标题： 通过机器学习原子间势能函数增强分子图的几何结构 显示英文标题

标题： Augmenting Molecular Graphs with Geometries via Machine Learning Interatomic Potentials

摘要： 准确的分子性质预测需要三维几何结构，这些通常通过昂贵的方法如密度泛函理论（DFT）获得。在这里，我们尝试仅依靠机器学习原子间势（MLIP）模型来获得分子几何结构。为此，我们首先整理了一个大规模的分子弛豫数据集，包含350万个分子和3亿个快照。然后，使用监督学习训练MLIP基础模型，以给定的三维分子结构预测能量和力。一旦训练完成，我们表明基础模型可以以不同的方式使用，以显式或隐式的方式获得几何结构。首先，可以通过几何优化获得低能三维几何结构，为下游分子性质预测提供弛豫的三维几何结构。为了减轻潜在偏差并增强下游预测，我们基于弛豫的三维几何结构引入了几何微调。其次，当有真实三维几何结构时，可以对基础模型直接进行属性预测微调。我们的结果表明，基于弛豫数据训练的MLIP基础模型可以提供有益于属性预测的有价值的分子几何结构。 显示英文摘要

摘要： Accurate molecular property predictions require 3D geometries, which are typically obtained using expensive methods such as density functional theory (DFT). Here, we attempt to obtain molecular geometries by relying solely on machine learning interatomic potential (MLIP) models. To this end, we first curate a large-scale molecular relaxation dataset comprising 3.5 million molecules and 300 million snapshots. Then MLIP foundation models are trained with supervised learning to predict energy and forces given 3D molecular structures. Once trained, we show that the foundation models can be used in different ways to obtain geometries either explicitly or implicitly. First, it can be used to obtain low-energy 3D geometries via geometry optimization, providing relaxed 3D geometries for downstream molecular property predictions. To mitigate potential biases and enhance downstream predictions, we introduce geometry fine-tuning based on the relaxed 3D geometries. Second, the foundation models can be directly fine-tuned for property prediction when ground truth 3D geometries are available. Our results demonstrate that MLIP foundation models trained on relaxation data can provide valuable molecular geometries that benefit property predictions.