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Materials science is entering a data-driven era, necessitating new frameworks for innovation. This study reviews advances in machine learning, databases, and applications for discovering novel inorganic materials.

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Area of Science:

  • Materials Science
  • Computational Science
  • Data Science

Background:

  • The field of materials science is increasingly data-intensive, driven by advancements in experimental and computational methods.
  • This shift necessitates the development of robust frameworks for data-driven scientific research and material innovation.
  • The transition marks the fourth paradigm of scientific research, emphasizing data as a core component.

Purpose of the Study:

  • To critically discuss current advances in data-driven materials discovery.
  • To focus on frameworks, machine learning algorithms, material-specific databases, and descriptors.
  • To explore targeted applications in inorganic materials innovation.

Main Methods:

  • Review of data-intensive strategies and machine-learning algorithms.
  • Analysis of material databases, data generation, and management tools.
  • Examination of molecular descriptors used in data-driven processes.

Main Results:

  • Discussion of frameworks for rationalizing data-driven material innovation.
  • In-depth review of key subdisciplines: algorithms, databases, and descriptors.
  • Exploration of applications in energy, environment, electronics, and magnetic materials.

Conclusions:

  • Synergy between materials science, computational tools, and mathematics underpins data-driven paradigms.
  • Highlights opportunities and challenges in advancing data-driven material innovation.
  • Emphasizes the need for authoritative frameworks to guide future research.