新型快速高精度主动学习算法的开发:以MAX相晶体的材料力学性能预测为例

Development of a novel rapid and high-precision active learning algorithm:A case study of the prediction of the mechanical properties of MAX phase crystals

近年来,MAX相晶体由于独特的纳米层状的晶体结构具有自润滑、高韧性、导电性等优点,成为全球的研究热点之一.其中M2AX相晶体兼具陶瓷和金属化合物的性能,同时具有抗热震性、高韧性、导电性和导热性,但是由于该类材料的单相样品实验制备比较困难,从而限制了其发展.主动学习是一种利用少量标记样本可以达到较好预测性能的机器学习方法,本文将高效全局优化算法与残差主动学习回归算法相结合,提出了一种改良的主动学习选择策略RS-EGO,基于169个M2AX相晶体的数据集,对M2AX相晶体的体模量、杨氏模量与剪切模量进行建模与预测寻优,通过计算模拟的方式来探索材料性能从而减少无效的验证实验.结果发现,RS-EGO在快速寻找最优值的同时具有较好的预测能力,综合性能要优于两种原始选择策略,也更适合样本量较少的材料性能预测问题,同时选择不同的结合参数会影响改良算法的优化方向.通过在两个公开数据集上运用改良算法证明了其有效性,并给出了结合参数的选择,设计不同结合参数下的模型实验,进一步探究不同参数对模型优化方向的影响.

In recent years,MAX phase crystals have emerged as a prominent area of global research due to their unique nanolayered crystal structure,which provides advantages such as self-lubrication,high tenacity,and electrical conductivity.M2AX phase crystals have properties associated with both ceramic and metal compounds,such as thermal shock resistance,high tenacity,electrical conductivity,and thermal conductivity.However,research on these materials is challenging due to the difficulty in preparing single-phase samples for such materials.Active learning is a machine learning method that uses a small number of labeled samples to achieve high prediction performance.This paper proposes an improved active learning selection strategy,called RS-EGO,based on the combination of efficient global optimization and residual active learning regression according to their characteristics after analyzing the sampling strategies of active learning and efficient global optimization algorithms.The proposed strategy is applied to predict and determine the optimal values of the bulk modulus,Young’s modulus,and shear modulus based on a dataset of 169 M2AX phase crystals.This analysis is conducted using computational simulations to explore the material properties,reducing the need for ineffective validation experiments.The results showed that RS-EGO has good prediction ability and can rapidly find the optimal value.Its comprehensive performance is not only better than the two original selection strategies but is also more suitable for material property prediction problems with limited sample data.The choice of various parameter combinations can influence the direction of optimization of this improved algorithm.RS-EGO was applied to two publicly available datasets(one with a sample size of 103 and the other with a sample size of 1836),and both analyses achieved smaller root mean square errors,smaller opportunity costs,and larger decidable coefficient values,which demonstrates the effectiveness of the algorithm for both small and large sample datasets.A range of parameter combinations broader than previous experiments is explored,with experiments designed to explore the regularity of the contribution of different parameters to different optimization directions of the model.The results show that larger parameter values cause the algorithm to behave more like the efficient global optimization algorithm with a better ability to find the optimal value.Conversely,the closer the model is to the residual active learning regression algorithm with a better accuracy prediction performance,the better will be its prediction performance.Thus,the focus of the two capabilities can be adjusted by choosing the combination of parameters appropriately.