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Area of Science:

  • Materials Science
  • Artificial Intelligence
  • Computational Chemistry

Background:

  • Artificial intelligence (AI) has emerged as a transformative force in materials discovery.
  • AI facilitates the prediction of material properties, formability, and guides experimental synthesis.
  • Advancements are driven by increasing database sizes and enhanced computing power.

Purpose of the Study:

  • To systematically review AI-empowered materials science, focusing on both new material discovery and cognition of existing materials.
  • To reflect on the advanced design of intelligent systems for materials discovery, synthesis, prediction, and validation.
  • To outline future directions for AI in materials science.

Main Methods:

  • Review of current AI methodologies in materials science.
  • Analysis of AI system designs incorporating data, machine learning, and automated laboratories.
  • Summarization of strategies for developing high-performance AI systems for materials.

Main Results:

  • AI significantly accelerates the discovery and design of novel materials.
  • AI enhances the in-depth understanding and cognition of existing materials.
  • Intelligent systems are being developed for comprehensive materials lifecycle management.

Conclusions:

  • AI is a powerful tool for advancing materials science, enabling both rapid discovery and deeper understanding.
  • Future AI systems in materials science will likely feature more sophisticated integration of data and automation.
  • Continued development of AI is crucial for unlocking new material potentials and applications.