Materials Informatics for Mechanical Deformation: A Review of Applications and Challenges

Area of Science:

• Materials Science
• Data Science
• Mechanical Engineering

Background:

• Materials informatics methods are increasingly used in materials design and development.
• Classification and regression techniques are applied to mechanical deformation data from simulations and experiments.
• Large datasets from experiments and multiscale modeling simulations can accelerate materials discovery.

Purpose of the Study:

• To review advances in data science applied to mechanical deformation simulations and experiments.
• To focus on the application of these methods to metals and alloys.
• To identify challenges and future prospects in this rapidly growing field.

Main Methods:

• Review of existing literature at the intersection of data science and mechanical deformation.
• Analysis of applications in metals and alloys.
• Identification of trends, challenges, and opportunities.

Main Results:

• Data science methods, including classification and regression, are valuable tools in understanding material behavior.
• Materials informatics can significantly accelerate the discovery of novel materials with excellent mechanical properties.
• The review highlights diverse applications and provides insights into the field's trajectory.

Conclusions:

• The integration of data science with mechanical deformation studies is crucial for advancing materials discovery.
• Further research is needed to address existing challenges and unlock the full potential of materials informatics.
• The field holds significant promise for developing new materials and understanding material behavior.