VlachosGroup/Pdn-Cluster-Structure-Optimization

Dataset, statistical learning-based energy model and structure optimization algorithms in Python for subnanometer Pd clusters supported on Ceria

Pdn Cluster Structure Optimization

Dataset, statistical learning-based energy model and structure optimization algorithms in Python for subnanometer Pd clusters supported on Ceria

Computational Framework

Pd cluster structure dataset

The dataset contains Pdn cluster structures in the size range from 1 to 21.

• DFT dataset
• Configuration (lattice mapping of DFT structures) dataset

Pd cluster energy model

• LASSO-assisted Cluster Expansion (CE), an efficient mapping from structures to energies

Structure optimization algoirthms in cannoical ensembles

The detailed usage can be found in the links.

• Metropolis MC
• Cluster Genetic Algorithm (CGA)

Dependencies

• Python version 3.6+
• Numpy: Used for vector and matrix operations
• Matplotlib: Used for plotting
• Scipy: Used for linear algebra calculations
• Pandas: Used to import data from Excel files
• Sklearn: Used for training machine learning models
• Seaborn: Used for plotting
• Networkx: Used for graph opertations

How to cite this work

Wang, Y., Su, Y., Hensen, E. J. M., & Vlachos, D. G. (2020). Finite-Temperature Structures of Supported Subnanometer Catalysts Inferred via Statistical Learning and Genetic Algorithm-Based Optimization. ACS Nano. https://doi.org/10.1021/acsnano.0c06472