High-throughput inverse design and Bayesian optimization of functionalities: spin splitting in two-dimensional...

Area of Science:

• Condensed Matter Physics
• Materials Science
• Computational Materials Science

Background:

• Spintronic devices require materials exhibiting spin splitting (SS).
• Ab initio calculations are crucial for predicting material properties.
• Two-dimensional (2D) materials offer unique properties for advanced applications.

Purpose of the Study:

• To build a comprehensive database of ab initio calculated spin splitting in 2D materials.
• To propose and demonstrate a workflow for materials design using inverse design and Bayesian optimization.
• To identify 2D materials with specific spin splitting characteristics for spintronics.

Main Methods:

• Utilizing density functional theory (DFT) for ab initio calculations.
• Implementing an inverse design approach combined with Bayesian inference optimization.
• Screening the C2DB database for 2D materials with spin splitting.

Main Results:

• A database of 358 2D materials classified by spin splitting type at valence and/or conduction bands.
• Identification of 2D materials with potential for spintronics applications.
• Demonstration of a workflow for rationalized 2D material design.

Conclusions:

• The developed workflow enables efficient materials discovery for spintronics.
• The database serves as a valuable resource for researchers in the field.
• The methodology is adaptable for designing materials with other desired properties.