Unravelling Negative In-plane Stretchability of 2D MOF by Large Scale Machine Learning Potential Molecular Dynamics

TL;DR

This study uses machine learning-enhanced molecular dynamics to reveal the unusual negative in-plane stretchability of a 2D MOF, highlighting its potential for flexible electronics and sensors.

Contribution

The paper introduces a new machine learning potential enabling large-scale molecular dynamics simulations of 2D MOFs, uncovering their negative in-plane stretchability and anisotropic mechanical properties.

Findings

2D MOF exhibits negative in-plane stretchability.

MLP enables large-scale, finite-temperature simulations.

Anisotropic Poisson's ratio observed in the MOF.

Abstract

Two-dimensional (2D) metal-organic frameworks (MOFs) hold immense potential for various applications due to their distinctive intrinsic properties compared to their 3D analogues. Herein, we designed in silico a highly stable NiF$_2$(pyrazine)$_2$ 2D MOF with a two-periodic wine-rack architecture. Extensive first-principles calculations and Molecular Dynamics simulations based on a newly developed machine learning potential (MLP) revealed that this 2D MOF exhibits huge in-plane Poisson's ratio anisotropy. This results into an anomalous negative in-plane stretchability, as evidenced by an uncommon decrease of its in-plane area upon the application of uniaxial tensile strain that makes this 2D MOF particularly attractive for flexible wearable electronics and ultra-thin sensor applications. We further demonstrated that the derived MLP offers a unique opportunity to effectively anticipate the finite temperature mechanical properties of MOFs at large scale. As a proof-concept, MLP-based Molecular Dynamics simulations were successfully achieved on 2D NiF$_2$(pyrazine)$_2$ with a dimension of 28.2$\times$28.2 nm$^2$ relevant to the length scale experimentally attainable for the fabrication of MOF film.