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Data-driven discovery of formulas by symbolic regression

Published online by Cambridge University Press:  12 July 2019

Sheng Sun ,
Runhai Ouyang ,
Bochao Zhang  and
Tong-Yi Zhang
Sheng Sun
Affiliation:
Materials Genome Institute, Shanghai University, China; mgissh@t.shu.edu.cn
Runhai Ouyang
Affiliation:
Materials Genome Institute, Shanghai University, China; rouyang@shu.edu.cn
Bochao Zhang
Affiliation:
Materials Genome Institute, Shanghai University, China; mgizbc@shu.edu.cn
Tong-Yi Zhang
Affiliation:
Materials Genome Institute, Shanghai University, China; zhangty@shu.edu.cn
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Abstract

Discovering knowledge from data is a quantum jump from quantity to quality, which is the characteristic and the spirit of the development of science. Symbolic regression (SR) is playing a greater role in the discovery of knowledge from data, specifically in this era of exponential data growth, because SRs are able to discover mathematical formulas from data. These formulas may provide scientifically meaningful models, especially when combined with domain knowledge. This article provides an overview of SR applications in the field of materials science and engineering. Integrating domain knowledge with SR is the key and a crucial approach, which allows gaining knowledge from data quickly, accurately, and scientifically. In the data-driven paradigm, SR allows for uncovering the underlying mechanisms of materials behavior, properties, and functions, in a wide range of areas from basic academic research to industrial applications, including experiments and computations, by providing explicit interpretable models from data, in comparison with other machine-learning “black-box” models. SR will be a powerful tool for rational and automatic materials development.

Keywords

symbolic regressiongenetic programminggrammar-guided genetic programminggrammatical evolutionmathematic formulas
Type
The Machine Learning Revolution in Materials Research
Information
MRS Bulletin , Volume 44 , Issue 7: The Machine Learning Revolution in Materials Research , July 2019 , pp. 559 - 564
Copyright
Copyright © Materials Research Society 2019 

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