colabfit/ANI-1xnr

ANI-1xnr数据集是为了训练ANI-1xnr模型而开发的，该模型旨在模拟反应性化学。具体来说，ANI-1xnr用于表示碳固体相成核、乙炔形成石墨烯环、生物燃料添加剂、甲烷燃烧以及早期地球小分子自发形成甘氨酸的过程。该数据集通过一种主动学习方法生成，其中从头纳米反应器模拟提供了机器学习原子间势能（MLIP）的训练；然后对MLIP进行测试，并根据具有高不确定性的结构生成新的模拟，为下一次MLIP训练周期提供数据。

The ANI-1xnr dataset was developed to train the ANI-1xnr model, which is intended to model reactive chemistry. Specifically, ANI-1xnr is meant to represent carbon solid-phase nucleation, graphene ring formation from acetylene, biofuel additives, combustion of methane, and the spontaneous formation of glycine from early earth small molecules. The dataset was generated using an active learning method in which ab initio nanoreactor simulations supplied MLIP training; the MLIP was subsequently tested and new simulations were generated based on structures tested with high uncertainty to supply the next cycle of MLIP training.