融合GNN与符号回归，提升材料预测准确率并实现可解释性
Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability
- 整合多种预训练GNN的隐式特征与符号回归新特征
- 在Matbench上使MODNet误差显著降低，多任务提升超40%
- 通过代理模型将隐式特征转为物理可解释公式，适合研发人员使用
本研究提出MatterVial，一种面向材料科学的混合特征学习框架。该框架通过集成结构型（MEGNet）、组分型（ROOST）和等变型（ORB）等多种预训练图神经网络的隐式表示，结合计算高效的近似描述符及符号回归生成的新特征，拓展了特征空间。方法融合传统特征模型的化学透明性与深度学习的预测能力。在Matbench任务中，用此法增强MODNet模型，显著降低误差，性能达到甚至超越多个先进端到端GNN，在多个任务中准确率提升超过40%。集成的可解释模块利用代理模型与符号回归，将GNN提取的隐式特征还原为明确的物理意义公式。该统一框架推动材料信息学发展，提供高性能且透明的工具，符合可解释AI原则，助力更精准、自主的材料发现。
原文摘要 · Abstract (English)
This study introduces MatterVial, an innovative hybrid framework for feature-based machine learning in materials science. MatterVial expands the feature space by integrating latent representations from a diverse suite of pretrained graph neural network (GNN) models including: structure-based (MEGNet), composition-based (ROOST), and equivariant (ORB) graph networks, with computationally efficient, GNN-approximated descriptors and novel features from symbolic regression. Our approach combines the chemical transparency of traditional feature-based models with the predictive power of deep learning architectures. When augmenting the feature-based model MODNet on Matbench tasks, this method yields significant error reductions and elevates its performance to be competitive with, and in several cases superior to, state-of-the-art end-to-end GNNs, with accuracy increases exceeding 40% for multiple tasks. An integrated interpretability module, employing surrogate models and symbolic regression, decodes the latent GNN-derived descriptors into explicit, physically meaningful formulas. This unified framework advances materials informatics by providing a high-performance, transparent tool that aligns with the principles of explainable AI, paving the way for more targeted and autonomous materials discovery.
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