Machine learning for glass science and engineering: A review
Machine learning for glass science and engineering: A review
复制标题
DOI：
10.1016/j.nocx.2019.100036
复制
发表时间：
2019-07
影响因子：
3.5
通讯作者：
Han Liu;Zipeng Fu;Kai Yang;Xinyi Xu;M. Bauchy
中科院分区：
文献类型：
--
作者：
Han Liu;Zipeng Fu;Kai Yang;Xinyi Xu;M. Bauchy
新眼镜的设计经常受到效率低下的爱迪生式“试错”发现方法的困扰。作为替代途径，材料基因组计划在很大程度上推广了依靠人工智能和机器学习的新方法，以加速发现和优化新型先进材料。在这里，我们回顾了采用机器学习来加速设计具有定制特性的新眼镜的一些最新进展。
The design of new glasses is often plagued by poorly efficient Edisonian “trial-and-error” discovery approaches. As an alternative route, the Materials Genome Initiative has largely popularized new approaches relying on artificial intelligence and machine learning for accelerating the discovery and optimization of novel, advanced materials. Here, we review some recent progress in adopting machine learning to accelerate the design of new glasses with tailored properties.