The MACE architecture represents the state of the art in the field of machine learning force fields for a variety of in-domain, extrapolation and low-data regime tasks. In this paper, we further evaluate MACE by fitting models for published benchmark datasets. We show that MACE generally outperforms alternatives for a wide range of systems from amorphous carbon, universal materials modelling, and general small molecule organic chemistry to large molecules and liquid water. We demonstrate the capabilities of the model on tasks ranging from constrained geometry optimisation to molecular dynamics simulations and find excellent performance across all tested domains. We show that MACE is very data efficient, and can reproduce experimental molecular vibrational spectra when trained on as few as 50 randomly selected reference configurations. We further demonstrate that the strictly local atom-centered model is sufficient for such tasks even in the case of large molecules and weakly interacting molecular assemblies.

翻译：MACE架构在机器学习力场领域代表了当前最先进水平，适用于多种域内任务、外推任务以及低数据量任务。本文通过拟合已发布基准数据集中的模型，进一步评估了MACE架构的性能。研究表明，MACE在广泛的系统（从无定形碳、通用材料建模、普通小分子有机化学，到大分子和液态水）中通常优于其他替代方法。我们展示了该模型在从约束几何优化到分子动力学模拟等任务上的能力，在所有测试领域均表现出卓越性能。我们还证明MACE具有极高的数据效率：仅需训练50个随机选取的参考构型，即可复现实验分子振动光谱。此外，我们进一步证实，即使是针对大分子和弱相互作用分子组装体，这种严格的局域原子中心模型也已完全胜任此类任务。