The impact of spurious imaginary phonon modes on thermal properties of Metal-organic Frameworks

TL;DR

This paper investigates how spurious imaginary phonon modes affect the accuracy of thermal property predictions in MOFs, revealing significant errors and proposing a correction workflow to improve computational screening.

Contribution

It systematically demonstrates the impact of imaginary phonon modes on heat capacity estimates and introduces a post-processing method to correct these errors in MOF simulations.

Findings

Spurious imaginary phonon modes cause substantial errors in heat capacity calculations.

Benchmarking MLIPs against datasets with imaginary modes can mislead model assessments.

A simple workflow can correct heat capacity estimates by addressing imaginary phonon modes.

Abstract

Metal-organic Frameworks (MOFs) have emerged as potential candidates for direct air capture (DAC) of green house gases and water. Thermal properties of MOFs, such as their heat capacity, are used to determine the energy penalty associated with the adsorbent retrieval during the Temperature Swing Adsorption process. To aid exploration of the vast experimental design space of MOFs for such applications, computational methods like Density Functional Theory (DFT) or surrogate machine learning models trained on DFT data have been developed for obtaining phonon-derived heat capacities of MOFs. However, the high cost of explicit phonon computation in large and flexible nanoporous MOFs often necessitates the use of small supercells or lower convergence criteria which decrease predictive accuracy. These approximations often result in spurious imaginary phonon modes which are commonly ignored in practice. At present, there is no clear consensus in the literature on what magnitude of negative frequency or what fraction of imaginary modes can be considered acceptable. Here, we systematically demonstrate that spurious imaginary phonon modes can introduce substantial errors in heat capacity estimates, leading to incorrect ranking of MOFs in thermal-property-based screening. We further show that benchmarking machine learning interatomic potentials (MLIPs) against DFT datasets containing spurious imaginary modes can misrepresent models that predict physically meaningful phonon spectra for dynamically stable MOFs. Finally, we introduce a simple, rapid post-processing workflow that can be applied to standard phonon calculations to effectively correct heat capacity estimates and account for spurious imaginary modes in MOFs.