DSMR: an AI framework for exploring combinations of data and algorithm to overcome efficiency-accuracy trade-off

Abstract

Machine learning models demonstrate remarkable capabilities in predicting properties of novel material. The optimal model can theoretically be obtained through an exhaustive search of data subsets, algorithms, and hyperparameters. However, the fundamental challenge lies in identifying the most efficient pathway through this immense search space. In this paper, we address this challenge by proposing an active learning-based data screening and model retrieval framework, which can develop enhanced models based on internal data while incorporating additional external data to further improve model performance. Systematic validation studies were conducted using four datasets, comprising both classification and regression data. Superior models were obtained within 10 iterative cycles for all cases, achieving a 3.3%-10.3% improvement compared to state-of-the-art results in current literature. Among the results, the framework reduced modeling error by 10.3% for AlCoCrCuFeNi hardness internal data and achieved a more significant error reduction of 42.6% through the integration of additional external hardness data. The framework achieves an ideal balance between computational efficiency and predictive accuracy while enabling deeper data exploration, with its low-code implementation and user-friendly characteristics making it a promising tool for materials design.

Keywords

Active learning / data enhancement / model optimization

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