A process-level perspective of the impact of molecular force fields on the computational screening of MOFs for carbon capture

被引:15
作者
Cleeton, Conor [1 ]
de Oliveira, Felipe Lopes [2 ,3 ]
Neumann, Rodrigo F. [2 ]
Farmahini, Amir H. [1 ]
Luan, Binquan [4 ]
Steiner, Mathias [2 ]
Sarkisov, Lev [1 ]
机构
[1] Univ Manchester, Manchester, England
[2] IBM Res, Av Republ Chile 330, BR-20031170 Rio De Janeiro, RJ, Brazil
[3] Univ Fed Rio De Janeiro, Inst Quim, Rio De Janeiro, RJ, Brazil
[4] IBM Res, 1101 Kitchawan Rd, Yorktown Hts, NY 10519 USA
基金
英国工程与自然科学研究理事会;
关键词
METAL-ORGANIC FRAMEWORKS; STRUCTURE-PROPERTY RELATIONSHIPS; PROCESS LANGMUIR MODEL; ATOMIC CHARGES; SWING ADSORPTION; CO2; CAPTURE; SURROGATE MODELS; POROUS MATERIALS; OPTIMIZATION; EQUILIBRATION;
D O I
10.1039/d3ee00858d
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
The question we pose in this study is to what extent the ranking of metal organic frameworks (MOFs) for adsorption-based carbon capture, and the selection of top performers identified in Pressure Swing Adsorption (PSA) process modelling, depends on the choice of the commonly available forcefields. To answer this question, we first generated distributions of CO2 and N-2 adsorption isotherms via molecular simulation in 690 MOFs using six typical forcefields: the UFF or Dreiding sets of Lennard-Jones parameters, in combination with partial charges derived from ab initio calculations or by charge equilibration schemes. We then conducted a systematic uncertainty quantification study using PSA process-level modelling. We observe that: (i) the ranking of MOFs significantly depends on the choice of forcefield; (ii) partial charge assignment is the prevailing source of uncertainty, and that charge equilibration schemes produce results which are inconsistent with ab initio-derived charges; (iii) the choice of Lennard-Jones parameters is a considerable source of uncertainty. Our work highlights that is not really possible to obtain material rankings with high resolution using a single molecular modelling approach and that, as a minimum, some uncertainty should be estimated for the performance of MOFs shortlisted using high throughput computational screening workflows. Future prospects for computational screening studies are also discussed.
引用
收藏
页码:3899 / 3918
页数:20
相关论文
共 100 条
[1]   Emerging CO2 capture systems [J].
Abanades, J. C. ;
Arias, B. ;
Lyngfelt, A. ;
Mattisson, T. ;
Wiley, D. E. ;
Li, H. ;
Ho, M. T. ;
Mangano, E. ;
Brandani, S. .
INTERNATIONAL JOURNAL OF GREENHOUSE GAS CONTROL, 2015, 40 :126-166
[2]   Role of partial charge assignment methods in high-throughput screening of MOF adsorbents and membranes for CO2/CH4 separation [J].
Altintas, Cigdem ;
Keskin, Seda .
MOLECULAR SYSTEMS DESIGN & ENGINEERING, 2020, 5 (02) :532-543
[3]   Database for CO2 Separation Performances of MOFs Based on Computational Materials Screening [J].
Altintas, Cigdem ;
Avci, Gokay ;
Daglar, Hilal ;
Azar, Ayda Nemati Vesali ;
Velioglu, Sadiye ;
Erucar, Ilknur ;
Keskin, Seda .
ACS APPLIED MATERIALS & INTERFACES, 2018, 10 (20) :17257-17268
[4]   Do New MOFs Perform Better for CO2 Capture and H2 Purification? Computational Screening of the Updated MOF Database [J].
Avci, Gokay ;
Erucar, Ilknur ;
Keskin, Seda .
ACS APPLIED MATERIALS & INTERFACES, 2020, 12 (37) :41567-41579
[5]   A WELL-BEHAVED ELECTROSTATIC POTENTIAL BASED METHOD USING CHARGE RESTRAINTS FOR DERIVING ATOMIC CHARGES - THE RESP MODEL [J].
BAYLY, CI ;
CIEPLAK, P ;
CORNELL, WD ;
KOLLMAN, PA .
JOURNAL OF PHYSICAL CHEMISTRY, 1993, 97 (40) :10269-10280
[6]   Multi-objective optimisation using surrogate models for the design of VPSA systems [J].
Beck, Joakim ;
Friedrich, Daniel ;
Brandani, Stefano ;
Fraga, Eric S. .
COMPUTERS & CHEMICAL ENGINEERING, 2015, 82 :318-329
[7]   Polarizable Force Fields for CO2 and CH4 Adsorption in M-MOF-74 [J].
Becker, Tim M. ;
Heinen, Jurn ;
Dubbeldam, David ;
Lin, Li-Chiang ;
Vugt, Thijs J. H. .
JOURNAL OF PHYSICAL CHEMISTRY C, 2017, 121 (08) :4659-4673
[8]   Generalized neural-network representation of high-dimensional potential-energy surfaces [J].
Behler, Joerg ;
Parrinello, Michele .
PHYSICAL REVIEW LETTERS, 2007, 98 (14)
[9]   Four Generations of High-Dimensional Neural Network Potentials [J].
Behler, Joerg .
CHEMICAL REVIEWS, 2021, 121 (16) :10037-10072
[10]   Perspective: Machine learning potentials for atomistic simulations [J].
Behler, Joerg .
JOURNAL OF CHEMICAL PHYSICS, 2016, 145 (17)