发表机构
Indian Institute of Science（印度科学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出多任务物理信息神经网络，直接从Voronoi基序预测Cu-Zr金属玻璃温度并分类状态，融合物理梯度约束，实现高精度预测并识别二十面体基序为玻璃态关键指纹。
AI 中文摘要
将局部原子结构与金属玻璃热力学状态相关联的机器学习模型通常是在训练后评估物理一致性，而不是在学习过程中强制执行。在此，我们开发了一种多任务物理信息神经网络（PINN），该网络直接从Voronoi基序种群直方图预测温度，同时纳入由自动微分导出的梯度约束，这些约束代表了结构基序、淬火速率和温度之间物理上合理的关系。该模型通过辅助分类头同时将每个构型分类为液态、过冷/过渡态或玻璃态。分类任务在测试集上达到96.6%的准确率，宏F1分数为0.95，而回归头在轨迹不相交的留出测试集上产生23.4 K的平均绝对误差。采用五折轨迹分组交叉验证、深度集成预测和蒙特卡洛dropout来评估模型鲁棒性和预测不确定性。敏感性分析表明，物理约束可以在广泛的损失权重范围内纳入，而不会损害预测精度，同时显著提高对规定物理趋势的符合性。与传统机器学习回归器的基准比较进一步证明了竞争性的预测性能。跨交叉验证集成的SHAP分析识别出耦合的近二十面体基序家族，特别是完整二十面体基序及其单原子扰动对应物，作为玻璃态的主要结构指纹。这些结果表明，物理上合理的约束可以直接嵌入基于基序的结构-性质模型中，为金属玻璃从事后可解释性走向物理约束机器学习提供了一条途径。
英文摘要
Machine-learning models that relate local atomic structure to the thermodynamic state of metallic glasses typically assess physical consistency after training rather than enforcing it during learning. Here, we develop a multi-task physics-informed neural network (PINN) that predicts temperature directly from Voronoi-motif population histograms while incorporating autograd-derived gradient constraints representing physically motivated relationships between structural motifs, quench rate, and temperature. The model simultaneously classifies each configuration as liquid, supercooled/transition, or glass through an auxiliary classification head. The classification task achieves 96.6% test accuracy with a macro-F1 score of 0.95, while the regression head yields a mean absolute error of 23.4 K on a trajectory-disjoint held-out test set. Five-fold trajectory-grouped cross-validation, deep-ensemble predictions, and Monte Carlo dropout are used to assess model robustness and predictive uncertainty. Sensitivity analyses demonstrate that physics constraints can be incorporated over a broad range of loss weights without compromising predictive accuracy, while substantially improving compliance with the prescribed physical trends. Benchmarking against conventional machine-learning regressors further demonstrates competitive predictive performance. SHAP analysis across the cross-validation ensemble identifies the coupled near-icosahedral motif family, particularly the full icosahedral motif and its single-atom-perturbed counterpart, as the dominant structural fingerprints of the glassy state. These results demonstrate that physically motivated constraints can be embedded directly into motif-based structure-property models, providing a pathway from post-hoc interpretability toward physically constrained machine learning for metallic glasses.
Comments31 pages, 11 figures
