Glasses have played a critical role in the development of modern civilization and will continue to bring new solutions to global challenges from energy and the environment to healthcare and information/communication technology. To meet the accelerated pace of modern technology delivery, a more sophisticated approach to the design of advanced glass chemistries must be developed to enable faster, cheaper, and better research and development of new glass compositions for future applications. In the spirit of the U.S. Materials Genome Initiative, here we describe an approach for designing new glasses based on a mathematical optimization of composition-dependent glass property models. The models combine known physical insights regarding glass composition-property relationships together with data-driven approaches including machine learning techniques. Using such a combination of physical and empirical modeling approaches, we seek to decode the “glass genome,” enabling the improved and accelerated design of new glassy materials.

眼镜在现代文明的发展中起着至关重要的作用，并将继续为从能源和环境到医疗保健和信息/通信技术的全球挑战带来新的解决方案。为了适应现代技术交付的加快步伐，必须开发出一种更先进的玻璃化学设计方法，以便更快，更便宜，更好地研究和开发用于未来应用的新型玻璃组合物。本着“美国材料基因组计划”的精神，我们在此描述一种基于成分依赖的玻璃特性模型的数学优化来设计新玻璃的方法。该模型将有关玻璃成分-属性关系的已知物理见解与包括机器学习技术在内的数据驱动方法结合在一起。