Big-Data-Driven Materials Science and its FAIR Data Infrastructure

TL;DR

This chapter explores big-data-driven materials science, emphasizing the importance of FAIR data infrastructure and AI methods for discovering new materials, while discussing current progress and future challenges.

Contribution

It highlights the critical role of FAIR data principles and AI in advancing big-data-driven materials research, providing a comprehensive review and future outlook.

Findings

FAIR data infrastructure is essential for progress in materials science.

AI methods enable discovery of patterns in large materials datasets.

Recent advances demonstrate the potential of big data in materials discovery.

Abstract

This chapter addresses the forth paradigm of materials research -- big-data driven materials science. Its concepts and state-of-the-art are described, and its challenges and chances are discussed. For furthering the field, Open Data and an all-embracing sharing, an efficient data infrastructure, and the rich ecosystem of computer codes used in the community are of critical importance. For shaping this forth paradigm and contributing to the development or discovery of improved and novel materials, data must be what is now called FAIR -- Findable, Accessible, Interoperable and Re-purposable/Re-usable. This sets the stage for advances of methods from artificial intelligence that operate on large data sets to find trends and patterns that cannot be obtained from individual calculations and not even directly from high-throughput studies. Recent progress is reviewed and demonstrated, and the chapter is concluded by a forward-looking perspective, addressing important not yet solved challenges.