Glassomics: An omics approach toward understanding glasses through modeling, simulations, and artificial intelligence

Glass science, like other materials domains, has been advancing at a rapid pace during the last few decades thanks to sophisticated experimental techniques, simulation methods, and computing capabilities. Specifically, the availability of a plethora of information about glass compositions and properties, synthesis and experimental protocols, and large-scale and multiscale modeling has enabled a unified approach to study glasses through a bottom-up approach. Here, taking inspiration from the omics approach of biological science, we propose glassomics to study glasses in a holistic fashion. We discuss how the advances in artificial intelligence, experiments, and simulations allow a high-throughput screening of glasses while scanning the entire periodic table-based compositions. We discuss how glassomics can provide a comprehensive understanding of complex interrelationships of composition, structure, process, and properties of glasses through simulations, machine learning, and natural language processing by reviewing the latest trends in the field. Finally, we also discuss some of the outstanding challenges in the field and some of the potential approaches toward addressing them.

Automated summary

Glass science has made rapid progress in recent decades, thanks to advanced experimental techniques, simulation methods, and computing capabilities. Glassomics, inspired by the omics approach in biological science, provides a holistic way to study glasses. By utilizing artificial intelligence, experiments, and simulations, glassomics allows high-throughput screening of glasses based on the entire periodic table. This approach offers a comprehensive understanding of the composition, structure, process, and properties of glasses through simulations, machine learning, and natural language processing.