Machine learning promises to accelerate the material discovery by enabling high-throughput prediction of desirable macro-properties from atomic-level descriptors or structures. However, the limited data available about precise values of these properties have been a barrier, leading to predictive models with limited precision or the ability to generalize. This is particularly true of lattice thermal conductivity (LTC): existing datasets of precise (ab initio, DFT-based) computed values are limited to a few dozen materials with little variability. Based on such datasets, we study the impact of transfer learning on both the precision and generalizability of a deep learning model (ParAIsite). We start from an existing model (MEGNet~\cite{Chen2019}) and show that improvements are obtained by fine-tuning a pre-trained version on different tasks. Interestingly, we also show that a much greater improvement is obtained when first fine-tuning it on a large datasets of low-quality approximations of LTC (based on the AGL model) and then applying a second phase of fine-tuning with our high-quality, smaller-scale datasets. The promising results obtained pave the way not only towards a greater ability to explore large databases in search of low thermal conductivity materials but also to methods enabling increasingly precise predictions in areas where quality data are rare.

本研究解决了材料发现中的数据有限性问题，尤其是在晶格热导率（LTC）方面，现有的精确数据集数量稀缺。通过迁移学习和对深度学习模型（ParAIsite）的微调，论文展示了一种创新的方法，首次通过预训练模型在低质量近似数据集上进行微调，随后在高质量小型数据集上再微调，从而显著提升模型在热导率预测中的精度和可推广性。这一发现对在数据稀缺领域实现更精准的预测具有重要影响。

基于迁移学习的深度学习晶格热导率预测