Polymatic: a generalized simulated polymerization algorithm for amorphous polymers

Large-Scale Construction and Analysis of Amorphous Porous Polymer Network Materials

In recent decades, data-driven methodologies have emerged as irreplaceable tools in materials science, particularly for elucidating structure-property relationships and facilitating the discovery of novel materials. However, despite the rapid development witnessed in other domains, amorphous materials have received relatively less attention in this context. The disordered atomic structure of amorphous materials resulting from irreversible reactions between building blocks has posed a difficulty in structural modeling, leading to a lack of databases that accurately reflect the amorphous nature of these materials. In this work, a database composed of 10,237 porous polymer networks (PPNs) was constructed from self-assembly simulations, resulting in the largest database of PPNs considering their amorphous characteristics. Through the distinct differences observed in comparison with existing databases, we emphasize that carefully considering the structural disorder of PPNs is essential for accurately characterizing their chemical behaviors. Machine learning models trained on the constructed database have confirmed that the macroscopic properties of amorphous PPNs can be predicted solely from the atomic structures of their monomers, implying that the characteristics of previously unseen PPNs can be assessed without the need for additional self-assembly simulations.

GMXPolymer: a generated polymerization algorithm based on GROMACS

CONTEXT

This work introduces a method for generating generalized structures of amorphous polymers using simulated polymerization and molecular dynamics equilibration, with a particular focus on amorphous polymers. The techniques and algorithms used in this method are described in the main text, and example input scripts are provided for the GMXPolymer code, which is based on the GROMACS molecular dynamics package. To demonstrate the efficacy of our method, we apply it to different glassy polymers exhibiting varying degrees of functionality, polarity, and rigidity. The reliability of the method is validated by comparing simulation results with experimental data in various structural and thermal properties, both of which show excellent agreement.

METHODS

This work implements the GMXPolymer simulated polymerization algorithm on the GROMACS program. GMXPolymer code controls the main polymerization loop. The energy minimizations and molecular dynamics simulations use the GROMACS program called by the GMXPolymer code. A new ITP file is generated when a new bond is formed, and the necessary additions to the ITP file are made to include new bonds, angles, and dihedrals. In preparing the ITP file of the monomer, the charge of the reactive atom must be modified before the code runs so that it is a correct value after bonding.

Tuning (Photo)Electronic Properties of an Electron Deficient Porous Polymer via n-Doping with Tetrathiafulvalene

Charge-transfer complex formation within the pores of porous polymers is an efficient way to tune their electronical properties. Introduction of electron accepting guests to the electron donating hosts to conduct their p-doping is intensively studied in this context. However, the vice versa scenario, n-doping by treating the electron deficient (i.e., n-type) porous polymers with electron donating dopants, is rare. In this work, synthesis of an n-type phenazine based conjugated microporous polymer and its exposure to strong electron donating tetrathiafulvalene (TTF) dopants are presented. The fundamental physical characterizations (e.g., elemental analysis, gas sorption) showed that the vacuum impregnation technique is a good approach to load the guest molecules inside the pores. Moreover, the formation of charge-transfer complexes between the phenazine building blocks of the polymeric network and TTF dopants are confirmed via spectral techniques such Fourier transform infra-red, UV-vis, steady-state/time-resolved photoluminescence, and transient absorbance spectroscopies. Effect of the doping to the electronical properties is monitored by employing photoelectrochemical measurements, which showed lower charge-transfer resistivity and nearly doubled photocurrents after the doping. The study is, therefore, an important advancement for the applicability of (n-type) porous polymeric materials in the field of photo(electro)catalysis and organic electronics.

Novel Discrete and Imprinted Fluoride-Selective Sensors: Bridging the Gap from DMSO to Aqueous Samples

Fluoride in drinking water has beneficial or harmful health effects depending on its concentration. This highlights the need for new low-cost and portable sensors capable of in situ monitoring of F- ions. Unfortunately, achieving high levels of water compatibility and fluoride specificity remains a challenge. Here, four new urea-based discrete sensors are prepared and characterized. The sensors containing anthracenyl- (5) and 9H-fluorenyl- (7) signaling units exhibit intense luminescent emissions in dimethyl sulfoxide, the former being particularly sensitive and selective to fluoride. In water, 5 displays a superior sensitivity (871 M-1) and a detection limit (8 µm) below international guidelines, albeit with cross-sensitivity to H2PO4‾. To enhance the performance, 5 and 7 are embedded into a fluoride-imprinted polymeric matrix to give solid-state sensors (5P and 7P, respectively). 5P shows good sensitivity (360 M-1) and specificity in water. Besides, it has a low detection limit (35 µm) and a response linear range (118-6300 µm) encompassing the limit established by the Environmental Protection Agency (211 µm). Furthermore, 5P also displays good reusability and adequate recovery values in real-sample testing (102 ± 2%), constituting the first example of a low-cost anion-imprinted polymeric probe tailored for the selective sensing of fluoride in aqueous samples.

Tuning MOF/polymer interfacial pore geometry in mixed matrix membrane for upgrading CO2 separation performance

The current paradigm considers the control of the MOF/polymer interface mostly for achieving a good compatibility between the two components to ensure the fabrication of continuous mixed-matrix metal-organic framework (MMMOF) membranes. Here, we unravel that the interfacial pore shape nanostructure plays a key role for an optimum molecular transport. The prototypical ultrasmall pore AlFFIVE-1-Ni MOF was assembled with the polymer PIM-1 to design a composite with gradually expanding pore from the MOF entrance to the MOF/polymer interfacial region. Concentration gradient-driven molecular dynamics simulations demonstrated that this pore nanostructuring enables an optimum guided path for the gas molecules at the MOF/polymer interface that decisively leads to an acceleration of the molecular transport all along the MMMOF membrane. This numerical prediction resulted in the successful fabrication of a [001]-oriented nanosheets AlFFIVE-1-Ni/PIM-1 MMMOF membrane exhibiting an excellent CO2 permeability, better than many MMMs, and ideally associated with a sufficiently high CO2/CH4 selectivity that makes this membrane very promising for natural gas/biogas purification.

Supported Ionic Liquid Membrane with Highly-permeable Polyamide Armor by In Situ Interfacial Polymerization for Durable CO2 Separation

Supported ionic liquid membranes (SILMs), owing to their capacities in harnessing physicochemical properties of ionic liquid for exceptional CO2 solubility, have emerged as a promising platform for CO2 extraction. Despite great achievements, existing SILMs suffer from poor structural and performance stability under high-pressure or long-term operations, significantly limiting their applications. Herein, a one-step and in situ interfacial polymerization strategy is proposed to elaborate a thin, mechanically-robust, and highly-permeable polyamide armor on the SILMs to effectively protect ionic liquid within porous supports, allowing for intensifying the overall stability of SILMs without compromising CO2 separation performance. The armored SILMs have a profound increase of breakthrough pressure by 105% compared to conventional counterparts without armor, and display high and stable operating pressure exceeding that of most SILMs previously reported. It is further demonstrated that the armored SILMs exhibit ultrahigh ideal CO2/N2 selectivity of about 200 and excellent CO2 permeation of 78 barrers upon over 150 h operation, as opposed to the full failure of CO2 separation performance within 36 h using conventional SILMs. The design concept of armor provides a flexible and additional dimension in developing high-performance and durable SILMs, pushing the practical application of ionic liquids in separation processes.

Scalable Preparation of Ultraselective and Highly Permeable Fully Aromatic Polyamide Nanofiltration Membranes for Antibiotic Desalination

Membranes are important in the pharmaceutical industry for the separation of antibiotics and salts. However, its widespread adoption has been hindered by limited control of the membrane microstructure (pore architecture and free-volume elements), separation threshold, scalability, and operational stability. In this study, 4,4',4'',4'''-methanetetrayltetrakis(benzene-1,2-diamine) (MTLB) as prepared as a molecular building block for fabricating thin-film composite membranes (TFCMs) via interfacial polymerization. The relatively large molecular size and rigid molecular structure of MTLB, along with its non-coplanar and distorted conformation, produced thin and defect-free selective layers (~27 nm) with ideal microporosities for antibiotic desalination. These structural advantages yielded an unprecedented high performance with a water permeance of 45.2 L m-2 h-1 bar-1 and efficient antibiotic desalination (NaCl/adriamycin selectivity of 422). We demonstrated the feasibility of the industrial scaling of the membrane into a spiral-wound module (with an effective area of 2.0 m2). This module exhibited long-term stability and performance that surpassed those of state-of-the-art membranes used for antibiotic desalination. This study provides a scientific reference for the development of high-performance TFCMs for water purification and desalination in the pharmaceutical industry.

Design of Mixed-Matrix MOF Membranes with Asymmetric Filler Density and Intrinsic MOF/Polymer Compatibility for Enhanced Molecular Sieving

The separation of high-value-added chemicals from organic solvents is important for many industries. Membrane-based nanofiltration offers a more energy-efficient separation than the conventional thermal processes. Conceivably, mixed-matrix membranes (MMMs), encompassing metal-organic frameworks (MOFs) as fillers, are poised to promote selective separation via molecular sieving, synergistically combining polymers flexibility and fine-tuned porosity of MOFs. Nevertheless, conventional direct mixing of MOFs with polymer solutions results in underutilization of the MOF fillers owing to their uniform cross-sectional distribution. Therefore, in this work, a multizoning technique is proposed to produce MMMs with an asymmetric-filler density, in which the MOF fillers are distributed only on the surface of the membrane, and a seamless interface at the nanoscale. The design strategy demonstrates five times higher MOF surface coverage, which results in a solvent permeance five times higher than that of conventional MMMs while maintaining high selectivity. Practically, MOFs are paired with polymers of similar chemical nature to enhance their adhesion without the need for surface modification. The approach offers permanently accessible MOF porosity, which translates to effective molecular sieving, as exemplified by the polybenzimidazole and Zr-BI-fcu-MOF system. The findings pave the way for the development of composite materials with a seamless interface.

Beyond nothingness in the formation and functional relevance of voids in polymer films

Voids-the nothingness-broadly exist within nanomaterials and impact properties ranging from catalysis to mechanical response. However, understanding nanovoids is challenging due to lack of imaging methods with the needed penetration depth and spatial resolution. Here, we integrate electron tomography, morphometry, graph theory and coarse-grained molecular dynamics simulation to study the formation of interconnected nanovoids in polymer films and their impacts on permeance and nanomechanical behaviour. Using polyamide membranes for molecular separation as a representative system, three-dimensional electron tomography at nanometre resolution reveals nanovoid formation from coalescence of oligomers, supported by coarse-grained molecular dynamics simulations. Void analysis provides otherwise inaccessible inputs for accurate fittings of methanol permeance for polyamide membranes. Three-dimensional structural graphs accounting for the tortuous nanovoids within, measure higher apparent moduli with polyamide membranes of higher graph rigidity. Our study elucidates the significance of nanovoids beyond the nothingness, impacting the synthesis‒morphology‒function relationships of complex nanomaterials.

An Accurate and Transferable Coarse-Graining Method for the Investigation of Microscopic Fracture Behaviors of Epoxy Thermosets

Coarse-grained modeling shows potential in exploring the thermo-mechanical behaviors of polymers applied in harsh conditions such as cryogenic environment, but its accuracy in simulating fracture behaviors of highly cross-linked epoxy thermosets is largely limited due to the complex molecular structures of the cross-linked networks. We address this fundamental problem by developing a CG modeling method where the backbones and electrostatic interaction (EI) contributions in the cross-linked networks are retained, and thus the potentials of the CG model can be directly extracted, or parametrized on the basis of, existing all-atomistic (AA) force fields. A multilevel parametrization procedure was adopted, where the bond potentials were parametrized relying on the results of density functional theory (DFT) simulation, whereas the nonbond potentials were parametrized by renormalizing the cohesive interaction strength. Remarkably, the CG model can reproduce stress-strain responses highly consistent with the AA simulation results at multiple stages, including elastic deformation, yielding, plastic flow, strain hardening, etc., and the straightforward parametrization procedure can be easily transferred to different materials and thermodynamic conditions. The CG modeling method was then used to build a large-scale representative volume element (RVE) to investigate the microscopic fracture behavior of an epoxy thermoset. It has been discovered that EI contributions play a significant role in generating correct mechanical responses and fracture morphologies. The influences of temperature (i.e., from room to cryogenic temperatures) and strain rates were discussed, and the fracture morphology in the RVE was unveiled and analyzed in a quantitative manner.

Molecular Dynamics Simulations for Rationalizing Polymer Bioconjugation Strategies: Challenges, Recent Developments, and Future Opportunities

The covalent modification of proteins with polymers is a well-established method for improving the pharmacokinetic properties of therapeutically valuable biologics. The conjugated polymer chains of the resulting hybrid represent highly flexible macromolecular structures. As the dynamics of such systems remain rather elusive for established experimental techniques from the field of protein structure elucidation, molecular dynamics simulations have proven as a valuable tool for studying such conjugates at an atomistic level, thereby complementing experimental studies. With a focus on new developments, this review aims to provide researchers from the polymer bioconjugation field with a concise and up to date overview of such approaches. After introducing basic principles of molecular dynamics simulations, as well as methods for and potential pitfalls in modeling bioconjugates, the review illustrates how these computational techniques have contributed to the understanding of bioconjugates and bioconjugation strategies in the recent past and how they may lead to a more rational design of novel bioconjugates in the future.

Solution-processable polytriazoles from spirocyclic monomers for membrane-based hydrocarbon separations

The thermal distillation of crude oil mixtures is an energy-intensive process, accounting for nearly 1% of global energy consumption. Membrane-based separations are an appealing alternative or tandem process to distillation due to intrinsic energy efficiency advantages. We developed a family of spirocyclic polytriazoles from structurally diverse monomers for membrane applications. The resulting polymers were prepared by a convenient step-growth method using copper-catalysed azide-alkyne cycloaddition, providing very fast reaction rates, high molecular weights and solubilities in common organic solvents and non-interconnected microporosity. Fractionation of whole Arabian light crude oil and atmospheric tower bottom feeds using these materials enriched the low-boiling-point components and removed trace heteroatom and metal impurities (comparable performance with the lighter feed as the commercial polyimide, Matrimid), demonstrating opportunities to reduce the energy cost of crude oil distillation with tandem membrane processes. Membrane-based molecular separation under these demanding conditions is made possible by high thermal stability and a moderate level of dynamic chain mobility, leading to transient interconnections between micropores, as revealed by the calculations of static and swollen pore structures.

Microscopic insight into the shaping of MOFs and its impact on CO2 capture performance

The traditional synthesis method produces microcrystalline powdered MOFs, which prevents direct implementation in real-world applications which demand strict control of shape, morphology and physical properties. Therefore, shaping of MOFs via the use of binders is of paramount interest for their practical use in gas adsorption/separation, catalysis, sensors, etc. However, so far, the binders have been mostly selected by trial-and-error without anticipating the adhesion between the MOF and binder components to ensure the processability of homogeneous and mechanically stable shaped MOFs and the impact of the shaping on the intrinsic properties of the MOFs has been overlooked. Herein, we deliver a first systematic multiscale computational exploration of MOF/binder composites by selecting CALF-20, a prototypical MOF for real application in the field of CO2 capture, and a series of binders that cover a rather broad spectrum of properties in terms of rigidity/flexibility, porosity, and chemical functionality. The adhesion between the two components and hence the effectiveness of the shaping as well as the impact of the overall porosity of the CALF-20/binder on the CO2/N2 selectivity, CO2 sorption capacity and kinetics was analyzed. Shaping of CALF-20 by carboxymethyl cellulose was predicted to enable a fair compromise between excellent adhesion between the two components, whilst maintaining high CO2/N2 selectivity, large CO2 uptake and CO2 transport as fast as in the CALF-20. This multiscale computational tool paves the way towards the selection of an appropriate binder to achieve an optimum shaping of a given MOF in terms of processability whilst maintaining its high level of performance.

High-Throughput Screening and Prediction of High Modulus of Resilience Polymers Using Explainable Machine Learning

The ability to store and release elastic strain energy, as well as mechanical strength, are crucial factors in both natural and man-made mechanical systems. The modulus of resilience (R) indicates a material's capacity to absorb and release elastic strain energy, with the yield strength (σy) and Young's modulus (E) as R = σy2/(2E) for linear elastic solids. To improve the R in linear elastic solids, a high σy and low E combination in materials is sought after. However, achieving this combination is a significant challenge as both properties typically increase together. To address this challenge, we propose a computational method to quickly identify polymers with a high modulus of resilience using machine learning (ML) and validate the predictions through high-fidelity molecular dynamics (MD) simulations. Our approach commences by training single-task ML models, multitask ML models, and Evidential Deep Learning models to forecast the mechanical properties of polymers based on experimentally reported values. Utilizing explainable ML models, we were able to determine the critical substructures that significantly impact the mechanical properties of polymers, such as E and σy. This information can be utilized to create and develop new polymers with improved mechanical characteristics. Our single-task and multitask ML models can predict the properties of 12 854 real polymers and 8 million hypothetical polyimides and uncover 10 new real polymers and 10 hypothetical polyimides with exceptional modulus of resilience. The improved modulus of resilience of these novel polymers was validated through MD simulations. Our method efficiently speeds up the discovery of high-performing polymers using ML predictions and MD validation and can be applied to other polymer material discovery challenges, such as polymer membranes, dielectric polymers, and more.

PolySMart: a general coarse-grained molecular dynamics polymerization scheme

The development of simulation methods to study the structure and dynamics of a macroscopically sized piece of polymer material is important as such methods can elucidate structure-property relationships. Several methods have been reported to construct initial structures for homo- and co-polymers; however, most of them are only useful for short linear polymers since one needs to pack and equilibrate the far-from-equilibrium initial structures, which is a tedious task for long or hyperbranched polymers and unfeasible for polymer networks. In this method article, we present PolySMart, i.e., an open-source python package, which can effectively produce fully equilibrated homo- and hetero-polymer melts and solutions with no limitation on the polymer topology and size, at a coarse-grained resolution and through a bottom-up approach. This python package is also capable of exploring the polymerization kinetics through its reactive scheme in realistic conditions so that it can model the multiple co-occurring polymerization reactions (with different reaction rates) as well as consecutive polymerizations under stoichiometric and non-stoichiometric conditions. Thus, the equilibrated polymer models are generated through correct polymerization kinetics. A benchmark and verification of the performance of the program for several realistic cases, i.e., for homo-polymers, co-polymers, and crosslinked networks, is given. We further discuss the capability of the program to contribute to the discovery and design of new polymer materials.

Automatic Multiscale Method of Building up a Cross-linked Polymer Reaction System: Bridging SMILES to the Multiscale Molecular Dynamics Simulation

An automatic method is introduced to generate the initial configuration and input file from SMILES for multiscale molecular dynamics (MD) simulation of cross-linked polymer reaction systems. Inputs are a modified version of SMILES of all the components and conditions of coarse-grained (CG) and all-atom (AA) simulations. The overall process comprises the following steps: (1) Modified SMILES inputs of all the components are converted to 3-dimensional coordinates of molecular structures. (2) Molecular structures are mapped to the coarse-grained scale, followed by a CG reaction simulation. (3) CG beads are backmapped to the atomic scale after the CG reaction. (4) An AA productive run is finally performed to analyze volume shrinkage, glass transition, and atomic detail of network structure. The method is applied to two common epoxy resin reactions, that is, the cross-linking process of DGEVA (diglycidyl ether of vanillyl alcohol) and DHAVA (dihydroxyaminopropane of vanillyl alcohol) and that of DGEBA (diglycidyl ether of bisphenol A) and DETA (diethylenetriamine). These components form network structures after the CG cross-linking reaction and are then backmapped to calculate properties in the atomic scale. The result demonstrates that the method can accurately predict volume shrinkage, glass transition, and all-atom structure of cross-linked polymers. The method bridges from SMILES to MD simulation trajectories in an automatic way, which shortens the time of building up cross-linked polymer reaction model and suitable for high-throughput computations.

Mapping nanocrystalline disorder within an amorphous metal-organic framework

Intentionally disordered metal-organic frameworks (MOFs) display rich functional behaviour. However, the characterisation of their atomic structures remains incredibly challenging. X-ray pair distribution function techniques have been pivotal in determining their average local structure but are largely insensitive to spatial variations in the structure. Fe-BTC (BTC = 1,3,5-benzenetricarboxylate) is a nanocomposite MOF, known for its catalytic properties, comprising crystalline nanoparticles and an amorphous matrix. Here, we use scanning electron diffraction to first map the crystalline and amorphous components to evaluate domain size and then to carry out electron pair distribution function analysis to probe the spatially separated atomic structure of the amorphous matrix. Further Bragg scattering analysis reveals systematic orientational disorder within Fe-BTC's nanocrystallites, showing over 10° of continuous lattice rotation across single particles. Finally, we identify candidate unit cells for the crystalline component. These independent structural analyses quantify disorder in Fe-BTC at the critical length scale for engineering composite MOF materials.

Monolithic Titanium Alkoxide Networks for Lithium-Ion Conductive All-Solid-State Electrolytes

Reticular chemistry provides opportunities to design solid-state electrolytes (SSEs) with modular tunability. However, SSEs based on modularly designed crystalline metal-organic frameworks (MOFs) often require liquid electrolytes for interfacial contact. Monolithic glassy MOFs can have liquid processability and uniform lithium conduction, which is promising for the reticular design of SSE without liquid electrolytes. Here, we develop a generalizable strategy for the modular design of noncrystalline SSEs based on a bottom-up synthesis of glassy MOFs. We demonstrate such a strategy by linking polyethylene glycol (PEG) struts and nanosized titanium-oxo clusters into network structures termed titanium alkoxide networks (TANs). The modular design allows the incorporation of PEG linkers with different molecular weights, which give optimal chain flexibility for high ionic conductivity, and the reticular coordinative network provides a controlled degree of cross-linking that gives adequate mechanical strength. This research shows the power of reticular design in noncrystalline molecular framework materials for SSEs.

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.

Mechanically Stable Ultrathin Layered Graphene Nanocomposites Alleviate Residual Interfacial Stresses: Implications for Nanoelectromechanical Systems

Advanced nanoelectromechanical systems made from polymer dielectrics deposited onto 2D-nanomaterials such as graphene are increasingly popular as pressure and touch sensors, resonant sensors, and capacitive micromachined ultrasound transducers (CMUTs). However, durability and accuracy of layered nanocomposites depend on the mechanical stability of the interface between polymer and graphene layers. Here we used molecular dynamics computer simulations to investigate the interface between a sheet of graphene and a layer of parylene-C thermoplastic polymer during large numbers of high-frequency (MHz) cycles of bending relevant to the operating regime. We find that important interfacial sliding occurs almost immediately in usage conditions, resulting in more than 2% expansion of the membrane, a detrimental mechanism which requires repeated calibration to maintain CMUTs accuracy. This irreversible mechanism is caused by relaxation of residual internal stresses in the nanocomposite bilayer, leading to the emergence of self-equilibrated tension in the polymer and compression in the graphene. It arises as a result of deposition-polymerization processing conditions. Our findings demonstrate the need for particular care to be exercised in overcoming initial expansion. The selection of appropriate materials chemistry including low electrostatic interactions will also be key to their successful application as durable and reliable devices.

Computational modeling to assist in the discovery of supramolecular materials

Computational modeling is increasingly used to assist in the discovery of supramolecular materials. Supramolecular materials are typically primarily built from organic components that are self-assembled through noncovalent bonding and have potential applications, including in selective binding, sorption, molecular separations, catalysis, optoelectronics, sensing, and as molecular machines. In this review, the key areas where computational prediction can assist in the discovery of supramolecular materials, including in structure prediction, property prediction, and the prediction of how to synthesize a hypothetical material are discussed, before exploring the potential impact of artificial intelligence techniques on the field. Throughout, the importance of close integration with experimental materials discovery programs will be highlighted. A series of case studies from the author's work across some different supramolecular material classes will be discussed, before finishing with a discussion of the outlook for the field.

Modeling the Effect of Defects and Disorder in Amorphous Metal-Organic Frameworks

Amorphous metal-organic frameworks (aMOFs) are a class of disordered framework materials with a defined local order given by the connectivity between inorganic nodes and organic linkers, but absent long-range order. The rational development of function for aMOFs is hindered by our limited understanding of the underlying structure-property relationships in these systems, a consequence of the absence of long-range order, which makes experimental characterization particularly challenging. Here, we use a versatile modeling approach to generate in silico structural models for an aMOF based on Fe trimers and 1,3,5-benzenetricarboxylate (BTC) linkers, Fe-BTC. We build a phase space for this material that includes nine amorphous phases with different degrees of defects and local order. These models are analyzed through a combination of structural analysis, pore analysis, and pair distribution functions. Therefore, we are able to systematically explore the effects of the variation of each of these features, both in isolation and combined, for a disordered MOF system, something that would not be possible through experiment alone. We find that the degree of local order has a greater impact on structure and properties than the degree of defects. The approach presented here is versatile and allows for the study of different structural features and MOF chemistries, enabling the derivation of design rules for the rational development of aMOFs.

Into the Unknown: How Computation Can Help Explore Uncharted Material Space

Novel functional materials are urgently needed to help combat the major global challenges facing humanity, such as climate change and resource scarcity. Yet, the traditional experimental materials discovery process is slow and the material space at our disposal is too vast to effectively explore using intuition-guided experimentation alone. Most experimental materials discovery programs necessarily focus on exploring the local space of known materials, so we are not fully exploiting the enormous potential material space, where more novel materials with unique properties may exist. Computation, facilitated by improvements in open-source software and databases, as well as computer hardware has the potential to significantly accelerate the rational development of materials, but all too often is only used to postrationalize experimental observations. Thus, the true predictive power of computation, where theory leads experimentation, is not fully utilized. Here, we discuss the challenges to successful implementation of computation-driven materials discovery workflows, and then focus on the progress of the field, with a particular emphasis on the challenges to reaching novel materials.

Temperature Effects in Flexible Adsorption Processes for Amorphous Microporous Polymers

A collection of atomistic molecular simulations is reported that illustrate the impact of adsorption temperature on species uptake and adsorbate-induced structural rearrangement for amorphous polymers of intrinsic microporosity. Temperature-sensitive structural rearrangement is evaluated by contrasting two methods: standard grand canonical Monte Carlo simulations using a rigid framework approximation and a combined Monte Carlo/molecular dynamics approach that fully incorporates framework flexibility. We report single-component gas phase adsorption isotherms for CH₄, C₂H₄, C₂H₆, C₃H₆, C₃H₈, and CO₂ across a temperature range of 250-400 K for models of an archetypal polymer of intrinsic microporosity, PIM-1. A quadratic model is presented that captures two main mechanisms of temperature-dependent adsorption-induced deformation of PIM-1 up to a relative swelling of 1.15: thermal expansion and an increased propensity to swell as a function of species uptake. Two case studies are reported that highlight the critical role of operating temperature in industrial storage and separation applications. The first study focuses on methane storage and delivery applications using a pressure-temperature swing adsorption application (PTSA). We demonstrate that larger working capacities are accompanied by increased volumetric strain between adsorption-desorption steps. The second case study considers PIM-1 as an adsorbent to separate an exemplar ternary syngas mixture at operating temperatures ranging 300-550 K. A temperature threshold of ∼400 K is identified, beyond which adsorption-induced PIM-1 swelling is negligible and the solubility selectivity-loading curve transitions to exhibiting a nearly linear relationship.

Gas Adsorption and Diffusion Behaviors in Interfacial Systems Composed of a Polymer of Intrinsic Microporosity and Amorphous Silica: A Molecular Simulation Study

We investigate the adsorption and diffusion behaviors of CO₂, CH₄, and N₂ in interfacial systems composed of a polymer of intrinsic microporosity (PIM-1) and amorphous silica using grand canonical Monte Carlo (GCMC) and molecular dynamics (MD) simulations. We build model systems of mixed matrix membranes (MMMs) with PIM-1 chains sandwiched between silica surfaces. Gas adsorption analysis using GCMC simulations shows that gas molecules are preferentially adsorbed in microcavities distributed near silica surfaces, resulting in an increase in the solubility coefficients of CO₂, CH₄, and N₂ compared to bulk PIM-1. In contrast, diffusion coefficients obtained from MD simulations and then calibrated using the dual-mode sorption model show different tendencies depending on gas species: CO₂ diffusivity decreases in MMMs compared to PIM-1, whereas CH₄ and N₂ diffusivities increase. These differences are attributed to competing effects of silica surfaces: the emergence of larger pores as a result of chain packing disruption, which enhances gas diffusion, and a quadrupole-dipole interaction between gas molecules and silica surface hydroxyl groups, which retards gas diffusion. The former has a greater impact on CH₄ and N₂ diffusivities, whereas the latter has a greater impact on CO₂ diffusivity due to the strong quadrupole-dipole interaction between CO₂ and surface hydroxyls. These findings add to our understanding of gas adsorption and diffusion behaviors in the vicinity of PIM-1/silica interfaces, which are unobtainable in experimental studies.

Development of efficient aqueous organic redox flow batteries using ion-sieving sulfonated polymer membranes

Redox flow batteries using aqueous organic-based electrolytes are promising candidates for developing cost-effective grid-scale energy storage devices. However, a significant drawback of these batteries is the cross-mixing of active species through the membrane, which causes battery performance degradation. To overcome this issue, here we report size-selective ion-exchange membranes prepared by sulfonation of a spirobifluorene-based microporous polymer and demonstrate their efficient ion sieving functions in flow batteries. The spirobifluorene unit allows control over the degree of sulfonation to optimize the transport of cations, whilst the microporous structure inhibits the crossover of organic molecules via molecular sieving. Furthermore, the enhanced membrane selectivity mitigates the crossover-induced capacity decay whilst maintaining good ionic conductivity for aqueous electrolyte solution at pH 9, where the redox-active organic molecules show long-term stability. We also prove the boosting effect of the membranes on the energy efficiency and peak power density of the aqueous redox flow battery, which shows stable operation for about 120 h (i.e., 2100 charge-discharge cycles at 100 mA cm⁻²) in a laboratory-scale cell.

Aqueous organic redox flow batteries are promising for grid-scale energy storage, although their practical application is still limited. Here, the authors report highly ion-conductive and selective polymer membranes, which boost the battery’s efficiency and stability, offering cost-effective electricity storage.

Rational design of mixed-matrix metal-organic framework membranes for molecular separations

Conventional separation technologies to separate valuable commodities are energy intensive, consuming 15% of the worldwide energy. Mixed-matrix membranes, combining processable polymers and selective adsorbents, offer the potential to deploy adsorbent distinct separation properties into processable matrix. We report the rational design and construction of a highly efficient, mixed-matrix metal-organic framework membrane based on three interlocked criteria: (i) a fluorinated metal-organic framework, AlFFIVE-1-Ni, as a molecular sieve adsorbent that selectively enhances hydrogen sulfide and carbon dioxide diffusion while excluding methane; (ii) tailoring crystal morphology into nanosheets with maximally exposed (001) facets; and (iii) in-plane alignment of (001) nanosheets in polymer matrix and attainment of [001]-oriented membrane. The membrane demonstrated exceptionally high hydrogen sulfide and carbon dioxide separation from natural gas under practical working conditions. This approach offers great potential to translate other key adsorbents into processable matrix.

Polymer Structure Predictor (PSP): A Python Toolkit for Predicting Atomic-Level Structural Models for a Range of Polymer Geometries

Three-dimensional atomic-level models of polymers are the starting points for physics-based simulation studies. A capability to generate reasonable initial structural models is highly desired for this purpose. We have developed a python toolkit, namely, polymer structure predictor (psp), to generate a hierarchy of polymer models, ranging from oligomers to infinite chains to crystals to amorphous models, using a simplified molecular-input line-entry system (SMILES) string of the polymer repeat unit as the primary input. This toolkit allows users to tune several parameters to manage the quality and scale of models and computational cost. The output structures and accompanying force field (GAFF2/OPLS-AA) parameter files can be used for downstream ab initio and molecular dynamics simulations. The psp package includes a Colab notebook where users can go through several examples, building their own models, visualizing them, and downloading them for later use. The psp toolkit, being a first of its kind, will facilitate automation in polymer property prediction and design.

Molecular Methods for Assessing the Morphology, Topology, and Performance of Polyamide Membranes

The molecular-scale morphology and topology of polyamide composite membranes determine the performance characteristics of these materials. However, molecular-scale simulations are computationally expensive and morphological and topological characterization of molecular structures are not well developed. Molecular dynamics simulation and analysis methods for the polymerization, hydration, and quantification of polyamide membrane structures were developed and compared to elucidate efficient approaches for producing and analyzing the polyamide structure. Polymerization simulations that omitted the reaction-phase solvent did not change the observed hydration, pore-size distribution, or water permeability, while improving the simulation efficiency. Pre-insertion of water into the aggregate pores (radius ≈ 4 Å) of dry domains enabled shorter hydration simulations and improved simulation scaling, without altering pore structure, properties, or performance. Medial axis and Minkowski functional methods were implemented to identify permeation pathways and quantify the polyamide morphology and topology, respectively. Better agreement between simulations and experimentally observed systems was accomplished by increasing the domain size rather than increasing the number of ensemble realizations of smaller systems. The largest domain hydrated was an order of magnitude larger by volume than the largest domain previously reported. This work identifies methods that can enable more efficient and meaningful fundamental modeling of membrane materials.

A Martini 3 coarse-grain model for the simulation of the photopolymerizable organic phase in dental composites

A coarse grain model of Bis-GMA and TEGDMA is presented and used to study aspects relevant to dental composites. It was found that the simulated polymerization shrinkage and double bond conversions are in good agreement with experimental data.

Benchmarking Machine Learning Models for Polymer Informatics: An Example of Glass Transition Temperature

In the field of polymer informatics, utilizing machine learning (ML) techniques to evaluate the glass transition temperature T_g and other properties of polymers has attracted extensive attention. This data-centric approach is much more efficient and practical than the laborious experimental measurements when encountered a daunting number of polymer structures. Various ML models are demonstrated to perform well for T_g prediction. Nevertheless, they are trained on different data sets, using different structure representations, and based on different feature engineering methods. Thus, the critical question arises on selecting a proper ML model to better handle the T_g prediction with generalization ability. To provide a fair comparison of different ML techniques and examine the key factors that affect the model performance, we carry out a systematic benchmark study by compiling 79 different ML models and training them on a large and diverse data set. The three major components in setting up an ML model are structure representations, feature representations, and ML algorithms. In terms of polymer structure representation, we consider the polymer monomer, repeat unit, and oligomer with longer chain structure. Based on that feature, representation is calculated, including Morgan fingerprinting with or without substructure frequency, RDKit descriptors, molecular embedding, molecular graph, etc. Afterward, the obtained feature input is trained using different ML algorithms, such as deep neural networks, convolutional neural networks, random forest, support vector machine, LASSO regression, and Gaussian process regression. We evaluate the performance of these ML models using a holdout test set and an extra unlabeled data set from high-throughput molecular dynamics simulation. The ML model's generalization ability on an unlabeled data set is especially focused, and the model's sensitivity to topology and the molecular weight of polymers is also taken into consideration. This benchmark study provides not only a guideline for the T_g prediction task but also a useful reference for other polymer informatics tasks.

Simu-D: A Simulator-Descriptor Suite for Polymer-Based Systems under Extreme Conditions

We present Simu-D, a software suite for the simulation and successive identification of local structures of atomistic systems, based on polymers, under extreme conditions, in the bulk, on surfaces, and at interfaces. The protocol is built around various types of Monte Carlo algorithms, which include localized, chain-connectivity-altering, identity-exchange, and cluster-based moves. The approach focuses on alleviating one of the main disadvantages of Monte Carlo algorithms, which is the general applicability under a wide range of conditions. Present applications include polymer-based nanocomposites with nanofillers in the form of cylinders and spheres of varied concentration and size, extremely confined and maximally packed assemblies in two and three dimensions, and terminally grafted macromolecules. The main simulator is accompanied by a descriptor that identifies the similarity of computer-generated configurations with respect to reference crystals in two or three dimensions. The Simu-D simulator-descriptor can be an especially useful tool in the modeling studies of the entropy- and energy-driven phase transition, adsorption, and self-organization of polymer-based systems under a variety of conditions.

Systematic Coarse-graining of Epoxy Resins with Machine Learning-Informed Energy Renormalization

A persistent challenge in predictive molecular modeling of thermoset polymers is to capture the effects of chemical composition and degree of crosslinking (DC) on dynamical and mechanical properties with high computational efficiency. We established a new coarse-graining (CG) approach that combines the energy renormalization method with Gaussian process surrogate models of the molecular dynamics simulations. This allows a machine-learning informed functional calibration of DC-dependent CG force field parameters. Taking versatile epoxy resins consisting of Bisphenol A diglycidyl ether combined with curing agent of either 4,4-Diaminodicyclohexylmethane or polyoxypropylene diamines, we demonstrated excellent agreement between all-atom and CG predictions for density, Debye-Waller factor, Young's modulus and yield stress at any DC. We further introduce a surrogate model enabled simplification of the functional forms of 14 non-bonded calibration parameters by quantifying the uncertainty of a candidate set of high-dimensional/flexible calibration functions. The framework established provides an efficient methodology for chemistry-specific, large-scale investigations of the dynamics and mechanics of epoxy resins.

How Long are Polymer Chains in Poly(3,4-ethylenedioxythiophene):Tosylate Films? An Insight from Molecular Dynamics Simulations

Poly(3,4-ethylenedioxythiophene) (PEDOT) is one of the most important conductive polymers utilized in a variety of applications in organic electronics and bioelectronics and energy storage. PEDOT chains are believed to be rather short, but detailed knowledge of their length is missing because of the challenges in its experimental determination due to insolubility of PEDOT films. Here, we report a molecular dynamics (MD) study of in situ oxidative chemical polymerization and simultaneous crystallization of molecularly doped PEDOT focusing on the determination of its chain lengths at different polymerization temperatures. We find the average chain length to be 6, 7, and 11 monomers for 298, 323 and 373 K, respectively. At the same time, the length distribution is rather broad, for example, between 2 and 16 monomer units for T = 323 K. We demonstrate that the limiting factor determining the chain length is the diffusivity of the reactants (PEDOT monomers and oligomers). We also study the polymer film formation during solvent evaporation, and we find that although crystallization starts and proceeds already during the polymerization and doping phases, it mostly occurs during the evaporation phase. Finally, we believe that our results providing the oligomer chain length and polymerization and crystallization mechanisms obtained by means of MD "computational microscopy" provide an important insight into the morphology of PEDOT that cannot be obtained by other means.

Control of Functionalized Pore Environment in Robust Ionic Ultramicroporous Polymers for Efficient Removal of Trace Propyne from Propylene

Separating trace propyne from propylene is of great importance in the petrochemical industry but difficult because of very close molecular sizes and physicochemical properties, which promotes the development of high-performance porous materials with great stability in practical adsorptive separation; however, a limited number of efficient adsorbents have been reported. Here, a class of robust functionalized ionic ultramicroporous polymers (IUPs) with different branched structures that feature high-density preferential anionic binding sites and outstanding thermal and water stability is systematically studied for the separation of propyne and propylene for the first time. The functionalized pore environment of IUPs achieves the highest selectivity of propyne and propylene (126.5) for the 1/99 (v/v) mixture among porous organic polymers, as well as excellent and recyclable dynamic separation performance. Modeling studies reveal that strong basic sites of IUPs with abundant ultramicroporosity facilitate the efficient removal of propyne from propylene. This study provides important clues for the design of robust functionalized adsorbents and thus expands the currently limited dictionary of adsorbents for the separation of important gas mixtures.

Functional Properties of Two-Component Hydrogel Systems Based on Gelatin and Polyvinyl Alcohol-Experimental Studies Supported by Computational Analysis

The presented research is focused on an investigation of the effect of the addition of polyvinyl alcohol (PVA) to a gelatin-based hydrogel on the functional properties of the resulting material. The main purpose was to experimentally determine and compare the properties of hydrogels differing from the content of PVA in the blend. Subsequently, the utility of these matrices for the production of an immobilized invertase preparation with improved operational stability was examined. We also propose a useful computational tool to predict the properties of the final material depending on the proportions of both components in order to design the feature range of the hydrogel blend desired for a strictly specified immobilization system (of enzyme/carrier type). Based on experimental research, it was found that an increase in the PVA content in gelatin hydrogels contributes to obtaining materials with a visibly higher packaging density, degree of swelling, and water absorption capacity. In the case of hydrolytic degradation and compressive strength, the opposite tendency was observed. The functionality studies of gelatin and gelatin/PVA hydrogels for enzyme immobilization indicate the very promising potential of invertase entrapped in a gelatin/PVA hydrogel matrix as a stable biocatalyst for industrial use. The molecular modeling analysis performed in this work provides qualitative information about the tendencies of the macroscopic parameters observed with the increase in the PVA and insight into the chemical nature of these dependencies.