Boosting Graph Neural Networks with Molecular Mechanics: A Case Study of Sigma Profile Prediction.

Sigma profiles are quantum-chemistry-derived molecular descriptors that encode the polarity of molecules. They have shown great performance when used as a feature in machine learning applications. To accelerate the development of these models and the construction of large sigma profile databases, this work proposes a graph convolutional network (GCN) architecture to predict sigma profiles from molecule structures. To do so, the usage of molecular mechanics (force field atom types) is explored as a computationally inexpensive node-level featurization technique to encode the local and global chemical environments of atoms in molecules. The GCN models developed in this work accurately predict the sigma profiles of assorted organic and inorganic compounds. The best GCN model here reported, obtained using Merck molecular force field (MMFF) atom types, displayed training and testing set coefficients of determination of 0.98 and 0.96, respectively, which are superior to previous methodologies reported in the literature. This performance boost is shown to be due to both the usage of a convolutional architecture and node-level features based on force field atom types. Finally, to demonstrate their practical applicability, we used GCN-predicted sigma profiles as the input to machine learning models previously developed in the literature that predict boiling temperatures and aqueous solubilities. Using the predicted sigma profiles as input, these models were able to compute both physicochemical properties using significantly less computational resources and displayed only a slight decrease in performance when compared with sigma profiles obtained from quantum chemistry methods.

Molecular Dynamics Simulation of the Influence of External Electric Fields on the Glass Transition Temperature of the Ionic Liquid 1-Ethyl-3-methylimidazolium Bis(trifluoromethylsulfonyl)imide.

We present the results of molecular dynamics simulations of the ionic liquid (IL) 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide [C2C1im][NTf2] in the presence of external electric fields (EEFs) of varying strengths to understand the effects of EEFs on the glass transition temperature Tg. We compute Tg with an automated and objective method and observe a depression in Tg when cooling the IL within an EEF above a critical strength. The effect is reversible, and glasses prepared with EEFs recover their original zero-field Tg when heated. By examining the dynamics and structure of the liquid phase, we find that the EEF lowers the activation energy for diffusion, reducing the energetic barrier for movement and consequently Tg. We show that the effect can be leveraged to drive an electrified nonvapor compression refrigeration cycle.

Machine Learning Identification of Organic Compounds Using Visible Light.

Identifying chemical compounds is essential in several areas of science and engineering. Laser-based techniques are promising for autonomous compound detection because the optical response of materials encodes enough electronic and vibrational information for remote chemical identification. This has been exploited using the fingerprint region of infrared absorption spectra, which involves a dense set of absorption peaks that are unique to individual molecules, thus facilitating chemical identification. However, optical identification using visible light has not been realized. Using decades of experimental refractive index data in the scientific literature of pure organic compounds and polymers over a broad range of frequencies from the ultraviolet to the far-infrared, we develop a machine learning classifier that can accurately identify organic species based on a single-wavelength dispersive measurement in the visible spectral region, away from absorption resonances. The optical classifier proposed here could be applied to autonomous material identification protocols and applications.

Atomistic simulation of soft porous coordination polymers.

Soft porous coordination polymers (SPCPs) are materials with exceptional potential because of their ability to incorporate the properties of nominally rigid porous materials like metal-organic frameworks (MOFs) and those of soft matter, such as polymers of intrinsic microporosity (PIMs). This combination could offer the gas adsorption properties of MOFs together with the mechanical stability and processability of PIMs, opening up a space of flexible, highly responsive adsorbing materials. In order to understand their structure and behavior, we present a process for the construction of amorphous SPCPs from secondary building blocks. We then use classical molecular dynamics simulations to characterize the resulting structures based on branch functionalities (f), pore size distributions (PSDs), and radial distribution functions and compare them to experimentally synthesized analogs. In the course of this comparison, we demonstrate that the pore structure of SPCPs is due to both pores intrinsic to the secondary building blocks, and intercolloid spacing between colloid particles. We also illustrate the differences in nanoscale structure based on linker length and flexibility, particularly in the PSDs, finding that stiff linkers tend to produce SPCPs with larger maximum pore sizes.

How Reproducible are Surface Areas Calculated from the BET Equation?

Porosity and surface area analysis play a prominent role in modern materials science. At the heart of this sits the Brunauer-Emmett-Teller (BET) theory, which has been a remarkably successful contribution to the field of materials science. The BET method was developed in the 1930s for open surfaces but is now the most widely used metric for the estimation of surface areas of micro- and mesoporous materials. Despite its widespread use, the calculation of BET surface areas causes a spread in reported areas, resulting in reproducibility problems in both academia and industry. To prove this, for this analysis, 18 already-measured raw adsorption isotherms were provided to sixty-one labs, who were asked to calculate the corresponding BET areas. This round-robin exercise resulted in a wide range of values. Here, the reproducibility of BET area determination from identical isotherms is demonstrated to be a largely ignored issue, raising critical concerns over the reliability of reported BET areas. To solve this major issue, a new computational approach to accurately and systematically determine the BET area of nanoporous materials is developed. The software, called "BET surface identification" (BETSI), expands on the well-known Rouquerol criteria and makes an unambiguous BET area assignment possible.

Sigma profiles in deep learning: towards a universal molecular descriptor.

This work showcases the remarkable ability of sigma profiles to function as molecular descriptors in deep learning. The sigma profiles of 1432 compounds are used to train convolutional neural networks that accurately correlate and predict a wide range of physicochemical properties. The architectures developed are then exploited to include temperature as an additional feature.

Multiscale structural control of linked metal-organic polyhedra gel by aging-induced linkage-reorganization.

Assembly of permanently porous metal-organic polyhedra/cages (MOPs) with bifunctional linkers leads to soft supramolecular networks featuring both porosity and processability. However, the amorphous nature of such soft materials complicates their characterization and thus limits rational structural control. Here we demonstrate that aging is an effective strategy to control the hierarchical network of supramolecular gels, which are assembled from organic ligands as linkers and MOPs as junctions. Normally, the initial gel formation by rapid gelation leads to a kinetically trapped structure with low controllability. Through a controlled post-synthetic aging process, we show that it is possible to tune the network of the linked MOP gel over multiple length scales. This process allows control on the molecular-scale rearrangement of interlinking MOPs, mesoscale fusion of colloidal particles and macroscale densification of the whole colloidal network. In this work we elucidate the relationships between the gel properties, such as porosity and rheology, and their hierarchical structures, which suggest that porosity measurement of the dried gels can be used as a powerful tool to characterize the microscale structural transition of their corresponding gels. This aging strategy can be applied in other supramolecular polymer systems particularly containing kinetically controlled structures and shows an opportunity to engineer the structure and the permanent porosity of amorphous materials for further applications.

Engineering entangled photon pairs with metal-organic frameworks.

The discovery and design of new materials with competitive optical frequency conversion efficiencies can accelerate the development of scalable photonic quantum technologies. Metal-organic framework (MOF) crystals without inversion symmetry have shown potential for these applications, given their nonlinear optical properties and the combinatorial number of possibilities for MOF self-assembly. In order to accelerate the discovery of MOF materials for quantum optical technologies, scalable computational assessment tools are needed. We develop a multi-scale methodology to study the wavefunction of entangled photon pairs generated by selected non-centrosymmetric MOF crystals via spontaneous parametric down-conversion (SPDC). Starting from an optimized crystal structure, we predict the shape of the G (2) intensity correlation function for coincidence detection of the entangled pairs, produced under conditions of collinear type-I phase matching. The effective nonlinearities and photon pair correlation times obtained are comparable to those available with inorganic crystal standards. Our work thus provides fundamental insights into the structure-property relationships for entangled photon generation with metal-organic frameworks, paving the way for the automated discovery of molecular materials for optical quantum technology.

Transfer Learning Study of Gas Adsorption in Metal-Organic Frameworks.

Metal-organic frameworks (MOFs) are a class of materials promising for gas adsorption due to their highly tunable nanoporous structures and host-guest interactions. While machine learning (ML) has been leveraged to aid the design or screen of MOFs for different purposes, the needs of big data are not always met, limiting the applicability of ML models trained against small data sets. In this work, we introduce an inductive transfer learning technique to improve the accuracy and applicability of ML models trained with a small amount of MOF adsorption data. This technique leverages potentially shareable knowledge from a source task to improve the models on the target tasks. As demonstrations, a deep neural networks (DNNs) trained on H2 adsorption data with 13 506 MOF structures at 100 bar and 243 K is used as the source task. When transferring knowledge from the source task to H2 adsorption at 100 bar and 130 K (one target task), the average predictive accuracy on target tasks was improved from 0.960 (direct training) to 0.991 (transfer learning), and transfer learning works in 89.3% of the cases. We also tested transfer learning across different gas species (i.e., from H2 to CH4), with an average predictive accuracy of CH4 adsorption being improved from 0.935 (direct training) to 0.980 (transfer learning), and transfer learning works in 82.0% of the cases. More importantly, transfer learning is shown to effectively improve the models on the target tasks with low accuracy from direct training. However, when transferring the knowledge from the source task to Xe/Kr adsorption, the transfer learning does not improve the predictive accuracy significantly and even makes it worse in ∼50.0% of the cases, which is attributed to the lack of common descriptors that is key to the underlying knowledge.

The role of molecular modelling and simulation in the discovery and deployment of metal-organic frameworks for gas storage and separation.

Metal-organic frameworks (MOFs) are highly tuneable, extended-network, crystalline, nanoporous materials with applications in gas storage, separations, and sensing. We review how molecular models and simulations of gas adsorption in MOFs have informed the discovery of performant MOFs for methane, hydrogen, and oxygen storage, xenon, carbon dioxide, and chemical warfare agent capture, and xylene enrichment. Particularly, we highlight how large, open databases of MOF crystal structures, post-processed to enable molecular simulations, are a platform for computational materials discovery. We discuss how to orient research efforts to routinise the computational discovery of MOFs for adsorption-based engineering applications.

The effect of co-adsorbed solvent molecules on H2 binding to metal alkoxides.

The introduction of metal alkoxides has been proposed as an attractive option to enhance hydrogen binding energies in porous materials such as metal-organic frameworks (MOFs) for room-temperature hydrogen storage applications. The presence of residual solvent molecules from MOF synthesis can, however, affect the performance of these functional groups. We perform quantum chemical calculations to predict solvent binding energies onto the metal-alkoxides and the temperatures required to drive off the solvent molecules and successfully activate porous materials with these moieties. Calculations are performed for Li, Mg, Zn, Cu, and Ni alkoxides and chloroform (CHCl3), dimethylformamide (DMF), ethanol, methanol, and water solvent molecules. We identify CHCl3 as a promising solvent that can be removed from these alkoxides at mild temperatures, whereas DMF binds strongly to the metal alkoxides and removal would require temperatures above the present upper bound of thermal stability in MOFs. As a second objective, we calculated the binding energies of hydrogen to metal alkoxide-solvent complexes to explore the effect of any solvent molecules that cannot be removed.

Free energy of metal-organic framework self-assembly.

Metal-organic frameworks (MOFs) represent an important class of materials. Careful selection of building blocks allows for tailoring of the properties of the resulting framework. The self-assembly process, however, is not understood, and without detailed knowledge of the underlying molecular mechanism, it is difficult to anticipate whether a particular design can be realized, or whether the material adopts a metastable, kinetically arrested state. We present a detailed examination of early-stage self-assembly pathways of the MOF-5. Enhanced sampling techniques are used to model a self-assembly in an explicit solvent (dimethylformamide, DMF). We identify several free energy barriers encountered during the assembly of the final MOF, which arise from structural rearrangements preceding MOF formation and from disrupted MOF-solvent interactions as formation proceeds. In all cases considered here, MOFs exhibit favorable entropic gains during the assembly. More generally, the strategy presented provides a step toward the experimental design characterizing the formation of ordered frameworks and possible sources of polymorphism.

Intra-molecular Charge Transfer and Electron Delocalization in Non-fullerene Organic Solar Cells.

Two types of electron acceptors were synthesized by coupling two kinds of electron-rich cores with four equivalent perylene diimides (PDIs) at the α-position. With fully aromatic cores, TPB and TPSe have π-orbitals spread continuously over the whole aromatic conjugated backbone, unlike TPC and TPSi, which contain isolated PDI units due to the use of a tetrahedron carbon or silicon linker. Density functional theory calculations of the projected density of states showed that the highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) for TPB are localized in separate regions of space. Further, the LUMO of TPB shows a greater contribution from the orbitals belonging to the connective core of the molecules than that of TPC. Overall, the properties of the HOMO and LUMO point at increased intra-molecular delocalization of negative charge carriers for TPB and TPSe than for TPC and TPSi and hence at a more facile intra-molecular charge transfer for the former. The film absorption and emission spectra showed evidences for the inter-molecular electron delocalization in TPB and TPSe, which is consistent with the network structure revealed by X-ray diffraction studies on single crystals of TPB. These features benefit the formation of charge transfer states and/or facilitate charge transport. Thus, higher electron mobility and higher charge dissociation probabilities under Jsc condition were observed in blend films of TPB:PTB7-Th and TPSe:PTB7-Th than those in TPC:PTB7-Th and TPSi:PTB7-Th blend films. As a result, the Jsc and fill factor values of 15.02 mA/cm2, 0.58 and 14.36 mA/cm2, 0.55 for TPB- and TPSe-based solar cell are observed, whereas those for TPC and TPSi are 11.55 mA/cm2, 0.47 and 10.35 mA/cm2, 0.42, respectively.

SSAGES: Software Suite for Advanced General Ensemble Simulations.

Molecular simulation has emerged as an essential tool for modern-day research, but obtaining proper results and making reliable conclusions from simulations requires adequate sampling of the system under consideration. To this end, a variety of methods exist in the literature that can enhance sampling considerably, and increasingly sophisticated, effective algorithms continue to be developed at a rapid pace. Implementation of these techniques, however, can be challenging for experts and non-experts alike. There is a clear need for software that provides rapid, reliable, and easy access to a wide range of advanced sampling methods and that facilitates implementation of new techniques as they emerge. Here we present SSAGES, a publicly available Software Suite for Advanced General Ensemble Simulations designed to interface with multiple widely used molecular dynamics simulations packages. SSAGES allows facile application of a variety of enhanced sampling techniques-including adaptive biasing force, string methods, and forward flux sampling-that extract meaningful free energy and transition path data from all-atom and coarse-grained simulations. A noteworthy feature of SSAGES is a user-friendly framework that facilitates further development and implementation of new methods and collective variables. In this work, the use of SSAGES is illustrated in the context of simple representative applications involving distinct methods and different collective variables that are available in the current release of the suite. The code may be found at: https://github.com/MICCoM/SSAGES-public.

Hierarchically porous organic polymers: highly enhanced gas uptake and transport through templated synthesis.

Porous organic polymers (POPs) possessing meso- and micropores can be obtained by carrying out the polymerization inside a mesoporous silica aerogel template and then removing the template after polymerization. The total pore volume (tpv) and specific surface area (ssa) can be greatly enhanced by modifying the template (up to 210% increase for tpv and 73% for ssa) as well as by supercritical processing of the POPs (up to an additional 142% increase for tpv and an additional 32% for ssa) to include larger mesopores. The broad range of pores allows for faster transport of molecules through the hierarchically porous POPs, resulting in increased diffusion rates and faster gas uptake compared to POPs with only micropores.

Water adsorption in UiO-66: the importance of defects.

Simulated adsorption isotherms for water in UiO-66 illustrate that defects in the form of missing linkers make this MOF more hydrophilic. Heats of adsorption and density plots further confirm the effect of defects on adsorption of water in UiO-66 at low loadings. In addition, water and CO2 isotherms indicate that not only the amount of defects but their locations within the material affect the loading of guest molecules.

High-throughput computational screening of metal-organic frameworks.

There is an almost unlimited number of metal-organic frameworks (MOFs). This creates exciting opportunities but also poses a problem: how do we quickly find the best MOFs for a given application? Molecular simulations have advanced sufficiently that many MOF properties - especially structural and gas adsorption properties - can be predicted computationally, and molecular modeling techniques are now used increasingly to guide the synthesis of new MOFs. With increasing computational power and improved simulation algorithms, it has become possible to conduct high-throughput computational screening to identify promising MOF structures and uncover structure-property relations. We review these efforts and discuss future directions in this new field.

A facile synthesis of UiO-66, UiO-67 and their derivatives.

A scalable, reproducible method of synthesizing UiO-66- and UiO-67-type MOFs, entailing the addition of HCl to the reaction mixture, has been investigated. The new protocol requires a fraction of the time of previously reported procedures, yields exceptional porosities, and works with a range of linkers.

Understanding excess uptake maxima for hydrogen adsorption isotherms in frameworks with rht topology.

For a series of metal-organic frameworks with rht topology, we study computationally the effect of the linker length on the surface area, pore size, and pore volume, relating them with the hydrogen adsorption properties. The results provide new insights about the excess capacities and the pressures where the uptake maxima in the excess isotherms occur. We found that, of the materials studied, has the optimal pore volume for excess gravimetric hydrogen uptake.