DOI: 10.5281/zenodo.16878887
title: Predictive model for the discovery of sinter-resistant supports for metallic nanoparticle catalysts by interpretable machine learning
title: NN-MD Database: Neural Network Potential-Driven Molecular Dynamics Simulations for Supported Pt Nanoparticles

description: Neural Network Potential-driven Molecular Dynamics Database for Catalyst Support Materials

This database contains Neural Network Potential driven Molecular Dynamics (NN-MD) simulation results for 3nm Pt nanoparticle supported on various metal oxides supports at 800 °C for 500 ps and high-throughput screening results for catalyst support materials, organized into three datasets for comprehensive analysis of sinter-resistant catalyst supports.

Dataset Contents

Dataset 1: NN-MD_Database_Custom_Selection.zip



Source: Custom-selected material systems

Content: Molecular dynamics simulation results for specifically chosen material surfaces

Files: MD trajectories (PDB), analysis plots (SVG), and numerical data (CSV)


Dataset 2: NN-MD_Database_From_OC22.zip



Source: OC22 (Open Catalyst 2022) dataset

Content: NN-MD simulation results for catalyst surfaces from OC22 database

Files: MD trajectories (PDB), analysis plots (SVG), and numerical data (CSV)


Dataset 3: Atomic_Structures_for_Promising_Sinter-Resistant_Supports.zip



Source: High-throughput screening using interpretable generalized additive model (iGAM)

Content: Atomic structures of promising sinter-resistant catalyst supports

Files: VASP POSCAR format structure files (Extracted from the last frame of the local optimization trajectory)


File Structure and Naming

Folders: Named as {Material_name}_{MP-ID}_{Miller_index} (e.g., MoO3_mp-20589_001) or {Material_name}_{Miller_index}_{Trajectory._ID_in_OC22} (e.g., {FePtO4-rutile}_{001}_{FePtO4_FePtO4-rutile_clean_Tj36HCumzE} )

Key Files:



NNMD_output.pdb: MD trajectory data

MD_contact_angle_Normalized_MSI_descriptor.svg: Contact angle and MSI descriptor plots

MD_Pt_Eadh_ChemicalPotential.svg: Platinum adsorption energy analysis

MD_Pt_contact_angle_adhesion_energy.csv: Quantitative adhesion data

POSCAR: VASP-format atomic structures extracted from the last frame of traj. file in OC22 database (Dataset 3 only)


Technical Specifications



Simulation Method: Neural network potential-based molecular dynamics

Coordinate System: Cartesian coordinates

Units: Distance (Å), Energy (eV), Time (ps)

Structure Format: VASP POSCAR format for atomic structures

Screening Method: iGAM (interpretable Generalized Additive Model)


Applications

This database supports research in:



Catalyst support design and optimization

Sinter-resistance analysis

Platinum-support interaction studies

High-throughput materials screening

Neural network potential validation


Usage

Compatible with standard molecular dynamics analysis software (VMD, OVITO) and materials science visualization tools (VESTA, Materials Studio). CSV files contain ready-to-use numerical data for statistical analysis.

Keywords

molecular dynamics, neural network potential, catalyst support, sinter-resistance, high-throughput screening, OC22 dataset, materials informatics

Related Resources



Open Catalyst 2022 (OC22) Dataset

Materials Project Database