CMSP Webinar (Atomistic Simulation Seminar Series) 02/11, 5:00p.m. CET, Dr Yusuf Shaidu

[CMSP Seminars Secretariat]

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CMSP Seminar (Atomistic Simulation Seminar Series)
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** * * Wednesday**, 2 November 2022, 5:00p.m.**CET* * **
/Zoom registration link: 
https://zoom.us/meeting/register/tJ0pdeypqTIrE9a8rlFYfdrXN18w5J5bWepx/**

Speaker: *Yusuf Shaidu *(University of California & Lawrence Berkeley 
National Laboratory)

Title: *Accurate Neural Network Interatomic Potentials for Carbon 
Capture in Amine-appended Metal-organic Frameworks
*

Abstract:

The Mg2(dobpdc) is a metal organic framework (MOF) made up of Mg metal 
ions connected via a dobpdc4- (dobpdc4-= 
4,4'-dihydroxy-(1,1'-biphenyl)-3,3'-dicarboxylic acid) organic molecule 
to form a hexagonal porous material along the c crystallographic axis. 
The diamine-functionalized variant of Mg2(dobpdc) has been demonstrated 
as a transformative material for carbon capture applications. The 
functionalized materials form carbamate species upon CO2 insertion and 
exhibit cooperative adsorption mechanisms leading to a step-shaped 
isotherm that enables a full CO2 capacity to be accessed with a minimal 
temperature swing[1]. Thermal properties, CO2 adsorption kinetics and 
mechanisms of CO2 insertion are understudied due to the complexity of 
the materials. Finite temperature simulations of these materials are 
prohibitively costly because of the large number of atoms in a unit 
cell. Previous studies have been based on empirical force-fields whose 
parameters are not optimized to reproduce the density functional theory 
(DFT) energies and forces of these systems. Here, we present our work on 
the development of a reactive interatomic potential based on neural 
network techniques for amine-appended Mg2(dobpdc) systems. The neural 
network potentials (NNPs) belong to a class of reactive force-fields 
that allow to simulate bond breaking and formation which are prevalent 
during a finite temperature dynamics. The interatomic potentials here 
are constructed using the active learning approach combining the 
artificial neural network approaches and dispersion-corrected DFT. We 
show that the NNPs are accurate in predicting adsorption energy, 
mechanical, vibrational and thermal properties. In addition, we 
demonstrate that these potentials can be combined with a simulated 
annealing approach to find better starting structures for DFT energy 
minimization needed to compute CO2 binding energy.

Authors:
Yusuf Shaidu1,2, Alex Smith1,2, Eric Taw2,3 and Jeffrey B. Neaton1,2,4
1Physics Department, University of California, Berkeley, California, 
United States.
2Lawrence Berkeley National Laboratory, Berkeley, California, United States.
3Chemical and Biomolecular Engineering, University of California, 
Berkeley, California, United States.
4Kavli Energy NanoScience Institute, Berkeley, California, United States.

References:
[1] McDonald, T., Mason, J., Kong, X. et al. Nature 519, 303–308 (2015).
[2] Yusuf Shaidu et al. Accurate Neural Network Interatomic Potentials 
for Carbon Capture in Amine-appended Metal-organic Frameworks (In 
preparation)

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CMSP Seminars support:OnlineCMSP at ictp.it  

CMSP, Condensed Matter & Statistical Physics Section
http://www.ictp.it/research/cmsp.aspx

The Abdus Salam International Centre for Theoretical Physics
https://www.ictp.it/

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CMSP, Condensed Matter & Statistical Physics Section
http://www.ictp.it/research/cmsp.aspx

The Abdus Salam International Centre for Theoretical Physics
https://www.ictp.it/

CMSP Seminars support:OnlineCMSP at ictp.it  
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