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      Designing optical glasses by machine learning coupled with genetic algorithms

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          Abstract

          Engineering new glass compositions has experienced a sturdy tendency to move forward from (educated) trial-and-error to data- and simulation-driven strategies. In this work, we developed a software that combines data-driven predictive models (in this case, neural networks) with a genetic algorithm aimed at designing glass compositions having 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 {\deg}C or less). Two candidate compositions suggested by the combined algorithms were selected and produced in the laboratory. The experimental values of their properties were within the prediction uncertainty of our models. One of the glasses met all the constraints of the work, which supports the proposed framework, whereas the other missed the target by a very small margin. 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 for technological applications.

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          Author and article information

          Journal
          20 August 2020
          Article
          2008.09187

          http://arxiv.org/licenses/nonexclusive-distrib/1.0/

          Custom metadata
          21 pages, 7 figures
          cond-mat.mtrl-sci cond-mat.soft physics.comp-ph

          Condensed matter, Mathematical & Computational physics

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