Design of Recyclable Plastics with Machine Learning and Genetic Algorithm.

IF 5.3 2区化学 Q1 CHEMISTRY, MEDICINAL Journal of Chemical Information and Modeling Pub Date : 2024-12-23 Epub Date: 2024-12-03 DOI:10.1021/acs.jcim.4c01530

Chureh Atasi, Joseph Kern, Rampi Ramprasad

引用次数: 0

Abstract

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.

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基于机器学习和遗传算法的可回收塑料设计。

我们提出了一种人工智能指导的方法来设计耐用和化学可回收的开环聚合（ROP）类聚合物。该方法采用遗传算法（GA）设计新单体，然后利用虚拟正向合成（VFS）生成近100万个ROP聚合物。用于预测热、热力学和机械性能的机器学习模型──对特定应用性能和可回收性至关重要──用于指导遗传算法优选聚合物。我们提出了聚苯乙烯（PS）的潜在替代聚合物，以较低的估计合成复杂性实现所有性能目标。

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来源期刊

Journal of Chemical Information and Modeling 化学-化学综合

CiteScore

9.80

自引率

10.70%

发文量

529

审稿时长

1.4 months

期刊介绍： The Journal of Chemical Information and Modeling publishes papers reporting new methodology and/or important applications in the fields of chemical informatics and molecular modeling. Specific topics include the representation and computer-based searching of chemical databases, molecular modeling, computer-aided molecular design of new materials, catalysts, or ligands, development of new computational methods or efficient algorithms for chemical software, and biopharmaceutical chemistry including analyses of biological activity and other issues related to drug discovery. Astute chemists, computer scientists, and information specialists look to this monthly’s insightful research studies, programming innovations, and software reviews to keep current with advances in this integral, multidisciplinary field. As a subscriber you’ll stay abreast of database search systems, use of graph theory in chemical problems, substructure search systems, pattern recognition and clustering, analysis of chemical and physical data, molecular modeling, graphics and natural language interfaces, bibliometric and citation analysis, and synthesis design and reactions databases.