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Deep learning has revolutionized the process of new material discovery, withstate-of-the-art models now able to predict material properties based solely onchemical 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 PredictionModels (CPMs) significantly lags behind that of Structure-based PropertyPrediction Models (SPMs). To tackle this challenge, we propose an innovativeTeacher-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 CrabNethas risen to become the most accurate model among current CPMs. Initially, wedemonstrated the universality of this strategy. On the Materials Project (MP)and Jarvis datasets, we validated the effectiveness of the T-S strategy inboosting the accuracy of CPMs with two distinct network structures, namelyCrabNet 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, thisstrategy shows remarkable efficacy in small datasets. When predicting theformation energy on a small MP dataset comprising merely 5% of the samples, theT-S strategy boosted CrabNet's accuracy by 37.1%, exceeding the enhancementeffect of the T-S strategy on the whole dataset.

Addressing the Accuracy-Cost Tradeoff in Material Property Prediction: A Teacher-Student Strategy