GPT-Based Polymer Design for Ionic Conductivity

The patent uses machine learning models to create virtual copies of polymer structures and their properties. Instead of physically synthesizing and testing each polymer variant, the system generates computational representations (SMILES strings, molecular graphs) that mimic real polymer behavior. These digital twins allow rapid evaluation of thousands of polymer candidates without physical experimentation, dramatically reducing development time while maintaining property accuracy through validated ML predictions.

The patent performs preliminary screening and evaluation of polymer candidates using machine learning models before actual synthesis. The system pre-evaluates thousands of virtual polymer structures for desired properties (ionic conductivity, transference number, stability) using trained ML models, then only synthesizes the most promising candidates. This preliminary computational filtering eliminates the need to experiment with all possible polymers, reducing overall development time and cost.

The patent replaces expensive physical polymer synthesis and characterization with computational copying. Virtual polymer models are generated and evaluated using machine learning, eliminating the need for costly laboratory synthesis, purification, and property measurement for every candidate. Only the most promising virtual candidates proceed to physical synthesis, dramatically reducing material consumption and development costs.

The patent performs preliminary computational evaluation of polymer candidates before physical synthesis. Machine learning models predict key properties (ionic conductivity, transference number, electrochemical stability) for thousands of virtual polymers, allowing researchers to identify the most promising candidates for synthesis. This preliminary screening eliminates the need to synthesize and test low-probability candidates, reducing overall development cost.

The patent implements a feedback loop where machine learning predictions are continuously refined based on experimental validation results. The system generates virtual polymer candidates, predicts their properties using ML models, synthesizes selected candidates, measures actual properties, and uses this experimental data to retrain and improve the ML models. This closed-loop feedback ensures that as discovery speed increases through computational methods, prediction accuracy is maintained and continuously improved through learning from real data.

The patent uses molecular dynamics simulations as an intermediary between machine learning generation and experimental validation. The ML models generate candidate polymers, then molecular dynamics simulations provide a more rigorous computational validation layer that bridges the gap between simplified ML predictions and complex physical reality. This intermediary step filters and refines candidates before experimental synthesis, maintaining accuracy while enabling rapid screening of large candidate spaces.