We introduce a reversible deep learning model for 13C NMR that uses a single conditional invertible neural network for both directions between molecular structures and spectra. The network is built from i-RevNet style bijective blocks, so the forward map and its inverse are available by construction. We train the model to predict a 128-bit binned spectrum code from a graph-based structure encoding, while the remaining latent dimensions capture residual variability. At inference time, we invert the same trained network to generate structure candidates from a spectrum code, which explicitly represents the one-to-many nature of spectrum-to-structure inference. On a filtered subset, the model is numerically invertible on trained examples, achieves spectrum-code prediction above chance, and produces coarse but meaningful structural signals when inverted on validation spectra. These results demonstrate that invertible architectures can unify spectrum prediction and uncertainty-aware candidate generation within one end-to-end model.

翻译：我们提出了一种用于13C核磁共振的可逆深度学习模型，该模型采用单一的条件可逆神经网络实现分子结构与光谱之间的双向映射。该网络基于i-RevNet风格的双射模块构建，因此前向映射及其逆映射可通过结构设计自然获得。我们训练该模型从基于图结构的编码预测128位分箱光谱编码，而剩余的潜在维度则用于捕捉残差变异性。在推理阶段，我们通过同一训练网络的反向操作，从光谱编码生成结构候选集，这显式表征了光谱到结构推断的一对多特性。在过滤后的子集上，该模型对训练样本保持数值可逆性，其光谱编码预测性能超越随机基线，且在验证光谱上进行反向推理时能生成粗略但具有意义的结构信号。这些结果表明，可逆架构可在单一端到端模型内统一实现光谱预测与不确定性感知的候选结构生成。