Machine learning-driven new material discovery.

Area of Science:

• Materials Science
• Computer Science
• Data Science

Background:

• Traditional trial-and-error methods are insufficient for the rapid discovery of novel materials needed for technological progress.
• Machine learning (ML) presents a promising paradigm shift in accelerating material discovery and design.
• The integration of ML with computational methods like Density Functional Theory (DFT) is gaining traction.

Purpose of the Study:

• To review the current research landscape of applying machine learning in material discovery.
• To outline key components of ML-driven material discovery, including data preprocessing, feature engineering, algorithms, and validation.
• To propose the synergistic use of ML with DFT calculations for enhanced material exploration.

Main Methods:

• Review of existing literature and research methodologies in ML for material science.
• Analysis of data preprocessing techniques, feature engineering strategies, and relevant ML algorithms.
• Discussion of cross-validation procedures for robust model evaluation.
• Integration of ML with established computational chemistry methods like DFT.

Main Results:

• Machine learning demonstrates significant potential and advantages in various aspects of material science.
• Applications include property prediction, direct material discovery, and inverse design.
• ML aids in identifying materials for specific applications, such as corrosion detection.

Conclusions:

• Machine learning offers a powerful and efficient alternative to traditional methods for new material exploration.
• The synergy between ML and DFT calculations holds substantial promise for future material discovery.
• ML-driven approaches are poised to revolutionize technological advancements through accelerated material innovation.