Atasi Chureh, Kern Joseph, Ramprasad Rampi
School of Materials Science and Engineering, College of Engineering, Georgia Institute of Technology, 771 Ferst Dr. N.W., Atlanta, Georgia 30318, United States.
J Chem Inf Model. 2024 Dec 23;64(24):9249-9259. doi: 10.1021/acs.jcim.4c01530. Epub 2024 Dec 3.
We present an artificial intelligence-guided approach to design durable and chemically recyclable ring-opening polymerization (ROP) class polymers. This approach employs a genetic algorithm (GA) that designs new monomers and then utilizes virtual forward synthesis (VFS) to generate almost a million ROP polymers. Machine learning models to predict thermal, thermodynamic, and mechanical properties─crucial for application-specific performance and recyclability─are used to guide the GA toward optimal polymers. We present potential substitute polymers for polystyrene (PS) that achieve all property targets with low estimated synthetic complexity.
我们提出了一种人工智能引导的方法来设计耐用且可化学回收的开环聚合（ROP）类聚合物。这种方法采用遗传算法（GA）来设计新的单体，然后利用虚拟正向合成（VFS）生成近百万种ROP聚合物。机器学习模型用于预测热性能、热力学性能和机械性能（这些性能对于特定应用的性能和可回收性至关重要），以引导遗传算法找到最优聚合物。我们展示了聚苯乙烯（PS）的潜在替代聚合物，这些聚合物以较低的估计合成复杂度实现了所有性能目标。
