Predicting boron coordination in multicomponent borate and borosilicate glasses using analytical models and machine learning

Accurate prediction of boron coordination in multicomponent glasses is critical in glass science and technology as it strongly affects the properties of borate and borosilicate glasses. We have collected a dataset containing 657 glasses from literature with boron coordination values and developed models using analytical functions based on the well accepted Dell, Xiao and Bray model. Good prediction of boron coordination with a R-2 value higher than 0.8 was obtained. The large variation of boron coordination from experiments, originated from sample preparations and characterizations, led to difficulties in obtaining models with better prediction performances. Various machine learning (ML) algorithms were evaluated and a slightly better prediction performance was observed; however, interpretation of the ML models is less straight forward. This study developed various models capable of providing quantitative boron coordination predictions, providing insights into its structural roles in multi component glasses, and suggesting fruitful areas for future research.

Automated summary

Accurately predicting boron coordination in multicomponent glasses is crucial in glass science and technology, with various models developed to achieve this. Experimental variations in boron coordination present challenges, but machine learning algorithms show potential for slightly better prediction performance, offering valuable insights for future research.