!
Synth stands on the same open, peer-reviewed libraries and models the community trusts (pymatgen/spglib/ASE + CrabNet/MEGNet/M3GNet), adds a transparent Eₕᵤₗₗ triage, and layers expert LLM reasoning—all inside a reproducible Flask/Mongo workflow.
Chemistry & Symmetry Backbone
We standardize cells, infer neighbors/bonds, and keep outputs interoperable using pymatgen + spglib, the same stack widely used across materials research.
Rapid Stability Triage (Eₕᵤₗₗ)
A stability hook exposes E above hull as a first-pass thermodynamic proxy; the web UI color-codes rows (green/yellow/red) so you can prioritize likely-synthesizable candidates at a glance.
Structure I/O & 3D Viewing
Synth reads/writes CIF/SDF/PDB and auto-generates a self-contained HTML 3D viewer (3Dmol.js) so candidates are immediately inspectable and shareable. Backed by pymatgen and ASE for robust crystallographic handling.
ML Property Predictors (Peer-Reviewed Models)
Property prediction integrates community models such as CrabNet, MEGNet, and M3GNet, alongside the SciPy/Pandas/NumPy stack—mirroring methods validated in the literature and open datasets (e.g., Materials Project/OQMD).
Expert Reasoning (GPT-5)
For selected candidates, Synth sends structure + predicted properties to GPT-powered analysis, returning chemist/physicist/materials-scientist style explanations, equations, and synthesis ideas—all logged with the rest of the run artifacts.
Integrated UI
Simple User Interface and usage makes the platform easy to use and leverage for your materials research.
Contact Us
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