Farris Riccardo, Neyman Konstantin M, Bruix Albert
Departament de Ciència de Materials i Química Física and Institut de Química Teòrica i Computacional (IQTC-UB), Universitat de Barcelona, 08028 Barcelona, Spain.
ICREA (Institució Catalana de Recerca i Estudis Avançats), 08010 Barcelona, Spain.
J Chem Phys. 2024 Oct 7;161(13). doi: 10.1063/5.0214377.
The energetically most favorable chemical ordering of bimetallic nanoparticles can be characterized by combining global optimization algorithms and surrogate energy models. The latter approximate the energy of nanoalloys relying on structural descriptors, training models, and data. Here, we systematically evaluate the performance of highly data-efficient topological descriptors [Kozlov et al., Chem. Sci. 6, 3868 (2015)] for predicting the energies of metal nanoalloys with different chemical orderings. We also introduce a new descriptor based on atomic coordination types, which results in a less data-efficient and interpretable approach, but improves the general accuracy and the quantification of orderings in the inner parts of nanoparticles. The capacity of both the original and new approaches in combination with a basin hopping algorithm is illustrated by generating convex hulls of PdZn nanoalloys and predicting the resulting active surface site distribution as a function of particle composition. Finally, we show how these approaches can be combined with machine-learning adsorption models in electrocatalysis studies for a fast evaluation of the reactivity landscape of targeted nanoalloys.
通过结合全局优化算法和替代能量模型，可以表征双金属纳米颗粒在能量方面最有利的化学排序。后者依靠结构描述符、训练模型和数据来近似纳米合金的能量。在此，我们系统地评估了高数据效率的拓扑描述符[科兹洛夫等人，《化学科学》6, 3868 (2015)]预测具有不同化学排序的金属纳米合金能量的性能。我们还引入了一种基于原子配位类型的新描述符，它虽然数据效率较低且较难解释，但提高了总体准确性以及纳米颗粒内部排序的量化程度。通过生成钯锌纳米合金的凸包并预测作为颗粒组成函数的活性表面位点分布，说明了原始方法和新方法与盆地跳跃算法相结合的能力。最后，我们展示了在电催化研究中，如何将这些方法与机器学习吸附模型相结合，以便快速评估目标纳米合金的反应活性情况。
