Extended Factorization Machine Annealing for Rapid Discovery of Transparent Conducting Materials

TL;DR

This paper introduces an extended factorization machine annealing method for efficiently discovering transparent conducting materials, demonstrating faster and more accurate results than existing optimization techniques, with applications to multi-objective material design.

Contribution

The paper develops an enhanced FMA framework with novel features like variable binarization and global search activation, improving the efficiency and accuracy of material discovery processes.

Findings

Faster and more accurate than Bayesian optimization and genetic algorithms

Effective in multi-objective optimization for band gap and formation energy

Validated on Kaggle competition data for transparent conductors

Abstract

The development of novel transparent conducting materials (TCMs) is essential for enhancing the performance and reducing the cost of next-generation devices such as solar cells and displays. In this research, we focus on the (Al$_x$Ga$_y$In$_z$)$_2$O$_3$ system and extend the FMA framework, which combines a Factorization Machine (FM) and annealing, to search for optimal compositions and crystal structures with high accuracy and low cost. The proposed method introduces (i) the binarization of continuous variables, (ii) the utilization of good solutions using a Hopfield network, (iii) the activation of global search through adaptive random flips, and (iv) fine-tuning via a bit-string local search. Validation using the (Al$_x$Ga$_y$In$_z$)$_2$O$_3$ data from the Kaggle "Nomad2018 Predicting Transparent Conductors" competition demonstrated that our method achieves faster and more accurate searches than Bayesian optimization and genetic algorithms. Furthermore, its application to multi-objective optimization showed its capability in designing materials by simultaneously considering both the band gap and formation energy. These results suggest that applying our method to larger, more complex search problems and diverse material designs that reflect realistic experimental conditions is expected to contribute to the further advancement of materials informatics.