Unravelling abnormal in-plane stretchability of two-dimensional metal-organic frameworks by machine learning potential molecular dynamics

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 a highly stable NiF2(pyrazine)2 2D MOF in silico with a two-dimensional periodic wine-rack architecture. Extensive first-principles calculations and molecular dynamics (MD) 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 in anomalous negative in-plane stretchability, as evidenced by an uncommon decrease in its in-plane area upon the application of uniaxial tensile strain, which makes this 2D MOF particularly attractive for flexible wearable electronics and ultra-thin sensor applications. We further demonstrated the unique capability of MLP to accurately predict the finite-temperature properties of MOFs on a large scale, exemplified by MLP-MD simulations with a dimension of 28.2 x 28.2 nm2, relevant to the length scale experimentally attainable for the fabrication of MOF films.

The concept of negative in-plane stretchability is proposed taking a 2D MOF, namely NiF2(pyrazine)2, as a case study, combining high-precision first-principles calculations and machine-learning potential (MLP) approaches.