Anion exchange membranes offer a promising alternative to the more expensive proton exchange membrane fuel cells; however, hydroxide ion conductivity in anion exchange membranes is poorly understood. In this paper, we use classical molecular dynamics simulations to study the structure and ion transport properties of four different polyethylene-based membranes prepared from ethylene-co-vinyl-acetate (EVA). We examine the microstructure of the membranes and find that polymers with a narrow cavity size distribution have tighter packing of water molecules around hydroxide ions, compared to membranes with a broad cavity distribution. We calculate the structure factor of the hydrated membranes and find a peak between 1 and 4 nm(-1), characteristic of ionic clusters in these materials. We estimate the self-diffusion coefficient of water and hydroxide ions and find that water molecules have a higher diffusion than hydroxide ions across all systems. The trends in hydroxide diffusion align well with experimental conductivity measurements. For systems with broad cavities, water facilitates hydroxide diffusion through vehicular transport, and in systems with narrow cavities, both ion hopping and vehicular transport are observed; this is quantified by calculating ion-ion and ion-solvent correlations through the Onsager transport coefficient framework.

Anion exchange membranes are used in alkaline fuel cells and offer a promising alternative to the more expensive proton exchange membrane fuel cells. However, hydroxide ion conductivity in anion exchange membranes is low, and the quest for membranes with superior ion conductivity, mechanical robustness, and chemical stability is ongoing. In this study, we use classical molecular dynamics simulations to study hydroxide ion transport and mechanical properties of eight different hydrated polyolefin-based membranes, to provide a molecular-level understanding of the structure-function relationships in these systems. We examine the microstructure of the membranes and find that polymers with narrow cavity size distribution have tighter packing of water molecules around hydroxide ions. We estimate the self-diffusion coefficient of water and hydroxide ions and find that water molecules have a higher diffusion than hydroxide ions across all systems. The trends in hydroxide diffusion align well with experimental conductivity measurements. Water facilitates hydroxide diffusion, and this is clearly observed when the hydration level is varied for the same polymer chemistry. In systems with narrow cavities and tightly bound hydroxide ions, hydroxide diffusion is the lowest, underscoring the fact that water channels facilitate hydroxide transport. Finally, we apply uniaxial deformation to calculate the mechanical properties of these systems and find that polymers with higher hydration levels show poor mechanical properties. Atomistic molecular dynamics models can accurately capture the trade-off between hydroxide transport and mechanical performance in anion exchange membranes and allow us to screen new candidates more efficiently.

Spin-labeling with electron paramagnetic resonance spectroscopy (EPR) is a facile method for interrogating macromolecular flexibility, conformational changes, accessibility, and hydration. Within we present a computationally based approach for the rational selection of reporter sites in Bacillus subtilis lipase A (BSLA) for substitution to cysteine residues with subsequent modification with a spin-label that are expected to not significantly perturb the wild-type structure, dynamics, or enzymatic function. Experimental circular dichroism spectroscopy, Michaelis-Menten kinetic parameters and EPR spectroscopy data validate the success of this approach to computationally select reporter sites for future magnetic resonance investigations of hydration and hydration changes induced by polymer conjugation, tethering, immobilization, or amino acid substitution in BSLA. Analysis of molecular dynamic simulations of the impact of substitutions on the secondary structure agree well with experimental findings. We propose that this computationally guided approach for choosing spin-labeled EPR reporter sites, which evaluates relative surface accessibility coupled with hydrogen bonding occupancy of amino acids to the catalytic pocket via atomistic simulations, should be readily transferable to other macromolecular systems of interest including selecting sites for paramagnetic relaxation enhancement NMR studies, other spin-labeling EPR studies or any method requiring a tagging method where it is desirable to not alter enzyme stability or activity.

The mu-opioid receptor (MOR) is a transmembrane protein and the primary target for pain-modulating drugs. Opioid drugs come with detrimental side-effects such as physical dependence and addiction. However, recent studies show that understanding structural properties and dynamics of MOR may aid in the design of opioid drugs with reduced side-effects. Molecular dynamics simulations allow researchers to study changes in protein conformation at an atomistic level. However, modeling systems including MOR embedded in a lipid bilayer can be computationally expensive. This study evaluates a modeling approach that uses harmonic restraints on the transmembrane regions of MOR to model the rigidity of the lipid bilayer without explicitly simulating lipid molecules, reducing the number of atoms in the simulation. The proposed model allows MOR to be simulated 49% faster than a simulation explicitly including the lipid bilayer. To assess the accuracy of the proposed model, simulations were performed of MOR in a lipid bilayer, the free MOR in water and MOR in water with harmonic restraints applied to all transmembrane residues using NAMD 3.0 alpha and the CHARMM36 force field. Dynamic properties of MOR were shown to be different in each system, with the free MOR having a higher root mean square deviation (RMSD) than MOR with an explicitly modeled lipid bilayer. The systems with harmonic restraint constants of 0.001 kcal/mol/Å2 applied to the transmembrane residues had RMSD values comparable to those in an explicitly modeled lipid bilayer. This study demonstrates that using restraints on the transmembrane residues of MOR is a feasible way of modeling the ligand-free receptor with reduced computational costs. This model could allow the dynamics of MOR in a lipid bilayer environment to be studied more efficiently. KEYWORDS: Molecular Dynamics; Atomistic Simulations; Computational Modeling; Mu-Opioid Receptor; G-Protein Coupled Receptor; Lipid Bilayer, Opioid, Transmembrane Protein

Amorphous porous organic polymers show promise for energy-efficient adsorptive separations, but it is difficult to understand or improve their performance through intentional structural modification. Herein, we report the synthesis of porous aromatic frameworks (PAFs) with pore structures tailored by the incorporation of a monofunctionalized end-capping monomer that disrupts framework topology. Combining experimental characterization with molecular simulations, we show that this defect engineering strategy yields less densely crosslinked networks, which leads to pore collapse and the presence of unique adsorption sites. These defect-engineered PAFs exhibit enhanced removal of 1,4-dioxane, an important environmental pollutant, from water. Increasing the concentration of end-capping monomers produces PAFs with narrower pore size distributions and improved 1,4-dioxane uptake. These results illustrate that defect engineering can effectively modulate polymer connectivity and porosity for applications in selective adsorptive separations. This technique avoids post-synthetic treatments and presents another approach to tailor amorphous polymeric adsorbents.

A major obstacle for machine learning (ML) in chemical science is the lack of physically informed feature representations that provide both accurate prediction and easy interpretability of the ML model. In this work, we describe adsorption systems using novel two-dimensional energy histogram (2D-EH) features, which are obtained from the probe-adsorbent energies and energy gradients at grid points located throughout the adsorbent. The 2D-EH features encode both energetic and structural information of the material and lead to highly accurate ML models (coefficient of determination R2 ∼ 0.94-0.99) for predicting single-component adsorption capacity in metal-organic frameworks (MOFs). We consider the adsorption of spherical molecules (Kr and Xe), linear alkanes with a wide range of aspect ratios (ethane, propane, n-butane, and n-hexane), and a branched alkane (2,2-dimethylbutane) over a wide range of temperatures and pressures. The interpretable 2D-EH features enable the ML model to learn the basic physics of adsorption in pores from the training data. We show that these MOF-data-trained ML models are transferrable to different families of amorphous nanoporous materials. We also identify several adsorption systems where capillary condensation occurs, and ML predictions are more challenging. Nevertheless, our 2D-EH features still outperform structural features including those derived from persistent homology. The novel 2D-EH features may help accelerate the discovery and design of advanced nanoporous materials using ML for gas storage and separation in the future.

Polymers of intrinsic microporosity (PIMs) are a family of materials with potential to be effective and scalable solutions for challenging adsorbent and membrane applications. The broad range of repeat unit chemistry, microporous structural features, and polymer processing makes exploration of the expansive PIM design space inefficient via chemical and materials intuition alone. Computational techniques such as molecular simulations and machine learning can provide a leap in capabilities to address this polymer design challenge and will be central to the future development of PIMs. We highlight recent microporous material studies that arrived at key results by employing computational techniques and provide our perspective on the prospects for in silico design and development of PIMs.

This chapter provides the essential background to a novice modeler on the choice of simulation techniques used to model Deep Eutectic Solvents (DESs). It describes methods used to obtain important physical, thermodynamic, transport, and structural properties of bulk DES systems including an evaluation of the strengths and drawbacks of the current simulation models. The chapter discusses future directions for simulating DES-based systems. One of the major thrust areas of ab initio investigations on DESs has been to provide a physical explanation for the observed low melting point in these systems and its effects on their physicochemical properties. Molecular simulations have played a crucial role in conjunction with experimental investigations in elucidating the structure–property relationships of DESs. Classical molecular dynamics simulations that obey Newton's laws of motion use force fields to calculate the potential energy of a system as a function of their atomic coordinates.

The efficacy of hydrogel materials used in biomedical applications is dependent on polymer network topology and the structure of water-laden pore space. Hydrogel microstructure can be tuned by adjusting synthesis parameters such as macromer molar mass and concentration. Moreover, hydrogels beyond dilute conditions are needed to produce mechanically robust and dense networks for tissue engineering and/or drug delivery systems. Thus, this study utilizes a combined experimental and molecular simulation approach to characterize structural features for 4.8 and 10 kDa poly (ethylene glycol) diacrylate (PEGDA) hydrogels formed from a range of semi-dilute solution concentrations. The connection between chain-chain interactions in polymer solutions, hydrogel structure, and equilibrium swelling behavior is presented. Bulk rheology analysis revealed an entanglement concentration for PEGDA pre-gel solutions around 28 wt% for both macromers studied. A similar transition in swelling behavior was revealed around the same concentration where hydrogel capacity to retain water was drastically reduced. To understand this transition, the hydrogel structure was characterized using the swollen polymer network hypothesis and compared to pore size distributions from molecular dynamics simulations. We find in both approaches a structural transition concentration at the hydrogel swelling inflection point that is comparable to the entanglement concentration. Calculated mesh sizes from theory are compared with computationally determined average maximum pore diameters; mesh sizes from theory yielded greater feature sizes across all concentrations considered. Molecular simulations are further used to assess pore dynamics, which are shown to vary in distribution shape and number of modes compared to the time-averaged hydrogel pore features. Altogether, this work provides insights into hydrogel network features and their dynamic behavior at physiological conditions (37 °C) as a basis for hydrogel design beyond dilute conditions for biomedical applications.

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.

Pysimm is a framework for molecular simulations of polymers and polymer-based nanostructures, which enables their direct chemical synthesis and preparation. Pysimm facilitates the understanding of novel, amorphous, processable materials for a broad range of applications, including heterogeneous catalysts, adsorbents and gas storage materials, as well as protein-polymer conjugates. This tutorial provides a detailed guide on the construction of atomistic and united-atom models of polymers using Pysimm: an open-source Python Application Programming Interface for atomistic molecular simulations. The API complements and simplifies the work of widely known molecular simulation software, such as LAMMPS, CASSANDRA, NAMD and Amber. Readers should be familiar with the basic concepts of atomistic molecular simulations, as well as the basic knowledge of Python programming language, before attempting to follow this tutorial. This work is separated into 3 main sections. First, the process of building an atomic-level model of a polymer chain from its repetitive units is described. The second section shows how to work with existing forcefields, and how Pysimm can automatically read, recognize, and assign appropriate Force Field parameters to a molecule. The final section discusses how to use Pysimm to construct polymer chains with pre-specified tacticity. The section is also available in the form of an interactive Jupyter notebook tutorial outlining simple guidelines to construct polymer models.

Entangled with inherent sensitivity to its environment; proteins, nature's workhorse performs extraordinary tasks vital for the upkeep of the biological world. Conjugating proteins with polymers has been used as a strategy to exploit these functional capabilities beyond their natural limits. However, the molecular level effects of using initiator compounds are not well understood and left unexploited. In this work, we demonstrate that initiator cluster formation reduces the charge-charge repulsions between Lys side chains, on a model protein, and brings them closer to each other through increased non-polar interactions. Crucially, it is found that initiator clusters imposed more conformational stability to a previously identified highly dynamic unstructured C-terminal region of the model protein, without altering the protein's global structure-dynamic relationship. The possibility of computationally identified initiator cluster formation has enormous potential to allow experimental techniques, such as X-ray crystallographic, to resolve highly dynamic, unstructured regions of a protein, i.e. intrinsically disordered regions, without altering the protein's global structure-dynamic relationship and thus its stability and activity. This can prove to be a game changer of the field.

This update of the pysimm application programming interface, pysimm 1.1, provides both infrastructural as well as functional updates. Moreover, improvements to the random walk application that allow it to construct polymers with controlled tacticity are highlighted. Additions to the forcefield module include an update to enable working with the family of CHARMM forcefields and automated typing with the CHARMM generalized forcefield (CGenFF). Finally, new detailed examples demonstrating new features are also provided.

Even the most advanced protein-polymer conjugate therapeutics do not eliminate antibody-protein and receptor-protein recognition. Next-generation bioconjugate drugs will need to replace stochastic selection with rational design to select desirable levels of protein-protein interaction while retaining function. The "Holy Grail" for rational design would be to generate functional enzymes that are fully catalytic with small molecule substrates while eliminating interaction between the protein surface and larger molecules. Using chymotrypsin, an important enzyme that is used to treat pancreatic insufficiency, we have designed a series of molecular chimeras with varied grafting densities and shapes. Guided by molecular dynamic simulations and next-generation molecular chimera characterization with asymmetric flow field-flow fractionation chromatography, we grew linear, branched, and comb-shaped architectures from the surface of the protein by atom-transfer radical polymerization. Comb-shaped polymers, grafted from the surface of chymotrypsin, completely prevented enzyme inhibition with protein inhibitors without sacrificing the ability of the enzyme to catalyze the hydrolysis of a peptide substrate. Asymmetric flow field-flow fractionation coupled with multiangle laser light scattering including dynamic light scattering showed that nanoarmor designed with comb-shaped polymers was particularly compact and spherical. The polymer structure significantly increased protein stability and reduced protein-protein interactions. Atomistic molecular dynamic simulations predicted that a dense nanoarmor with long-armed comb-shaped polymer would act as an almost perfect molecular sieve to filter large ligands from substrates. Surprisingly, a conjugate that was composed of 99% polymer was needed before the elimination of protein-protein interactions.

High-throughput calculations based on molecular simulations to predict the adsorption of molecules inside metal-organic frameworks (MOFs) have become a useful complement to experimental efforts to identify promising adsorbents for chemical separations and storage. For computational convenience, all existing efforts of this kind have relied on simulations in which the MOF is approximated as rigid. In this paper, we use extensive adsorption-relaxation simulations that fully include MOF flexibility effects to explore the validity of the rigid framework approximation. We also examine the accuracy of several approximate methods to incorporate framework flexibility that are more computationally efficient than adsorption-relaxation calculations. We first benchmark various models of MOF flexibility for four MOFs with well-established CO2 experimental consensus isotherms. We then consider a range of adsorption properties, including Henry's constants, nondilute loadings, and adsorption selectivity, for seven adsorbates in 15 MOFs randomly selected from the CoRE MOF database. Our results indicate that in many MOFs adsorption-relaxation simulations are necessary to make quantitative predictions of adsorption, particularly for adsorption at dilute concentrations, although more standard calculations based on rigid structures can provide useful information. Finally, we investigate whether a correlation exists between the elastic properties of empty MOFs and the importance of including framework flexibility in making accurate predictions of molecular adsorption. Our results did not identify a simple correlation of this type.

A sulfone modified variant (soPIM-1) of the first polymer of intrinsic microporosity has been studied through molecular simulations to analyze its applicability for adsorption-based separations of diverse nonpolar, quadrupolar, and dipolar adsorbate species. Single component gas phase adsorption isotherms of each adsorbate are provided. The adsorptive properties of soPIM-1 are reported from the application of two methods: (a) traditional grand canonical Monte Carlo (GCMC) simulations and (b) a combination of Monte Carlo and molecular dynamics (GCMC/MD) techniques, which accounts for sorption-induced polymer dynamics. The calculated isosteric heats of adsorption are compared to those from the parent PIM-1 structure and reveals increased CO2 affinity with relatively no change in hydrocarbon affinity. Moreover, to quantify soPIM-1's resistance to dilation the evolution of the microporous structure across the adsorption isotherm loading pressures has been evaluated. Relatedly, nonequilibrium MD simulations have been utilized to determine that soPIM-1 is approximately twice as stiff as PIM-1.

Macromolecules such as proteins conjugated to polyethylene glycol (PEG) have been employed in therapeutic drug applications, and recent research has emphasized the potential of varying polymer architectures and conjugation strategies to achieve improved efficacy. In this study, we performed atomistic molecular dynamics simulations of bovine serum albumin (BSA) conjugated to 5 kDa PEG polymers in an array of schemes, including varied numbers of attached chains, grafting density, and nonlinear architectures. Nonlinear architectures included U-shaped PEG, Y-shaped PEG, and poly(oligoethylene glycol methacrylate) (POEGMA). Buried surface area calculations and polymer volume map analyses revealed that volume exclusion behaviors of the high grafting density conjugate promoted additional protein-polymer interactions when compared to simply increasing numbers of conjugated chains uniformly across the protein surface. Investigation of nonlinear polymer architectures showed that stable polymer-lysine loop-like conformations seen in previous conjugate designs were more variable in prevalence, especially in POEGMA, which contained short oligomer PEG chains. The findings of this comprehensive study of alternate PEGylation schemes of BSA provide critical insight into molecular patterns of interaction within bioconjugates and highlight their importance in the future of controlled modification of conjugate system parameters.

With the growing need for chemical separation and chemical storage solutions, polymeric adsorbents have emerged as a promising class of candidate materials because of their potentially tunable sorption properties, membrane structure and relatively cost consciousness. Moreover, the developing field of polymeric membrane materials has shown particular success at integrating both experimental and computational studies. However, these material systems are known to suffer from varying degrees of induced membrane structural rearrangement upon adsorbate uptake, and thus many polymeric membrane performance metrics are often considered to degrade with an increasing number of 'guest' species. In this mini-review, we highlight methodology tradeoffs and provide insights into atomistic molecular simulations used to study adsorption with flexible frameworks, which have the potential to predict separation, storage or catalytic capabilitiesa priorito experimental efforts. Specifically, molecular simulation methods that have been applied to provide predictions of polymeric membrane properties that have included consideration for sorbate-induced polymer chain rearrangement, swelling and/or plasticization are reviewed. The examples and methodologies described provide demonstrations of the applicability of simulations as an approach to understand adsorption-based phenomena at an atomistic/molecular level, and as a tool to carry out screening studies aimed at efficiently providing analysis for a diversity of polymeric adsorbent-adsorbate systems. (c) 2020 Society of Industrial Chemistry

Adsorptive hydrogen storage is a desirable technology for fuel cell vehicles, and efficiently identifying the optimal storage temperature requires modeling hydrogen loading as a continuous function of pressure and temperature. Using data obtained from high-throughput Monte Carlo simulations for zeolites, metal-organic frameworks, and hyper-cross-linked polymers, we develop a meta-learning model that jointly predicts the adsorption loading for multiple materials over wide ranges of pressure and temperature. Meta-learning gives higher accuracy and improved generalization compared to fitting a model separately to each material and allows us to identify the optimal hydrogen storage temperature with the highest working capacity for a given pressure difference. Materials with high optimal temperatures are found in close proximity in the fingerprint space and exhibit high isosteric heats of adsorption. Our method and results provide new guidelines toward the design of hydrogen storage materials and a new route to incorporate machine learning into high-throughput materials discovery.

The enormous number of combinations of adsorbing molecules and porous materials that exist is known as adsorption space. The adsorption space for microporous polymers has not yet been systematically explored, especially when compared with efforts for crystalline adsorbents. We report molecular simulation data for the adsorptive and structural properties of polymers of intrinsic microporosity with a diverse set of adsorbate species with 345 distinct adsorption isotherms and over 240,000 fresh and swollen structures. These structures and isotherms were obtained using a sorption-relaxation technique that accounts for the critical role of flexibility of the polymeric adsorbents. This enables us to introduce a set of correlations that can estimate adsorbent swelling and fractional free volume dilation as a function of adsorbate uptake based on readily characterized properties. The separation selectivity of the 276 distinct binary molecular pairs in our data is reported and high-performing adsorbent systems are identified.