标题： SA-GAT-SR：用于高保真材料属性预测的自适应图注意力网络与符号回归 显示英文标题

标题： SA-GAT-SR: Self-Adaptable Graph Attention Networks with Symbolic Regression for high-fidelity material property prediction

摘要： 近年来，机器学习的进展展示了深度学习方法的巨大效用，特别是图神经网络（GNNs）在材料科学中的应用。这些方法已经成为高通量预测材料属性的强大工具，为传统的第一性原理计算提供了有说服力的增强和替代方案。尽管社区主要集中在开发越来越复杂和通用的模型以提高预测准确性，但此类方法往往缺乏物理可解释性和对材料行为的洞察。 在这里，我们介绍了一种新的计算范式，即结合符号回归的自适应图注意网络（SA-GAT-SR），它将GNNs的预测能力与符号回归的解释能力协同结合。我们的框架采用了一种自适应编码算法，该算法能够自动识别和调整注意力权重，从而从一个庞大的180维特征空间中筛选出关键特征，同时保持O(n)的计算扩展性。 集成的SR模块随后将这些特征提炼成紧凑的解析表达式，明确揭示了具有量子力学意义的关系，与依赖于第一性原理计算特征的传统SR实现相比，实现了23倍的加速。 这项工作提出了一种在计算材料科学中的新框架，弥合了预测准确性和物理可解释性之间的差距，为材料行为提供了有价值的物理见解。 显示英文摘要

摘要： Recent advances in machine learning have demonstrated an enormous utility of deep learning approaches, particularly Graph Neural Networks (GNNs) for materials science. These methods have emerged as powerful tools for high-throughput prediction of material properties, offering a compelling enhancement and alternative to traditional first-principles calculations. While the community has predominantly focused on developing increasingly complex and universal models to enhance predictive accuracy, such approaches often lack physical interpretability and insights into materials behavior. Here, we introduce a novel computational paradigm, Self-Adaptable Graph Attention Networks integrated with Symbolic Regression (SA-GAT-SR), that synergistically combines the predictive capability of GNNs with the interpretative power of symbolic regression. Our framework employs a self-adaptable encoding algorithm that automatically identifies and adjust attention weights so as to screen critical features from an expansive 180-dimensional feature space while maintaining O(n) computational scaling. The integrated SR module subsequently distills these features into compact analytical expressions that explicitly reveal quantum-mechanically meaningful relationships, achieving 23 times acceleration compared to conventional SR implementations that heavily rely on first principle calculations-derived features as input. This work suggests a new framework in computational materials science, bridging the gap between predictive accuracy and physical interpretability, offering valuable physical insights into material behavior.