Assessing UFF and DFT-Tuned Force Fields for Predicting Experimental Isotherms of MOFs.

IF 5.3 2区 化学 Q1 CHEMISTRY, MEDICINAL Journal of Chemical Information and Modeling Pub Date : 2025-04-14 Epub Date: 2025-03-18 DOI:10.1021/acs.jcim.4c02044
Yeongsu Cho, Jakob Teetz, Heather J Kulik
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Abstract

Metal-organic frameworks (MOFs) are promising materials for gas storage and separation applications due to their high tunability and porosity. The rational design of MOFs relies on accurate computational modeling, with grand canonical Monte Carlo (GCMC) simulations frequently employed to model gas uptake. However, GCMC predictions often deviate from experimental observations, limiting their utility in MOF screening. These discrepancies primarily arise from three factors: inaccuracies in the force field, neglect of atomic motions, and neglect of structural imperfections in MOFs. In this study, we systematically evaluate the impact of the first factor on the predictive accuracy of the GCMC simulations. We evaluate the widely used Universal Force Field (UFF) by comparing its predictions with experimental isotherms for four representative adsorbates, H2, CO2, C2H4, and C2H6, across 379 isotherms from 142 MOFs. The results show that UFF consistently overestimates the gas uptake in GCMC simulations. To isolate the contribution of force field inaccuracies to errors in GCMC, we developed a practical scheme for fitting force field parameters to DFT-calculated energies for a large set of MOFs. While the refined force field improves the accuracy of interatomic interaction energies, its reduction of repulsion, combined with UFF's tendency to overestimate gas uptake, ultimately amplifies the overestimation of experimental gas uptake meaurement. Our analysis suggests that improving the agreement of gas adsorption prediction with experiments requires addressing atomic motion and structural defects in MOFs alongside force field refinements.

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预测mof实验等温线的UFF和dft调谐力场评估。
金属有机骨架(mof)由于其高可调性和高孔隙率,在气体储存和分离领域具有广阔的应用前景。mof的合理设计依赖于精确的计算建模,通常采用大正则蒙特卡罗(GCMC)模拟来模拟气体吸收。然而,GCMC预测经常偏离实验观察,限制了它们在MOF筛选中的应用。这些差异主要是由三个因素引起的:力场的不精确,原子运动的忽视,以及mof结构缺陷的忽视。在本研究中,我们系统地评估了第一个因素对GCMC模拟预测精度的影响。我们通过将其预测结果与来自142个mof的379条等温线中H2、CO2、C2H4和C2H6四种代表性吸附的实验等温线进行比较,来评估广泛使用的通用力场(UFF)。结果表明,在GCMC模拟中,UFF一直高估了气体吸收量。为了隔离力场误差对GCMC误差的影响,我们开发了一种实用的方案,将力场参数拟合到大量mof的dft计算能量中。虽然精细化力场提高了原子间相互作用能的准确性,但其排斥力的降低,加上UFF对气体吸收量的高估,最终放大了实验气体吸收量的高估。我们的分析表明,提高气体吸附预测与实验的一致性需要解决mof中的原子运动和结构缺陷以及力场的改进。
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来源期刊
CiteScore
9.80
自引率
10.70%
发文量
529
审稿时长
1.4 months
期刊介绍: The Journal of Chemical Information and Modeling publishes papers reporting new methodology and/or important applications in the fields of chemical informatics and molecular modeling. Specific topics include the representation and computer-based searching of chemical databases, molecular modeling, computer-aided molecular design of new materials, catalysts, or ligands, development of new computational methods or efficient algorithms for chemical software, and biopharmaceutical chemistry including analyses of biological activity and other issues related to drug discovery. Astute chemists, computer scientists, and information specialists look to this monthly’s insightful research studies, programming innovations, and software reviews to keep current with advances in this integral, multidisciplinary field. As a subscriber you’ll stay abreast of database search systems, use of graph theory in chemical problems, substructure search systems, pattern recognition and clustering, analysis of chemical and physical data, molecular modeling, graphics and natural language interfaces, bibliometric and citation analysis, and synthesis design and reactions databases.
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