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Identifying models of dielectric breakdown strength from high-throughput data via genetic programming.

Fenglin Yuan¹, Tim Mueller²

¹Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA.

Scientific Reports

|December 16, 2017

Summary

Genetic programming rapidly predicts dielectric breakdown properties by identifying key material features like band gap and phonon frequency. This approach outperforms others in material science model generation.

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Area of Science:

Materials Science
Computational Materials Science
Data Science

Background:

Predicting material properties accelerates material discovery and design.
Analyzing large material datasets to find predictive models is a key challenge.

Purpose of the Study:

To demonstrate genetic programming for generating dielectric breakdown models.
To identify key features for predicting dielectric properties.

Main Methods:

Utilized genetic programming on a dataset of 82 dielectric materials.
Identified band gap (Eg) and phonon cut-off frequency (ωmax) as crucial features.

Main Results:

Developed new classes of models based on Eg and ωmax.
Genetic programming demonstrated superior performance in model generation compared to other methods.

Conclusions:

Genetic programming is an effective tool for rapid material property prediction.
The identified features (Eg, ωmax) are vital for modeling dielectric breakdown.