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Data-Driven Materials Science: Status, Challenges, and Perspectives.

Lauri Himanen1, Amber Geurts1,2,3, Adam Stuart Foster1,4,5

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Summary
This summary is machine-generated.

Data-driven materials science leverages large datasets to discover new materials. Challenges like data quality and integration must be addressed for future progress.

Keywords:
artificial intelligencedata sciencedatabasesmachine learningmaterialsmaterials scienceopen innovationopen science

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Area of Science:

  • Materials Science
  • Data Science

Background:

  • Data-driven science is emerging as a transformative paradigm in materials science.
  • Fueled by open science, funding, and IT advancements, it utilizes large datasets for materials discovery.
  • Tools like materials databases, machine learning, and high-throughput methods are integral.

Purpose of the Study:

  • To discuss the historical development and current status of data-driven materials science.
  • To review key successes and persistent challenges in the field.
  • To offer a perspective on the future trajectory of data-driven materials research.

Main Methods:

  • Review of historical evolution of data-driven materials science.
  • Analysis of current state and infrastructure development.
  • Identification and discussion of key successes and challenges.

Main Results:

  • Materials databases, machine learning, and high-throughput methods are established tools.
  • Significant challenges include data veracity, integration of diverse data, longevity, and standardization.
  • A gap exists between academic research and industrial applications.

Conclusions:

  • Data-driven approaches are revolutionizing materials discovery and understanding.
  • Addressing challenges in data management and integration is crucial for continued advancement.
  • Future development requires bridging academic and industrial interests for practical impact.