Prediction of repeat unit of optimal polymer by Bayesian optimization
Prediction of repeat unit of optimal polymer by Bayesian optimization
复制标题
通过贝叶斯优化预测最佳聚合物的重复单元
作者：
Takuya Minami;M. Kawata;T. Fujita;Katsumi Murofushi;H. Uchida;Kazuhiro Omori;Y. Okuno
采用贝叶斯优化方法加快了功能高分子材料的设计过程，大大减少了设计过程中的试验次数。与随机选择相比，优化过程加快了40倍以上以找到目标聚合物。利用目标函数预测概率分布的标准差，成功地提高了优化效率。该方法的性能对于分析中的数据集大小是稳健的;即使对于小的训练数据集也可以找到目标聚合物。该方法为高性能聚合物的设计提供了一个很有前途的工具，在聚合物工业中具有广泛的应用前景。
Design processes of functional polymers were accelerated by adopting the Bayesian optimization; the number of trials in the process was substantially reduced. The optimization process was more than forty time accelerated to find out the target polymer compared to the random selection. The optimization efficiency was found to be successfully improved by utilizing the standard deviation of predicted probability distribution of objective function. The performance of the method was robust for dataset size in the analysis; the target polymer could be found even for a small training dataset. The proposed method is a promising tool for the high-performance polymer design, and a wide range of its applications will be expected in the polymer industry.