1. Data-Driven Materials Science
- Author
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Himanen, Lauri, Geurts, Amber, Foster, Adam Stuart, Rinke, Patrick, Surfaces and Interfaces at the Nanoscale, Department of Management Studies, Computational Electronic Structure Theory, Department of Applied Physics, Aalto-yliopisto, and Aalto University
- Subjects
MATERIALS INFORMATICS ,databases ,materials science ,QUANTUM-MECHANICS ,INFRASTRUCTURE ,artificial intelligence ,materials ,open innovation ,COMPUTATIONAL MATERIALS SCIENCE ,MACHINE ,COMBINATORIAL ,machine learning ,DESIGN ,open science ,DENSITY-FUNCTIONAL THEORIES ,data science ,NEURAL-NETWORKS ,SEMANTIC WEB - Abstract
openaire: EC/H2020/676580/EU//NoMaD Data-driven science is heralded as a new paradigm in materials science. 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. Multiple factors, including the open science movement, national funding, and progress in information technology, have fueled its development. Such related tools as materials databases, machine learning, and high-throughput methods are now established as parts of the materials research toolset. 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. 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. Key successes and challenges so far are also reviewed, providing a perspective on the future development of the field.
- Published
- 2019