标题： 实时对中子振动光谱进行对称等变Hessian矩阵预测的解释 显示英文标题

标题： Real-time interpretation of neutron vibrational spectra with symmetry-equivariant Hessian matrix prediction

摘要： 分子的振动行为是其结构、化学状态和周围环境的重要特征。中子振动光谱能够全面测量振动模式，且不受选择规则的限制。然而，分析和解释所得光谱仍然是一个计算上极具挑战性的任务。在此，我们引入了一种具有对称意识的神经网络，可以直接从分子结构预测Hessian矩阵，从而实现快速的振动光谱重建。与传统方法侧重于特征值预测不同，Hessian矩阵提供了更丰富、更基础的信息，具有更广泛的应用和优越的外推能力。该方法还为预测其他性质（如反应路径）铺平了道路。在小分子上进行训练，我们的模型实现了光谱级别的准确性，使得实时、明确的峰位分配成为可能。此外，它在较大分子上也保持了高准确性，表现出强大的可迁移性。这种适应性开启了新的能力，包括未来自主实验室的实时光谱解释，并为有针对性的化学路径提供了分子设计的见解。 显示英文摘要

摘要： The vibrational behavior of molecules serves as a crucial fingerprint of their structure, chemical state, and surrounding environment. Neutron vibrational spectroscopy provides comprehensive measurements of vibrational modes without selection rule restrictions. However, analyzing and interpreting the resulting spectra remains a computationally formidable task. Here, we introduce a symmetry-aware neural network that directly predicts Hessian matrices from molecular structures, thereby enabling rapid vibrational spectral reconstruction. Unlike traditional approaches that focus on eigenvalue prediction, the Hessian matrix provides richer, more fundamental information with broader applications and superior extrapolation. This approach also paves the way for predicting other properties, such as reaction pathways. Trained on small molecules, our model achieves spectroscopic-level accuracy, allowing real-time, unambiguous peak assignment. Moreover, it maintains high accuracy for larger molecules, demonstrating strong transferability. This adaptability unlocks new capabilities, including on-the-fly spectral interpretation for future autonomous laboratories, and offers insights into molecular design for targeted chemical pathways.