Machine learning for design, phase transformation and mechanical properties of alloys

Machine learning is now applied in virtually every sphere of life for data analysis and interpretation. The main strengths of the method lie in the relative ease of the construction of its structures and its ability to model complex non-linear relationships and behaviours. While application of existing materials have enabled significant technological advancement there are still needs for novel materials that will enable even greater achievement at lower cost and higher effectiveness. The physics underlining the phenomena involved in materials processing and behaviour however still pose considerable challenge and yet require solving. Machine learning can facilitate the achievement of these new aspirations and desires by learning from existing knowledge and data to fill in gaps that have so far been intractable for various reasons including cost and time. This paper reviews the applications of machine learning to various aspects of materials design, processing, characterisation, and some aspects of fabrication and environmental impact evaluation.

Automated summary

Machine learning is widely applied in various aspects of life for data analysis, modeling complex relationships and behaviors. The development of novel materials is crucial for achieving greater technological advancement at lower cost and higher effectiveness. Challenges in physics underlying materials processing and behaviors can be addressed by utilizing machine learning to learn from existing knowledge and data to achieve new aspirations and desires.