摘要：Accurate calculation of specific spectral properties for NMR is an important step for molecular structure elucidation. Here we report the development of a novel machine learning technique for accurately predicting chemical shifts of both $${^1\mathrm{H}}$$ and $${^{13}\mathrm{C}}$$ nuclei which exceeds DFT-accessible accuracy for $${^{13}\mathrm{C}}$$ and $${^1\mathrm{H}}$$ for a subset of nuclei, while being orders of magnitude more performant. Our method produces estimates of uncertainty, allowing for robust and confident predictions, and suggests future avenues for improved performance.
