DeepSciReasoner：物理约束下的科学光谱预测框架
Deep Scientific Reasoning under Physical Constraints: Structure-Aware Spectrum Prediction
一篇把物理守恒定律塞进光谱预测的论文，覆盖 eDOS、XANES、拉曼等四类谱，做材料发现的方向可以看看。
论文提出 DeepSciReasoner，用于在硬物理约束下预测科学光谱。该框架把结构感知的光谱解码与保约束的物理推理结合，以电子态密度（eDOS）为主要示例，现有方法将其当作无约束序列导致物理结构丢失。评估覆盖 eDOS、声子态密度（phDOS）、X 射线吸收近边结构（XANES）和拉曼光谱四类光谱，预测精度提升且保持物理一致性。作者将其定位为结构化光谱预测的可复用范式。
Deep Scientific Reasoning under Physical Constraints: Structure-Aware Spectrum Prediction
Structured scientific spectra encode rich physical information while obeying hard constraints, such as conservation laws and characteristic spectral geometry. Accurate prediction of these spectra is central to materials discovery, yet existing methods often treat them as unconstrained sequences and therefore fail to enforce the underlying physical structure. Taking electronic density of states (eDOS) as the prototypical example, we introduce DeepSciReasoner, a general paradigm for predicting scientific spectra under physical constraints. The framework combines structure-aware spectrum decoding with constraint-preserving physical reasoning, allowing predictions to capture rich spectral structure while respecting the underlying physics. We evaluate DeepSciReasoner on eDOS, phonon density of states (phDOS), X-ray absorption near-edge structure (XANES), and Raman spectra, where it substantially improves prediction accuracy while maintaining physical consistency. These results establish DeepSciReasoner as a reusable blueprint for structured scientific spectrum prediction under hard physical constraints.