Artificial intelligence-driven approaches for materials design and discovery

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.