NanoParticleLibrary (NPL)

Table of contents

• NanoParticleLibrary (NPL)
  • About NPL
  • Documentation
  • Installation
  • Requirements
  • Installation from PyPI
  • Installation from source
  • Examples
  • References
  • Contact
  • License

About NPL

NPL is a Python library for the simulation and structural optimization of nanoparticles, specifically tailored for bimetallic nanoparticles. Built on the robust ASE (Atomic Simulation Environment), it enables users to easily set up and analyze complex nanoparticle structures across a range of chemical compositions and structures. NPL provides high-level abstractions, making it accessible for both beginners and experienced researchers aiming to perform detailed nanoparticle simulations.

Documentation

A partial documentation is available at: https://npl.readthedocs.io/en/latest/

Installation

Requirements

• Python 3.9+
• Atomic Simulation Environment (ASE) >= 3.21
• scikit-learn
• sortedcontainers

Installation from PyPI

You can install NPL with pip:

sh pip install npl

or from github:

sh git clone https://github.com/farrisric/npl pip install ./npl

Examples

Monte Carlo Run Example

Here’s a revised version without using lists:

This example demonstrates how to perform a Monte Carlo simulation using NPL. First, pre-trained topological coefficients are loaded to guide the simulation. A truncated octahedral Pt151Cu50 nanoparticle is then initialized as the system for the simulation. The Monte Carlo simulation is set up with a temperature parameter of 250 K and a total of 10,000 steps. Once the simulation is executed, the positions of the nanoparticle atoms are optimized. Finally, the optimized positions are printed for analysis.

```python

from npl.descriptors import ExtendedTopologicalFeaturesClassifier from npl.calculators import TOPCalculator from npl.core import Nanoparticle from npl.montecarlo import runmontecarlo from npl.visualize import plotparted_particle

energycalculator = TOPCalculator('ETOP', stoichiometry='Pt151Cu50', featureclassifier=ExtendedTopologicalFeaturesClassifier) featureclassifier = energycalculator.getfeatureclassifier()

temperature = 250 max_steps = 10000

startparticle = Nanoparticle() startparticle.truncatedoctahedron(7, 2, {'Pt': 151, 'Cu': 50}) bestparticle, acceptedenergies = runmontecarlo(temperature, maxsteps, startparticle, energycalculator, feature_classifier)

plotpartedparticle(best_particle) ```

References

If you use this code, please cite our papers:

```bibtex @neuman{10.1063/5.0214377, author = {Felix Neumann and Johannes T Margraf and Karsten Reuter and Albert Bruix}, title = "{Interplay between shape and composition in bimetallic nanoparticles revealed by an efficient optimal-exchange optimization algorithm}", archivePrefix = {ChemRxiv}, doi = {10.26434/chemrxiv-2021-26ztp}, }

@article{10.1063/5.0193848, author = {Farris, Riccardo and Merinov, Boris V. and Bruix, Albert and Neyman, Konstantin M.}, title = "{Effects of Zr dopants on properties of PtNi nanoparticles for ORR catalysis: A DFT modeling}", journal = {The Journal of Chemical Physics}, volume = {160}, number = {12}, pages = {124706}, year = {2024}, issn = {0021-9606}, doi = {10.1063/5.0193848}, url = {https://doi.org/10.1063/5.0193848}, }

@farris{10.1063/5.0214377, author = {Farris, Riccardo and Neyman, Konstantin M. and Bruix, Albert}, title = "{Determining the chemical ordering in nanoalloys by considering atomic coordination types}", journal = {The Journal of Chemical Physics}, volume = {161}, number = {13}, pages = {134114}, year = {2024}, issn = {0021-9606}, doi = {10.1063/5.0214377} } ```

Contact

For any questions or issues, please contact:

• Riccardo Farris: rfarris@ub.edu
• GitHub Issues: npl Issues

License

This project is licensed under the MIT License - see the LICENSE file for details.