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Data-driven discovery of formulas by symbolic regression

  • The Machine Learning Revolution in Materials Research
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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.

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Authors and Affiliations

  1. Materials Genome Institute, Shanghai University, China

    Sheng Sun, Runhai Ouyang, Bochao Zhang & Tong-Yi Zhang

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  1. Sheng Sun
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  2. Runhai Ouyang
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  3. Bochao Zhang
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  4. Tong-Yi Zhang
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Correspondence to Sheng Sun.

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Sun, S., Ouyang, R., Zhang, B. et al. Data-driven discovery of formulas by symbolic regression. MRS Bulletin 44, 559–564 (2019). https://doi.org/10.1557/mrs.2019.156

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