AI in Molecular Discovery

Accelerating Tribology: How Generative Adversarial Networks (GANs) are Redefining Additive Synthesis.

Traditional additive formulation is a game of trial and error—costly, slow, and often toxic. At LubriMon, we are replacing the beaker with the algorithm.

We utilize Machine Learning models trained on vast tribological datasets to predict the behavior of Bio-based Smart Additives before they are even synthesized. By using Generative Adversarial Networks (GANs), we can simulate how a specific ester-based molecule will interact with metal surfaces under extreme pressure (EP).

Our AI analyzes structure-property relationships (QSPR) to optimize for:

1. Biodegradability: Ensuring the molecule breaks down harmlessly.
2. Self-Healing Capability: Designing polymers that repair tribofilms dynamically.
3. Thermal Stability: Predicting oxidation points with 99% accuracy.

This isn’t just chemistry; it’s computational material science. We are coding the fluids of tomorrow to ensure the sustainability of today’s industries.