用符号回归发现二维材料缺陷的相互作用规律
Symbolic regression for defect interactions in 2D materials
- 采用深度符号回归算法SEGVAE自动推导缺陷性质的解析公式
- 在多个数据集上达到与图神经网络相当的预测精度
- 适合需要可解释性模型的材料科学与物理研究者
机器学习已在各科学领域广泛应用。基于神经网络从数据中提取特征并进行推断的方法虽精度高，但存在可解释性差等缺陷。符号回归能发现描述数据的解析方程，生成可解释且泛化能力强的模型。随着神经网络技术发展，该方法重获活力，尤其以结果可解释为核心优势。本文将深度符号回归算法SEGVAE应用于二维材料缺陷性质的建模，结果表明其性能可与当前最先进的图神经网络方法相当，甚至在某些情况下完全一致。研究还探讨了此类方法在自然科学中的适用性。
原文摘要 · Abstract (English)
Machine learning models have become firmly established across all scientific fields. Extracting features from data and making inferences based on them with neural network models often yields high accuracy; however, this approach has several drawbacks. Symbolic regression is a powerful technique for discovering analytical equations that describe data, providing interpretable and generalizable models capable of predicting unseen data. Symbolic regression methods have gained new momentum with the advancement of neural network technologies and offer several advantages, the main one being the interpretability of results. In this work, we examined the application of the deep symbolic regression algorithm SEGVAE to determine the properties of two-dimensional materials with defects. Comparing the results with state-of-the-art graph neural network-based methods shows comparable or, in some cases, even identical outcomes. We also discuss the applicability of this class of methods in natural sciences.
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