Designing optical glasses by machine learning coupled with a genetic algorithm
| Autor: | Daniel R. Cassar, Gisele G. Santos, Edgar Dutra Zanotto |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
010302 applied physics
Condensed Matter - Materials Science Materials science Computer program Artificial neural network Process Chemistry and Technology Materials Science (cond-mat.mtrl-sci) FOS: Physical sciences 02 engineering and technology Condensed Matter - Soft Condensed Matter Computational Physics (physics.comp-ph) 021001 nanoscience & nanotechnology 01 natural sciences Surfaces Coatings and Films Electronic Optical and Magnetic Materials Stepping stone 0103 physical sciences Genetic algorithm Materials Chemistry Ceramics and Composites Soft Condensed Matter (cond-mat.soft) 0210 nano-technology Glass transition Physics - Computational Physics Algorithm |
| DOI: | 10.48550/arxiv.2008.09187 |
| Popis: | 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 {\deg}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. Comment: 24 pages, 8 figures. Updated with journal reference |
| Databáze: | OpenAIRE |
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