Harnessing Artificial Intelligence for Modeling Amorphous and Amorphous Porous Palladium: A Deep Neural Network Approach

TL;DR

This paper introduces a deep neural network model trained on extensive ab initio data to accurately simulate amorphous and porous palladium, significantly reducing computational costs and enabling advanced material analysis.

Contribution

The study presents a novel AI-based interatomic potential model specifically designed for amorphous palladium, validated against DFT, improving simulation efficiency and accuracy.

Findings

AI model accurately predicts structural properties

Reduces computational costs compared to traditional methods

Validated against density functional theory

Abstract

Amorphous and amorphous porous palladium are key materials for catalysis, hydrogen storage, and functional applications, but their complex structures present computational challenges. This study employs a deep neural network trained on 33,310 atomic configurations from ab initio molecular dynamics simulations to model their interatomic potential. The AI-driven approach accurately predicts structural and thermal properties while significantly reducing computational costs. Validation against density functional theory confirms its reliability in reproducing forces, energies, and structural distributions. These findings highlight AI's potential in accelerating the study of amorphous materials and advancing their applications in energy and catalysis.