Engineering Laboratory, University of Cambridge, Cambridge CB2 1PZ, United Kingdom.
Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, United Kingdom.
J Chem Phys. 2018 Jun 28;148(24):241714. doi: 10.1063/1.5016317.
We demonstrate how machine-learning based interatomic potentials can be used to model guest atoms in host structures. Specifically, we generate Gaussian approximation potential (GAP) models for the interaction of lithium atoms with graphene, graphite, and disordered carbon nanostructures, based on reference density functional theory data. Rather than treating the full Li-C system, we demonstrate how the energy and force differences arising from Li intercalation can be modeled and then added to a (prexisting and unmodified) GAP model of pure elemental carbon. Furthermore, we show the benefit of using an explicit pair potential fit to capture "effective" Li-Li interactions and to improve the performance of the GAP model. This provides proof-of-concept for modeling guest atoms in host frameworks with machine-learning based potentials and in the longer run is promising for carrying out detailed atomistic studies of battery materials.
我们展示了如何使用基于机器学习的原子间势来模拟主结构中的客体原子。具体来说，我们基于参考密度泛函理论数据，为锂离子与石墨烯、石墨和无序碳纳米结构的相互作用生成了高斯逼近势（GAP）模型。我们没有处理完整的 Li-C 体系，而是展示了如何模拟由于锂离子嵌入而产生的能量和力的差异，然后将其添加到纯元素碳的（预先存在且未修改的）GAP 模型中。此外，我们还展示了使用显式对势拟合来捕捉“有效”Li-Li 相互作用并提高 GAP 模型性能的好处。这为使用基于机器学习的势对主框架中的客体原子进行建模提供了概念验证，从长远来看，这对于对电池材料进行详细的原子级研究很有前景。
