Machine-learning force fields have been increasingly employed in order to extend the possibility of current first-principles calculations. However, the transferability of the obtained potential cannot always be guaranteed in situations that are outside the original database. To study such limitation, we examined the very difficult case of the interactions in gold–iron nanoparticles. For the machine-learning potential, we employed a linearized formulation that is parameterized using a penalizing regression scheme which allows us to control the complexity of the obtained potential. We showed that while having a more complex potential allows for a better agreement with the training database, it can also lead to overfitting issues and a lower accuracy in untrained systems.

为了扩大当前的第一性原理计算的可能性，越来越多地采用机器学习力场。但是，在原始数据库之外的情况下，不能始终保证所获得电位的可转移性。为了研究这种局限性，我们研究了金铁纳米颗粒相互作用的非常困难的情况。对于机器学习潜力，我们采用了线性化的公式，该公式使用惩罚回归方案进行了参数化，这使我们能够控制所获得的潜力的复杂性。我们证明了，尽管潜力更大，可以更好地与培训数据库达成协议，但也可能导致过度拟合的问题，并且在未经培训的系统中准确性较低。