Property-Oriented Material Design Based on a Data-Driven Machine Learning Technique.

Area of Science:

• Materials Science
• Computational Science
• Data Science

Background:

• Property-oriented material design is a key goal for scientists.
• Machine learning (ML) is emerging as a powerful tool in material design.
• ML offers faster, lower-cost material property prediction by using statistical analysis instead of physical equations.

Purpose of the Study:

• To discuss challenges in data quantity and diversity for ML-based material design.
• To explore various approaches for overcoming data limitations.
• To highlight the critical role of data in advancing ML for materials.

Main Methods:

• Review of current challenges in data availability and diversity for ML models.
• Discussion of strategies to improve data for materials informatics.
• Exploration of techniques like high-throughput calculations and database construction.

Main Results:

• Lack of sufficient and diverse data hinders practical ML applications in material design.
• Existing methods like high-throughput calculations and database construction show promise.
• Development of better descriptors is essential for improving ML model performance.

Conclusions:

• Data quantity and quality are paramount for successful ML-driven material design.
• A multi-faceted approach involving data generation, curation, and algorithmic improvements is needed.
• Focusing on data itself will be key to unlocking the full potential of ML in materials science.