The accuracy of the training data limits the accuracy of bulk properties from machine-learned potentials. For example, hybrid functionals or wave-function-based quantum chemical methods are readily available for cluster data but effectively out-of-scope for periodic structures. We show that local, atom-centred descriptors for machine-learned potentials enable the prediction of bulk properties from cluster model training data, agreeing reasonably well with predictions from bulk training data. We demonstrate such transferability by studying structural and dynamical properties of bulk liquid water with density functional theory and have found an excellent agreement with experimental as well as theoretical counterparts.

翻译：训练数据的精度限制了基于机器学习的势函数预测体相性质的精度。例如，杂化泛函或基于波函数的量子化学方法可轻易用于处理簇数据，但实质上难以应用于周期性结构。我们证明，用于机器学习势函数的局域、以原子为中心的描述符能够利用簇模型训练数据预测体相性质，且与基于体相训练数据的预测结果吻合良好。通过密度泛函理论研究液态水的结构及动力学性质，我们展示了这种可迁移性，并发现与实验及理论结果均具有极佳的一致性。