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A Review of Performance Prediction Based on Machine Learning in Materials Science
Ziyang Fu1,2,3, Weiyi Liu4, Chen Huang1,2,3
1School of Computer Science and Information Engineering, Hubei University, Wuhan 430062, China.
Nanomaterials (Basel, Switzerland)
|September 9, 2022
Summary
Machine learning (ML) accelerates new material discovery by analyzing vast datasets to predict material properties. This approach overcomes limitations of traditional methods, saving time and resources in materials science.
Area of Science:
- Materials Science
- Computational Materials Science
- Data Science
Background:
- The demand for advanced materials is increasing, but practical constraints hinder progress.
- Traditional methods for material design and discovery are time-consuming, resource-intensive, and often inefficient.
Purpose of the Study:
- To review recent advancements in machine learning (ML) for materials science.
- To highlight the application of ML in predicting material properties.
- To discuss challenges and opportunities in this interdisciplinary field.
Main Methods:
- Utilizing machine learning algorithms to analyze material data.
- Identifying correlations between material composition, structure, properties, and processing.
- Leveraging ML for predictive modeling in materials discovery.
Main Results:
- Machine learning offers a powerful new paradigm for exploring the materials field.
- ML effectively predicts material properties by learning from existing data.
- This approach significantly enhances the efficiency of rational material design.
Conclusions:
- Machine learning is revolutionizing materials science by enabling faster and more accurate discovery.
- Addressing key challenges will unlock further potential for ML in materials innovation.
- The integration of ML promises substantial technological and social impact through new materials.
