The subject of this paper is the technology (the ‘how’) of constructing machine-learning interatomic potentials, rather than science (the ‘what’ and ‘why’) of atomistic simulations using machine-learning potentials. Namely, we illustrate how to construct moment tensor potentials using active learning as implemented in the MLIP package, focusing on the efficient ways to automatically sample configurations for the training set, how expanding the training set changes the error of predictions, how to set up ab initio calculations in a cost-effective manner, etc. The MLIP package (short for Machine-Learning Interatomic Potentials) is available at https://mlip.skoltech.ru/download/.

本文的主题是构建机器学习原子间势的技术（“方法”），而不是使用机器学习势的原子模拟的科学（“什么”和“为什么”）。即，我们说明了如何使用MLIP软件包中实现的主动学习来构造矩张量势，着重于为训练集自动采样配置的有效方法，如何扩展训练集会改变预测误差，如何设置ab可以通过经济高效的方式进行初始计算等。MLIP程序包（机器学习原子间势的简称）可从https://mlip.skoltech.ru/download/获得。