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
