KE: A Knowledge Enhancing Framework for Machine Learning Models

Machine learning models are widely used in science and engineering to predict the properties of materials and solve complex problems. However, training large models can take days and fine-tuning hyperparameters can take months, making it challenging to achieve optimal performance. To address this issue, we propose a Knowledge Enhancing (KE) algorithm that enhances knowledge gained from a lower capacity model to a higher capacity model, enhancing training efficiency and performance. We focus on the problem of predicting the bandgap of an unknown material and present a theoretical analysis and experimental verification of our algorithm. Our experiments show that the performance of our knowledge enhancement model is improved by at least 10.21% compared to current methods on OMDB datasets. We believe that our generic idea of knowledge enhancement will be useful for solving other problems and provide a promising direction for future research.