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The utility of machine learning (ML) techniques in materials science hasaccelerated materials design and discovery. However, the accuracy of ML models- particularly deep neural networks - heavily relies on the quality andquantity of the training data. Data collection methods often have limitationsarising from cost, difficulty, and resource-intensive human efforts. Thus,limited high-quality data, especially for novel materials, poses a significantchallenge in developing reliable ML models. Generative adversarial networks(GANs) offer one solution to augment datasets through synthetic samplegeneration. The present work explores the application of GANs in magnesium (Mg)alloy design, by training two deep neural networks within the structure of aWasserstein GAN to generate new (novel) alloys with desired mechanicalproperties. This data augmentation-based strategy contributes to modelrobustness, particularly in cases where traditional data collection isimpractical. The approach presented may expedite Mg alloy development, througha GAN assisted inverse design approach.

Revolutionising inverse design of magnesium alloys through generative adversarial networks