Generative Inversion for Property-Targeted Materials Design: Application to Shape Memory Alloys

TL;DR

This paper presents a GAN-based inverse design framework for creating high-performance shape memory alloys, validated by synthesizing NiTi-based alloys with superior properties.

Contribution

Introduces a generative adversarial network inversion method coupled with property prediction for targeted alloy design, validated through experimental synthesis.

Findings

Synthesized NiTi-based SMAs with high transformation temperatures and large mechanical work.

Achieved a NiTiHfZr alloy with a transformation temperature of 404°C and work output of 9.9 J/cm³.

Demonstrated GAN inversion as an effective tool for property-targeted alloy discovery.

Abstract

The design of shape memory alloys (SMAs) with high transformation temperatures and large mechanical work output remains a longstanding challenge in functional materials engineering. Here, we introduce a data-driven framework based on generative adversarial network (GAN) inversion for the inverse design of high-performance SMAs. By coupling a pretrained GAN with a property prediction model, we perform gradient-based latent space optimization to directly generate candidate alloy compositions and processing parameters that satisfy user-defined property targets. The framework is experimentally validated through the synthesis and characterization of five NiTi-based SMAs. Among them, the Ni$_{49.8}$Ti$_{26.4}$Hf$_{18.6}$Zr$_{5.2}$ alloy achieves a high transformation temperature of 404 $^\circ$C, a large mechanical work output of 9.9 J/cm$^3$, a transformation enthalpy of 43 J/g , and a thermal hysteresis of 29 {\deg}C, outperforming existing NiTi alloys. The enhanced performance is attributed to a pronounced transformation volume change and a finely dispersed of Ti$_2$Ni-type precipitates, enabled by sluggish Zr and Hf diffusion, and semi-coherent interfaces with localized strain fields. This study demonstrates that GAN inversion offers an efficient and generalizable route for the property-targeted discovery of complex alloys.