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From Data Science to Materials Data Science

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Materials Data Science

Part of the book series: The Materials Research Society Series ((MRSS))

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Abstract

In the previous chapter, we saw that data science has ancient roots in many disciplines. For example, it is closely related to statistical theory and mathematics, data visualization, machine learning and artificial intelligence, and computer science and programming. Domain knowledge plays a special role: it is a great source for interesting datasets and problems, which, on the other hand, can also benefit greatly from data analysis. In this chapter, we will give an overview of materials science problems that have been successfully addressed using machine learning approaches, for example. We will also discuss the specifics of materials science data and shed light on the often repeated phrase “turning data into knowledge.”

Data is not information, information is not knowledge, knowledge is not understanding, understanding is not wisdom.

Clifford Stoll (born 1950),

American astronomer, author and teacher

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Notes

  1. 1.

    https://creativecommons.org/licenses/by/4.0/

  2. 2.

    Data is denoted as “FAIR” if it is Findable, Accessible, Interoperable, and Reusable. This is seen as an important prerequisite for making research data machine-readable and machine-actionable, which could also be of great benefit to ML and data science.

  3. 3.

    https://www.nist.gov/mgi

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  1. Institute for Advanced Simulation – Materials Data Science and Informatics (IAS-9), 52068 Aachen, Germany

    Stefan Sandfeld

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  1. Stefan Sandfeld
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Sandfeld, S. (2024). From Data Science to Materials Data Science. In: Materials Data Science. The Materials Research Society Series. Springer, Cham. https://doi.org/10.1007/978-3-031-46565-9_2

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