Kavli Affiliate: Kristin A. Persson
| First 5 Authors: Alin Marin Elena, Prathami Divakar Kamath, Théo Jaffrelot Inizan, Andrew S. Rosen, Federica Zanca
| Summary:
Metal-organic frameworks (MOFs) are highly porous and versatile materials
studied extensively for applications such as carbon capture and water
harvesting. However, computing phonon-mediated properties in MOFs, like thermal
expansion and mechanical stability, remains challenging due to the large number
of atoms per unit cell, making traditional Density Functional Theory (DFT)
methods impractical for high-throughput screening. Recent advances in machine
learning potentials have led to foundation atomistic models, such as MACE-MP-0,
that accurately predict equilibrium structures but struggle with phonon
properties of MOFs. In this work, we developed a workflow for computing phonons
in MOFs within the quasi-harmonic approximation with a fine-tuned MACE model,
MACE-MP-MOF0. The model was trained on a curated dataset of 127 representative
and diverse MOFs. The fine-tuned MACE-MP-MOF0 improves the accuracy of phonon
density of states and corrects the imaginary phonon modes of MACE-MP-0,
enabling high-throughput phonon calculations with state-of-the-art precision.
The model successfully predicts thermal expansion and bulk moduli in agreement
with DFT and experimental data for several well-known MOFs. These results
highlight the potential of MACE-MP-MOF0 in guiding MOF design for applications
in energy storage and thermoelectrics.
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