Efficient autonomous material search method combining ab initio calculations, autoencoder, and multi-objective Bayesian optimization

Abstract

Autonomous material search systems that combine ab initio calculations and Bayesian optimization are very promising for exploring huge material spaces. Setting up an appropriate material search space is necessary for efficient autonomous material search. However, performing the autonomous search within the material space set up using the prepared descriptors is not sufficient to obtain an efficient search, which can be achieved by prioritizing specific descriptors and properties. Here, a material search system that can autonomously search the huge material space while performing multi-objective optimization that considers similarities among elements and emphasizes specific descriptors is proposed. This system has been used for a material exploration of Heusler alloys. The system has successfully proposed several candidate materials with half-metallic properties. The proposed system is very versatile and can be applied to various properties and material systems.

Keywords

• heusler alloy
• machine learning
• ab initio calculation
• half metal
• bayesian optimization