The application of artificial neural networks (ANN) in the synthesis of polystyrene via ARGET-ATRP was presented for the first time. In this research, it was utilized a deterministic modeling to train ANN operating in the direct and inverse way, that is, with the possibility of identifying reaction conditions from target polymer average properties and vice versa. Prediction deviations by ANN were less than 20% in all cases, and for monomer conversion and dispersity, these values did not exceed 10%. This approach provides an alternative possibility for intelligent control of the dispersity and degree of polymerization. It was exposed that the control strategies learned are robust and can be transferred to similar ARGET-ATRP reaction configurations. Moreover, it was demonstrated that the inverse ANN remains an outstanding alternative to overcome the limitations of traditional deterministic modeling, in which direct and rapid prediction of reaction conditions from the polymer properties as input parameters is difficult. Hence, we believe this work represents a bottom line for the use of modern techniques of artificial intelligence in the controlled synthesis of polymers.

首次介绍了人工神经网络在ARGET-ATRP合成聚苯乙烯中的应用。在这项研究中，利用确定性模型来训练ANN以直接和逆向方式进行操作，也就是说，可以根据目标聚合物的平均性质来确定反应条件，反之亦然。在所有情况下，基于ANN的预测偏差均小于20％，并且对于单体转化率和分散度，这些值均不超过10％。这种方法为智能控制分散度和聚合度提供了另一种可能性。公开了所掌握的控制策略是可靠的，并且可以转移到类似的ARGET-ATRP反应配置中。此外，结果表明，逆人工神经网络仍然是克服传统确定性建模方法的局限性的替代方法，在传统确定性建模方法中，难以根据聚合物特性直接或快速地预测反应条件作为输入参数。因此，我们相信这项工作代表了在控制合成聚合物中使用现代人工智能技术的底线。