Materials scientists frequently dream of designing new materials from scratch for use in society. Materials science, the study of the characteristics and applications of materials is a well established discipline that combines chemistry, physics, and engineering research.
In this article, we address what it takes to develop such a search tool for materials. By way of analogy, it is nonetheless still instructive to imagine a Materials “Google”-the Materials Ultimate Search Engine (MUSE). Compared to these e-commerce and social media developments, the field of data-driven materials science is still under construction. The public debate of these terms is currently dominated by internet giants like Google, Amazon, and Facebook who also lead the technological development of data infrastructures, algorithms, and analysis tools. Data-driven invokes associations with big data, data management, open data and artificial intelligence (e.g., machine learning). In this perspective article, we review the current state of data-driven materials science with a focus on materials data infrastructures. Key successes and challenges so far are also reviewed, providing a perspective on the future development of the field.
In this perspective article, the historical development and current state of data-driven materials science, building from the early evolution of open science to the rapid expansion of materials data infrastructures are discussed. However, there are a variety of challenges that impede progress in data-driven materials science: data veracity, integration of experimental and computational data, data longevity, standardization, and the gap between industrial interests and academic efforts. Such related tools as materials databases, machine learning, and high-throughput methods are now established as parts of the materials research toolset. Multiple factors, including the open science movement, national funding, and progress in information technology, have fueled its development. In this field, data is the new resource, and knowledge is extracted from materials datasets that are too big or complex for traditional human reasoning-typically with the intent to discover new or improved materials or materials phenomena. Data-driven science is heralded as a new paradigm in materials science.
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