Quantum-chemistry simulations based on potential energy surfaces of molecules provide invaluable insight into the physicochemical processes at the atomistic level and yield such important observables as reaction rates and spectra. Machine learning potentials promise to significantly reduce the computational cost and hence enable otherwise unfeasible simulations. However, the surging number of such potentials begs the question of which one to choose or whether we still need to develop yet another one. Here, we address this question by evaluating the performance of popular machine learning potentials in terms of accuracy and computational cost. In addition, we deliver structured information for non-specialists in machine learning to guide them through the maze of acronyms, recognize each potential's main features, and judge what they could expect from each one.

中文翻译：

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选择合适的分子机器学习潜力

基于分子势能面的量子化学模拟在原子水平上提供了对物理化学过程的宝贵洞察，并产生了反应速率和光谱等重要的观察结果。机器学习潜力有望显着降低计算成本，从而实现原本不可行的模拟。然而，此类潜力的激增引发了选择哪一种或我们是否还需要开发另一种潜力的问题。在这里，我们通过评估流行的机器学习潜力在准确性和计算成本方面的性能来解决这个问题。此外，我们为非机器学习专家提供结构化信息，以引导他们通过首字母缩略词的迷宫，识别每个潜力的主要特征，