论文标题
通过机器学习设计光眼镜，再加上遗传算法
Designing optical glasses by machine learning coupled with a genetic algorithm
论文作者
论文摘要
工程新的玻璃构图经历了一种坚固的趋势，从（受过教育的）试验和纠正到数据和模拟驱动的策略。在这项工作中，我们开发了一个计算机程序，该程序将数据驱动的预测模型（在这种情况下为神经网络）与遗传算法结合在一起，以设计具有所需属性组合的玻璃组成。首先，我们使用45,302个具有39种不同化学元素的构图的数据集诱导了玻璃过渡温度（$ t_g $）的预测模型，并使用41,225个具有38种不同化学元素的组合物的数据集使用折射率（$ n_d $）。然后，我们使用具有高$ N_D $（1.7或更多）和低$ T_G $（500°C或更少）的玻璃趋势所告知的遗传算法搜索了相关的玻璃成分。在实验室中选择并生产了组合算法建议的两个候选组合物。这些组合物与用于诱导预测模型的数据集有显着不同，表明使用的方法确实能够探索。两只眼镜都符合作品的限制，该作品支持提议的框架。因此，该新工具可立即用于加速新眼镜的设计。这些结果是在机器学习引导设计的新玻璃设计的途径中的垫脚石。
Engineering new glass compositions have experienced a sturdy tendency to move forward from (educated) trial-and-error to data- and simulation-driven strategies. In this work, we developed a computer program that combines data-driven predictive models (in this case, neural networks) with a genetic algorithm to design glass compositions with desired combinations of properties. First, we induced predictive models for the glass transition temperature ($T_g$) using a dataset of 45,302 compositions with 39 different chemical elements, and for the refractive index ($n_d$) using a dataset of 41,225 compositions with 38 different chemical elements. Then, we searched for relevant glass compositions using a genetic algorithm informed by a design trend of glasses having high $n_d$ (1.7 or more) and low $T_g$ (500 °C or less). Two candidate compositions suggested by the combined algorithms were selected and produced in the laboratory. These compositions are significantly different from those in the datasets used to induce the predictive models, showing that the used method is indeed capable of exploration. Both glasses met the constraints of the work, which supports the proposed framework. Therefore, this new tool can be immediately used for accelerating the design of new glasses. These results are a stepping stone in the pathway of machine learning-guided design of novel glasses.
