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.
