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Artificial intelligence-driven approaches for materials design and discovery.

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Computational materials design, particularly inverse design, accelerates the discovery of new functional materials. Artificial intelligence and deep generative models are revolutionizing this field, moving beyond traditional trial-and-error methods for enhanced technological relevance.

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Artificial Intelligence

Background:

  • Traditional materials design relies on inefficient trial-and-error methods.
  • Computational techniques, especially artificial intelligence (AI), are transforming materials discovery.
  • Inverse design offers a promising approach to engineer materials with desired properties.

Purpose of the Study:

  • To review key computational advances in materials design over recent decades.
  • To trace the evolution of materials design techniques.
  • To highlight the shift towards AI-driven inverse generation.

Main Methods:

  • Review of high-throughput forward machine learning.
  • Analysis of evolutionary algorithms.
  • Examination of advanced AI strategies like reinforcement learning and deep generative models.

Main Results:

  • Demonstration of a paradigm shift from conventional screening to inverse generation.
  • Identification of deep generative models as key drivers of this shift.
  • Overview of progress in computational materials design.

Conclusions:

  • AI-powered inverse design is reshaping functional materials development.
  • Deep generative models are central to current and future materials design.
  • The field faces challenges but holds significant promise for technological innovation.