A well-known drawback of state-of-the-art machine-learning interatomic potentials is their poor ability to extrapolate beyond the training domain. For small-scale problems with tens to hundreds of atoms this can be solved by using active learning which is able to select atomic configurations on which a potential attempts extrapolation and add them to the ab initio -computed training set. In this sense an active learning algorithm can be viewed as an on-the-fly interpolation of an ab initio model. For large-scale problems, possibly involving tens of thousands of atoms, this is not feasible because one cannot afford even a single density functional theory (DFT) computation with such a large number of atoms. This work marks a new milestone toward fully automatic ab initio -accurate large-scale atomistic simulations. We develop an active learning algorithm that identifies local subregions of the simulation region where the potential extrapolates. Then the a...

中文翻译：

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在操作中主动学习大型仿真过程中的原子间相互作用

最先进的机器学习原子间电位的一个众所周知的缺点是其推断能力超出训练领域的能力很差。对于具有数十到数百个原子的小规模问题，可以通过使用主动学习来解决，该主动学习能够选择可能尝试外推的原子构型，并将其添加到从头算出的训练集中。从这个意义上讲，主动学习算法可以看作是从头算模型的动态插值。对于可能涉及成千上万个原子的大规模问题，这是不可行的，因为甚至连这么多的原子都无法提供单一的密度泛函理论（DFT）计算。这项工作标志着朝着全自动从头开始的精确大规模原子模拟的新里程碑。我们开发了一种主动学习算法，该算法可识别模拟区域中可能外推的局部子区域。然后...