Designing optical glasses by machine learning coupled with a genetic algorithm

Abstract

Engineering new glass compositions have experienced a sturdy tendency to move forward from (educated) trial-and-error to data- and simulation-driven strategies. In this work, we developed a computer program that combines data-driven predictive models (in this case, neural networks) with a genetic algorithm to design glass compositions with desired combinations of properties. First, we induced predictive models for the glass transition temperature ($T_g$) using a dataset of 45,302 compositions with 39 different chemical elements, and for the refractive index ($n_d$) using a dataset of 41,225 compositions with 38 different chemical elements. Then, we searched for relevant glass compositions using a genetic algorithm informed by a design trend of glasses having high $n_d$ (1.7 or more) and low $T_g$ (500 °C or less). Two candidate compositions suggested by the combined algorithms were selected and produced in the laboratory. These compositions are significantly different from those in the datasets used to induce the predictive models, showing that the used method is indeed capable of exploration. Both glasses met the constraints of the work, which supports the proposed framework. Therefore, this new tool can be immediately used for accelerating the design of new glasses. These results are a stepping stone in the pathway of machine learning-guided design of novel glasses.

arXiv comment24 pages, 8 figures. Updated with journal reference

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Cited by in corpus (5)

• ViscNet: Neural network for predicting the fragility index and the temperature-dependency of viscosity
• Predicting and interpreting oxide glass properties by machine learning using large datasets
• GlassNet: a multitask deep neural network for predicting many glass properties
• Predicting Oxide Glass Properties with Low Complexity Neural Network and Physical and Chemical Descriptors
• Unveiling the Glass Veil: Elucidating the Optical Properties in Glasses with Interpretable Machine Learning