The NanoParticleLibrary: a python package from the computational studies of Nanoalloys Riccardo Farris 1 , Konstantin M. Neyman 1,2 and Albert Bruix 1 1 Departament de Ciència de Materials i Química Física and Institut de Química Teòrica i Computacional (IQTCUB), Universitat de Barcelona, Spain, 2 ICREA (Institució Catalana de Recerca i Estudis Avançats), Spain The understanding of the chemical and physical properties of nanostructured materials is often precluded by the complexity of their structure, which also poses a challenge for constructing representative structural models in computational studies. For nanoalloys, this challenge also involves predicting the most stable arrangement of the two elements, as well as the chemical properties of the resulting surface sites. Stable chemical orderings of bimetallic nanoparticles can be determined with global optimization approaches, which typically evaluate hundreds or thousands of candidate structures algorithmically. Given the unaffordable computational cost of evaluating the stability of such a large number of structures with approximations based on the predominant density functional theory, global optimization algorithms often rely on surrogate energy models that are fit to reproduce energies of bimetallic particles calculated at this accurate level of theory [1, 2]. Similarly, surrogate energy models can be used to approximate the binding energies on a large number of inequivalent sites and thus explore the behavior of nanoalloys under reaction conditions. Here we present an Open Source-Python Package, the NanoParticleLibrary (NPL, https://github.com/reac- nps/NanoParticleLibrary), which has been developed for computational studies of nanoalloys. The library is a wrapper around the popular Atomistic Simulation Environment and includes a flexible energy pipeline for different surrogate energy models of nanoparticles, site recognition algorithms, and various global optimization algorithms. The surrogate energy models can be imported or trained within the library, and used with a Genetic Algorithm, Markov Chain Monte Carlo, and a recently developed Optimal-Exchange algorithm [3]. We showcase the capacity of this library by presenting different case studies involving the optimization of the shape, element ordering, and response to reaction conditions of different technologically relevant bimetallic nanoparticles. References 1. S. M. Kozlov, G. Kovács, R. Ferrando, and K. M. Neyman, “How to determine accurate chemical ordering in several nanometer large bimetallic crystallites from electronic structure calculations,” Chemical Science , vol. 6, no. 7, pp. 3868– 3880, 2015, doi: 10.1039/c4sc03321c. 2. Z. Yan, M. G. Taylor, A. Mascareno, and G. Mpourmpakis, “Size-, Shape-, and Composition-Dependent Model for Metal Nanoparticle Stability Prediction,” Nano Letters , vol. 18, no. 4, pp. 2696–2704, 2018, doi: 10.1021/acs.nanolett.8b00670. 3. Neumann F, Margraf JT, Reuter K, Bruix A. Interplay between shape and composition in bimetallic nanoparticles revealed by an efficient optimal-exchange optimization algorithm. ChemRxiv, doi: 10.26434/chemrxiv-2021-26ztp.
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