Benchmarking various types of partial atomic charges for classical all-atom simulations of metal-organic frameworks

Computational Analysis of Sarin, Soman, and Their Water Mixtures in NU-1000: Interaction Mechanisms, Distribution Patterns, and Pairing Effects

Due to their extraordinary structural stability under humid conditions, zirconium-based metal-organic frameworks (Zr-MOFs) have been widely investigated for the hydrolytic degradation of nerve agents. That said, mechanisms of hydrolysis in the solid state and the participation of environmental water are not well understood. This work utilizes computational techniques to evaluate the behavior of water and two organophosphorus nerve agents (sarin and soman) in NU-1000, a Zr-MOF with the characteristic attributes for hydrolytic efficiency under humid conditions. Density functional theory (DFT) calculations reveal that soman binds more favorably to NU-1000 active sites than sarin, resulting in different preferential locations of each nerve agent within the framework. The strength of nerve agent binding is also found to vary depending on the site environment, with more favorable binding of both nerve agents occurring in the c-pores of NU-1000 than in the mesopores. Molecular dynamics (MD) simulation results further illustrate that free water molecules in NU-1000 prioritize interactions with nerve agents. Given the variation in their affinity for active site interactions, the introduction of different nerve agents to the framework results in substantial differences in water distribution and behavior. The results give insight into potential variances in the functionality of NU-1000 toward the hydrolysis of each nerve agent. More importantly, they emphasize the significance of considering the role of environmental water in hydrolysis and the possibility of diverse reaction variables based on the type of nerve agent and the properties of the MOF.

Diversity-driven, efficient exploration of a MOF design space to optimize MOF properties

Metal-organic frameworks (MOFs) promise to engender technology-enabling properties for numerous applications. However, one significant challenge in MOF development is their overwhelmingly large design space, which is intractable to fully explore even computationally. To find diverse optimal MOF designs without exploring the full design space, we develop Vendi Bayesian optimization (VBO), a new algorithm that combines traditional Bayesian optimization with the Vendi score, a recently introduced interpretable diversity measure. Both Bayesian optimization and the Vendi score require a kernel similarity function, we therefore also introduce a novel similarity function in the space of MOFs that accounts for both chemical and structural features. This new similarity metric enables VBO to find optimal MOFs with properties that may depend on both chemistry and structure. We statistically assessed VBO by its ability to optimize three NH3-adsorption dependent performance metrics that depend, to different degrees, on MOF chemistry and structure. With ten simulated campaigns done for each metric, VBO consistently outperformed random search to find high-performing designs within a 1000-MOF subset for (i) NH3 storage, (ii) NH3 removal from membrane plasma reactors, and (iii) NH3 capture from air. Then, with one campaign dedicated to finding optimal MOFs for NH3 storage in a "hybrid" ∼10 000-MOF database, we identify twelve extant and eight hypothesized MOF designs with potentially record-breaking working capacity ΔN NH3 between 300 K and 400 K at 1 bar. Specifically, the best MOF designs are predicted to (i) achieve ΔN NH3 values between 23.6 and 29.3 mmol g-1, potentially surpassing those that MOFs previously experimentally tested for NH3 adsorption would have at the proposed operation conditions, (ii) be thermally stable at the operation conditions and (iii) require only ca. 10% of the energy content in NH3 to release the stored molecule from the MOF. Finally, the analysis of the generated simulation data during the search indicates that a pore size of around 10 Å, a heat of adsorption around 33 kJ mol-1, and the presence of Ca could be part of MOF design rules that could help optimize NH3 working capacity at the proposed operation conditions.

MEPO-ML: a robust graph attention network model for rapid generation of partial atomic charges in metal-organic frameworks

Accurate computation of the gas adsorption properties of MOFs is usually bottlenecked by the DFT calculations required to generate partial atomic charges. Therefore, large virtual screenings of MOFs often use the QEq method which is rapid, but of limited accuracy. Recently, machine learning (ML) models have been trained to generate charges in much better agreement with DFT-derived charges compared to the QEq models. Previous ML charge models for MOFs have all used training sets with less than 3000 MOFs obtained from the CoRE MOF database, which has recently been shown to have high structural error rates. In this work, we developed a graph attention network model for predicting DFT-derived charges in MOFs where the model was developed with the ARC-MOF database that contains 279,632 MOFs and over 40 million charges. This model, which we call MEPO-ML, predicts charges with a mean absolute error of 0.025e on our test set of over 27 K MOFs. Other ML models reported in the literature were also trained using the same dataset and descriptors, and MEPO-ML was shown to give the lowest errors. The gas adsorption properties evaluated using MEPO-ML charges are found to be in significantly better agreement with the reference DFT-derived charges compared to the empirical charges, for both polar and non-polar gases. Using only a single CPU core on our benchmark computer, MEPO-ML charges can be generated in less than two seconds on average (including all computations required to apply the model) for MOFs in the test set of 27 K MOFs.

PACMAN: A Robust Partial Atomic Charge Predicter for Nanoporous Materials Based on Crystal Graph Convolution Networks

We report a fast and easy method (PACMAN) to assign partial atomic charges on metal-organic framework (MOF) and covalent-organic framework (COF) crystal structures based on graph convolution networks (GCNs) trained on >1.8 million high-fidelity partial atomic charge data obtained from the Quantum Metal-Organic Framework (QMOF) database. The developed model shows outstanding performance, achieving a mean absolute error (MAE) of 0.0055 e (test set performance) while maintaining consistency with DDEC6, Bader, and CM5 charges across diverse chemistry and topologies of MOFs and COFs. We find that the new method accurately assigns partial atomic charges for ion-containing nanoporous materials, which has not been possible in previous machine learning (ML) models. Grand canonical Monte Carlo (GCMC) simulation results for CO2 and N2 uptakes and the Widom particle insertion calculation for Henry's law constant of water results based on PACMAN and the original DDEC6 charges show excellent agreements compared to other ML models reported in the literature. The runtime analysis of the new method demonstrates that the partial atomic charges of MOF and COF structures with up to 500 atoms can be obtained in less than 10 s. An easy-to-use web interface has been developed to facilitate the adoption of the developed model.

CRAFTED: An exploratory database of simulated adsorption isotherms of metal-organic frameworks

Grand Canonical Monte Carlo is an important method for performing molecular-level simulations and assisting the study and development of nanoporous materials for gas capture applications. These simulations are based on the use of force fields and partial charges to model the interaction between the adsorbent molecules and the solid framework. The choice of the force field parameters and partial charges can significantly impact the results obtained, however, there are very few databases available to support a comprehensive impact evaluation. Here, we present a database of simulations of CO₂ and N₂ adsorption isotherms on 690 metal-organic frameworks taken from the CoRE MOF 2014 database. We performed simulations with two force fields (UFF and DREIDING), six partial charge schemes (no charges, Qeq, EQeq, MPNN, PACMOF, and DDEC), and three temperatures (273, 298, 323 K). The resulting isotherms compose the Charge-dependent, Reproducible, Accessible, Forcefield-dependent, and Temperature-dependent Exploratory Database (CRAFTED) of adsorption isotherms.