Deep learning has revolutionized the process of new material discovery, with state-of-the-art models now able to predict material properties based solely on chemical compositions, thus eliminating the necessity for material structures. However, this cost-effective method has led to a trade-off in model accuracy. Specifically, the accuracy of Chemical Composition-based Property Prediction Models (CPMs) significantly lags behind that of Structure-based Property Prediction Models (SPMs). To tackle this challenge, we propose an innovative Teacher-Student (T-S) strategy, where a pre-trained SPM serves as the 'teacher' to enhance the accuracy of the CPM. Leveraging the T-S strategy, T-S CrabNet has risen to become the most accurate model among current CPMs. Initially, we demonstrated the universality of this strategy. On the Materials Project (MP) and Jarvis datasets, we validated the effectiveness of the T-S strategy in boosting the accuracy of CPMs with two distinct network structures, namely CrabNet and Roost. This led to CrabNet, under the guidance of the T-S strategy, emerging as the most accurate model among the current CPMs. Moreover, this strategy shows remarkable efficacy in small datasets. When predicting the formation energy on a small MP dataset comprising merely 5% of the samples, the T-S strategy boosted CrabNet's accuracy by 37.1%, exceeding the enhancement effect of the T-S strategy on the whole dataset.

基于深度学习的新材料发现过程已经取得了革命性的进展，可以基于化学成分预测材料性质而不需要材料结构，但这种成本效益的方法导致了模型准确性的权衡，为了解决这一挑战，我们提出了一种创新的师生策略，在这个策略中，预先训练的基于结构的模型作为“老师”来提高基于化学成分的模型的准确性，利用这种师生策略，T-S CrabNet 成为目前最准确的基于化学成分的模型之一，并且在小数据集上也表现出了显著的效果。

解决材料性质预测中的准确性与成本的权衡问题：一种师生策略