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Data for: Accelerating and enhancing thermodynamic simulations of electrochemical interfaces

Name: Data for: Accelerating and enhancing thermodynamic simulations of electrochemical interfaces
Creator: Du, Xiaochen

Research datakeyboard_double_arrow_right Dataset 21 Mar 2025Publisher:Zenodo

Authors: Du, Xiaochen;

doi: 10.5281/zenodo.15066440 , 10.5281/zenodo.15066441

Data for: Accelerating and enhancing thermodynamic simulations of electrochemical interfaces

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Abstract

This is the dataset for the publication "Accelerating and enhancing thermodynamic simulations of electrochemical interfaces", by X. Du, M. Liu, J. Peng, H. Chun, A. Hoffman, B. Yildiz, L. Li, M.Z. Bazant, and R. Gómez-Bombarelli. The repository contains the density-functional theory (DFT) data used to fine-tune the pre-trained neural network force fields (NFF), selected results from our Pt(111) and LaMnO3(001) Virtual Surface Site Relaxation-Monte Carlo (VSSR-MC) runs, and Jupyter notebooks used for Pourbaix analysis and plotting. To run the Jupyter notebooks and the commands below, you will need to install surface-sampling (tested up to commit 3c0c547 on pourbaix) and NeuralForceField (tested up to commit 2573e68 on vssr_pourbaix) from the Rafael Gómez-Bombarelli Group @ MIT, as well as our forked version of pymatgen (tested up to commit 5f1a155 on master).

Related Organizations

Massachusetts Institute of Technology
United States

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