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In this work, we introduce a polymer discovery platform designed to identifypolymers with tailored properties efficiently, exemplified through thediscovery of high-performance polymer electrolytes. The platform integratesthree core components: a conditioned generative model, validation modules, anda feedback mechanism, creating a self-improving system for material innovation.To demonstrate the efficacy of this platform, it is used to identify polymerelectrolyte materials with high ionic conductivity. A simple conditionalgenerative model, based on the minGPT architecture, can effectively generatecandidate polymers that exhibit a mean ionic conductivity that is significantlygreater than those in the original training set. This approach, coupled withmolecular dynamics simulations for validation and a specifically designedacquisition mechanism, allows the platform to refine its output iteratively.Notably, after the first iteration, we observed an increase in both the meanand the lower bound of the ionic conductivity of the new polymer candidates.The platform's effectiveness is underscored by the identification of 19 polymerrepeating units, each displaying a computed ionic conductivity surpassing thatof Polyethylene Oxide (PEO). The discovery of these polymers validates theplatform's efficacy in identifying potential polymer materials. Acknowledgingcurrent limitations, future work will focus on enhancing modeling techniques,validation processes, and acquisition strategies, aiming for broaderapplicability in polymer science and machine learning.

A Self-Improvable Polymer Discovery Framework Based on Conditional Generative Model