用几何先验训练模型，高效预测化学反应路径。
Transferable Learning of Reaction Pathways from Geometric Priors
- 基于对称性破缺的神经网络，从反应物产物预测路径偏差。
- 在多种小分子反应中准确对齐参考内在反应坐标。
- 无需过渡态或预优化路径，适合大规模反应空间探索。
识别最低能量路径（MEPs）对理解化学反应机理至关重要，但计算成本高昂。我们提出MEPIN，一种可扩展的机器学习方法，能从反应物和产物构型出发，高效预测MEPs，无需依赖过渡态几何或训练时的预优化路径。该任务定义为预测沿反应坐标的几何插值偏差。我们采用基于对称性破缺等变神经网络的连续反应路径模型，生成可变数量的中间结构。模型使用基于能量的目标函数训练，并通过测地线插值提供的几何先验提升效率，作为初始插值或预训练目标。该方法在多种小分子反应和[3+2]环加成反应上表现出跨反应的泛化能力，与参考内在反应坐标高度对齐。本方法实现了对大范围化学反应空间的高效、数据驱动的反应路径预测。
原文摘要 · Abstract (English)
Identifying minimum-energy paths (MEPs) is crucial for understanding chemical reaction mechanisms but remains computationally demanding. We introduce MEPIN, a scalable machine-learning method for efficiently predicting MEPs from reactant and product configurations, without relying on transition-state geometries or pre-optimized reaction paths during training. The task is defined as predicting deviations from geometric interpolations along reaction coordinates. We address this task with a continuous reaction path model based on a symmetry-broken equivariant neural network that generates a flexible number of intermediate structures. The model is trained using an energy-based objective, with efficiency enhanced by incorporating geometric priors from geodesic interpolation as initial interpolations or pre-training objectives. Our approach generalizes across diverse chemical reactions and achieves accurate alignment with reference intrinsic reaction coordinates, as demonstrated on various small molecule reactions and [3+2] cycloadditions. Our method enables the exploration of large chemical reaction spaces with efficient, data-driven predictions of reaction pathways.
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