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English(EN) MAST: Motif-Augmented Diffusion with Search Tree for Spectroscopic Molecular Structure Elucidation

MAST框架增强光谱分子结构解析能力

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了MAST，一个新颖的基于扩散的模型框架，旨在改进从光谱数据中解析分子结构。MAST在去噪过程中引入了显式的基元先验，以增强光谱-结构关系的学习，尤其是在处理有限的配对数据时。此外，它将扩散采样重构为奖励引导的树搜索，以有效地识别高质量的分子候选。该框架在QM9S基准测试中表现强劲，实现了94.89%的精确恢复，提高了3D保真度，同时保持了化学有效性。 AI

影响 这项研究通过提高从光谱数据确定分子结构的准确性和效率，有可能加速化学和材料表征。

排序理由 该集群包含一篇详细介绍分子结构解析新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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MAST框架增强光谱分子结构解析能力

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该集群包含一篇详细介绍分子结构解析新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Chenghao Jia, Mengdi Liu, Hong Chang, Shiguang Shan, Xilin Chen ·

    MAST: 基于搜索树的增强扩散模型用于光谱分子结构解析

    arXiv:2610.12067v1 Announce Type: cross Abstract: Elucidating molecular structures from spectra is a foundational problem in chemical and materials characterization, yet remains challenging due to spectral ambiguity and the vast molecular space. Although recent diffusion-based ge…

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