الذكاء الاصطناعي يكتشف مواد لا تنقل الحرارة تقريباً سريع

الملخص الأصلي

كما كتبه المؤلفون، بالإنجليزية arXiv:2604.03547

Machine learning has been widely used for predicting material properties. However, efficient prediction of lattice thermal conductivity ($$\kappa_\mathrm{L}$$) remains a long-standing challenge, primarily due to the scarcity of high-quality training data. Here we introduce KappaFormer, a physics-aware Transformer architecture that embeds the harmonic-anharmonic decomposition of $$\kappa_\mathrm{L}$$ within the network. KappaFormer comprises a harmonic branch pre-trained on large-scale elastic property data and an anharmonic branch fine-tuned on limited experimental $$\kappa_\mathrm{L}$$ data, enabling effective knowledge transfer and enhanced generalization. High-throughput screening with KappaFormer identifies multiple candidates with ultralow $$\kappa_\mathrm{L}$$, which are further confirmed by first-principles calculations. Physics interpretability further elucidates the vibrational mechanisms governing thermal transport suppression, linking structural motifs to strong anharmonicity. This study provides a generalizable framework for physics-guided machine learning to accelerate the discovery of new materials.