Deep regressor stacking to learn molecular quantum properties

Yan Li; Zheng Tan; Shiqing Yang

doi:10.1117/12.2674796

2 May 2023 Deep regressor stacking to learn molecular quantum properties

Yan Li, Zheng Tan, Shiqing Yang

Proceedings Volume 12642, Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023); 126422F (2023) https://doi.org/10.1117/12.2674796
Event: Second International Conference on Electronic Information Engineering, Big Data and Computer Technology (EIBDCT 2023), 2023, Xishuangbanna, China

Abstract

A multi-task model with high robustness is indispensable to resolve the inter-target dependence of molecular properties. Beyond the newly proposed stacked single target method, this paper aims to introduce a novel multi-task learning framework in a deep regressor stacking approach to produce a higher predictive accuracy. For the two datasets of interest, OE62 and QM9, we compared the performance of the deep regressor stacking to the single-task model. The new model shows 1 to 4% error reductions compared with the independent models, and the prediction accuracies are consistently improved across different tasks. Further studies on the selective deep stacking scheme show an additional enhancement of the prediction accuracies, indicating a great potential of the deep stacked framework in forecasting the correlated properties.

Citation Download Citation

Yan Li, Zheng Tan, and Shiqing Yang "Deep regressor stacking to learn molecular quantum properties", Proc. SPIE 12642, Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023), 126422F (2 May 2023); https://doi.org/10.1117/12.2674796

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