Despite an artificial intelligence-assisted modeling of disordered crystals is a widely used and well-tried method of new materials design, the issues of its robustness, reliability, and stability are still not resolved and even not discussed enough. To highlight it, in this work we composed a series of nested intermetallic approximants of quasicrystals datasets and trained various machine learning models on them correspondingly. Our qualitative and, what is more important, quantitative assessment of the difference in the predictions clearly shows that different reasonable changes in the training sample can lead to the completely different set of the predicted potentially new materials. We also showed the advantage of pre-training and proposed a simple yet effective trick of sequential training to increase stability.

翻译：尽管人工智能辅助的无序晶体建模已成为一种广泛使用且经过充分验证的新材料设计方法，但其鲁棒性、可靠性与稳定性问题仍未得到解决，甚至缺乏充分讨论。为阐明此问题，本研究构建了一系列嵌套的金属间化合物准晶近似相数据集，并相应训练了多种机器学习模型。我们对预测差异的定性及（更为重要的）定量评估清晰表明：训练样本中不同的合理变动可能导致预测出的潜在新材料集合完全不同。我们还展示了预训练的优势，并提出一种简单而有效的序列训练技巧以提升稳定性。