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Enhancing multi-component alloy composition prediction based on generative adversarial networks and proximal policy

Shun-Li Zhang^1,2, Shao-Bin Bai³, Wen-Jing Shang⁴

¹Department of Information Technology and Engineering, Jinzhong University, Jinzhong, 030619, People's Republic of China. 544891117@qq.com.

Scientific Reports

|April 20, 2026

Summary

This study introduces an AI-driven approach for alloy design, using generative adversarial networks (GAN) and proximal policy optimization (PPO) to overcome data scarcity and reduce costs in complex material systems.

Keywords:

Component prediction Generate adversarial networks (GAN)Multi-component alloys Proximal Policy Optimization (PPO)

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Area of Science:

Materials Science and Engineering
Artificial Intelligence
Computational Materials Science

Background:

Traditional alloy design faces challenges with data scarcity and high experimental costs in complex systems.
Material genomics engineering requires efficient methods for exploring vast compositional spaces.

Purpose of the Study:

To develop an innovative intelligent algorithm for alloy design.
To construct a new research paradigm combining data generation, intelligent optimization, and experimental verification.
To address limitations of traditional alloy design methods.

Main Methods:

A hybrid intelligent algorithm combining generative adversarial networks (GAN) and proximal policy optimization (PPO).
Utilizing GAN for high-quality synthetic alloy sample generation from limited initial data.
Framing alloy composition design as a Markov decision process for PPO optimization.

Main Results:

Effectively alleviated data scarcity by generating tens of thousands of alloy samples from limited experimental data.
Significantly improved search efficiency in high-dimensional combinatorial spaces.
Demonstrated advantages in computational efficiency, data utilization, and component prediction accuracy over traditional methods.

Conclusions:

The proposed method significantly reduces experimental costs and development cycles for high-performance multi-component alloys.
Provides scalable intelligent algorithm tools for material genome engineering.
Offers a novel research methodology for the reverse design of complex material systems with significant scientific and application value.