HomeCasesAnalysis of CO2 adsorption dynamics of MOF using NEB method

Analysis of CO2 adsorption dynamics of MOF using NEB method

Tokyo Institute of Technology

image

Overview

In order to achieve a carbon-neutral society, there is an urgent need for the development of materials to capture and separate the greenhouse gas CO2. Metal–organic frameworks (MOFs), which are porous materials composed of organic multidentate ligands combined with metal ions, have attracted attention as promising candidates because of their readily modifiable structures. 

In the study of reference 1, two kinds of MOFs with isolated voids which cannot be directly accessed were obtained. Despite the apparent absence of CO2 diffusion pathways in these structures, one of the MOFs was able to selectively capture and store CO2. Simulations using MatlantisTM were performed to elucidate the mechanism of this specific adsorption phenomenon.

image

Calculation models and methods

Here, MOF(1) and MOF(2) were used in the simulations. MOF(1) does not adsorb CO2 or N2, and MOF(2) does not adsorb N2 but does adsorb CO2. Both have isolated pores which seem to be inaccessible to even small gas molecules.

The Nudged Elastic Band (NEB) method was used to evaluate the diffusion path of CO2 through the MOF. The initial state (IS) consisted of one CO2 molecule enclosed inside a pore of a unit cell. Whereas in the final state (FS) , the same molecule was relocated into an adjacent closed pore. By the NEB method, their transition states were optimized.

image

Calculation Results and Discussion

The CO2 diffusion simulation result of MOF(2) is as the right fiture. In the transition state, their frameworks were slightly adjusted, opening up a channel between the neighboring pores. After CO2 passed through the channel, the passage closed behind and the MOF backbone returned to the initial state. This state was more energetically unstable than the initial and final states, suggesting that an energy barrier must be overcome as CO2 crosses between the pores.

Similar simulations for CO2 and N2 adsorption on MOF(1) and for N2 adsorption on MOF(2), the diffusion barriers are larger than for CO2 adsorption on MOF(2), which is consistent with the experimentally observed results. Additionally, X-ray crystallography did not confirm any clear structural transitions in the MOF before, during, or after adsorption, and the atomic behavior observed in the simulations supports the experimental findings.

Because of their complicated structure, the calculations of MOFs were computationally expensive and inaccurate. Thus, calculations of MOF adsorption properties have been widely used models that extract only substructures of MOFs, or that fixed atomic positions of MOFs. However, it is well known that MOFs often undergo structural changes during gas adsorption and that the flexibility contributes to their adsorption properties of them. The ability of Matlantis to perform high-speed calculations with flexibility of entire MOF structures is expected to have important implications for the future design of new MOFs.

Calculation Conditions

image

References

[1] T. Shimada et al. Adv. Sci. 2024, 11, 2307417 [2] G. Henkelman et al. J. Chem. Phys. 2000, 113, 9901. 

Related Info

[3] Lecture Video [4] Tokyo Institute of Technology Website
新規な「Magic door」機構によるCO2の選択的捕捉 カーボンニュートラル社会の実現へ向け革新的MOF素材を創出 | 東工大ニュース | 東京工業大学 (titech.ac.jp)
[5] ENEOS Corporation Website
新規な「Magic door」機構によるCO2の選択的捕捉に関する研究成果がAdvanced Scienceに掲載(2023年11月)|研究所NEWS & TOPICS|研究開発特設サイト (eneos-rd.com)

Contact Us
Go to List of Cases
Features
Features

Matlantis: 3 Key Features

Matlantis supports companies exploring innovative materials for a sustainable future.

Versatility

汎用性 / Versatile イメージ
Supports a wide range of elements and structures

Matlantis can simulate properties of molecules and crystal systems, including unknown materials. Any combinations of 72 elements are currently supported, and more elements will be added.

Speed

高速 / High Speed イメージ
Over 10,000x faster than conventional methods

The atomistic simulation tasks that take hours to months using density functional theory (DFT) on a high-performance computer can be finished in only a few seconds using Matlantis.

User-Friendliness

使いやすさ / Easy to Use イメージ
Just open your browser to run simulations

Thanks to the pre-trained deep learning model, physical property calculation library, and high-performance computing environment, no hardware or software installations are required for performing simulations. Unlike conventional machine learning potentials, Matlantis requires no data collection or training by users.

Go to Product Detail