MaterialGrammar

The physical properties of materials—such as strength, conductivity, elasticity, and optical behavior—are not just determined by what a material is made of, but how it's put together. Atomic arrangement, crystal lattice orientation, grain boundaries, and defects all play a central role in defining performance.

Understanding structure enables us to:

• Tailor materials for specific applications
• Improve efficiency in energy storage and conversion
• Design materials from the bottom up using principles of symmetry and bonding

Traditionally, discovering new materials relied on intuition, experience, and trial-and-error experiments. But now, with tools like machine learning, we're identifying hidden patterns across massive datasets of material behavior and structure.

Using techniques such as:

• Graph neural networks (GNNs)
• Dimensionality reduction (like PCA and t-SNE)
• Unsupervised clustering and anomaly detection

MaterialGrammar doesn't just compute data—it communicates ideas visually. We believe in making complex concepts intuitive by combining:

• Interactive visualizations (force-directed atomic graphs, crystal symmetry viewers)
• Storytelling around material phenomena
• Gamified learning interfaces that let users explore structures by “speaking” the language of materials