A neural network potential for the IRMOF series and its application for thermal and mechanical behaviors

• Published in: Physical chemistry chemical physics : PCCP Vol. 24; no. 19; pp. 11882 - 11897
• Main Authors: Tayfuroglu, Omer, Kocak, Abdulkadir, Zorlu, Yunus
• Format: Journal Article
• Language: English
• Published: England Royal Society of Chemistry 18.05.2022
• Subjects: Bulk density; Computing costs; Density of states; Equilibrium; First principles; Fragments; Hybrid structures; Lattice parameters; Metal-organic frameworks; Methane; Molecular dynamics; Network reliability; Neural networks; Simulation; Steric hindrance; Strain; Thermal expansion; Unit cell
• ISSN: 1463-9076; 1463-9084; 1463-9084
• DOI: 10.1039/d1cp05973d

Metal-organic frameworks (MOFs) with their exceptional porous and organized structures have been the subject of numerous applications. Predicting the bulk properties from atomistic simulations requires the most accurate force fields, which is still a major problem due to MOFs' hybrid structures governed by covalent, ionic and dispersion forces. Application of ab initio molecular dynamics to such large periodic systems is thus beyond the current computational power. Therefore, alternative strategies must be developed to reduce computational cost without losing reliability. In this work, we construct a generic neural network potential (NNP) for the isoreticular metal-organic framework (IRMOF) series trained by PBE-D4/def2-TZVP reference data of MOF fragments. We confirmed the success of the resulting NNP on both fragments and bulk MOF structures by prediction of properties such as equilibrium lattice constants, phonon density of states and linker orientation. The RMSE values of energy and force for the fragments are only 0.0017 eV atom −1 and 0.15 eV Å −1 , respectively. The NNP predicted equilibrium lattice constants of bulk structures, even though not included in training, are off by only 0.2-2.4% from experimental results. Moreover, our fragment based NNP successfully predicts the phenylene ring torsional energy barrier, equilibrium bond distances and vibrational density of states of bulk MOFs. Furthermore, the NNP enables revealing the odd behaviors of selected MOFs such as the dual thermal expansion properties and the effect of mechanical strain on the adsorption of hydrogen and methane molecules. The NNP based molecular dynamics (MD) simulations suggest IRMOF-4 and IRMOF-7 to have positive-to-negative thermal expansion coefficients while the rest to have only negative thermal expansion at the studied temperatures of 200 K to 400 K. The deformation of the bulk structure by reduction of the unit cell volume has been shown to increase the volumetric methane uptake in IRMOF-1 but decrease the volumetric methane uptake in IRMOF-7 due to the steric hindrance. To the best of our knowledge, this study presents the first pre-trained model publicly available giving the opportunity for the researchers in the field to investigate different aspects of IRMOFs by performing large-scale simulation at the first-principles level of accuracy. We construct a generic neural network potential (NNP) for IRMOF-n series trained by PBE-D4/def2-TZVP reference data of MOF fragments and identify bulk properties using NNP, much faster than DFT calculations.