Machine learning-based accelerated design of fluorphlogopite glass ceramic chemistries with targeted hardness

In this work, we develop and employ an accelerated design strategy using a machine learning algorithm to overcome the challenges for designing a new machinable glass ceramic. The trained machine learning model predicts the specific hardness value for numerous possibilities of processing conditions such as growth temperature and time. We report that the optimized growth parameters of 1200 degrees C and 5 h achieve the highest machinability of 0.4 in the glass ceramic. Furthermore, we predicted the eight most promising candidates containing specific ratios of silicon, magnesium, aluminum, lithium, boron, potassium, barium, and oxygen. Combining machine learning with experimental data enables a systemic and rapid design of a ceramic material while capturing the underlying physics represented in the experimental data.