Temperature-Dependent Density and Viscosity Prediction for Hydrocarbons: Machine Learning and Molecular Dynamics Simulations

Machine learning-based predictive models allow rapid and reliable prediction of material properties and facilitate innovative materials design. Base oils used in the formulation of lubricant products are complex hydrocarbons of varying sizes and structure. This study developed Gaussian process regression-based models to accurately predict the temperature-dependent density and dynamic viscosity of 305 complex hydrocarbons. In our approach, strongly correlated/collinear predictors were trimmed, important predictors were selected by least absolute shrinkage and selection operator (LASSO) regularization and prior domain knowledge, hyperparameters were systematically optimized by Bayesian optimization, and the models were interpreted. The approach provided versatile and quantitative structure-property relationship (QSPR) models with relatively simple predictors for determining the dynamic viscosity and density of complex hydrocarbons at any temperature. In addition, we developed molecular dynamics simulation-based descriptors and evaluated the feasibility and versatility of dynamic descriptors from simulations for predicting the material properties. It was found that the models developed using a comparably smaller pool of dynamic descriptors performed similarly in predicting density and viscosity to models based on many more static descriptors. The best models were shown to predict density and dynamic viscosity with coefficient of determination (R2) values of 99.6% and 97.7%, respectively, for all data sets, including a test data set of 45 molecules. Finally, partial dependency plots (PDPs), individual conditional expectation (ICE) plots, local interpretable model-agnostic explanation (LIME) values, and trimmed model R2 values were used to identify the most important static and dynamic predictors of the density and viscosity.

Machine Learning Integrating Protein Structure, Sequence, and Dynamics to Predict the Enzyme Activity of Bovine Enterokinase Variants

Despite recent advances in computational protein science, the dynamic behavior of proteins, which directly governs their biological activity, cannot be gleaned from sequence information alone. To overcome this challenge, we propose a framework that integrates the peptide sequence, protein structure, and protein dynamics descriptors into machine learning algorithms to enhance their predictive capabilities and achieve improved prediction of the protein variant function. The resulting machine learning pipeline integrates traditional sequence and structure information with molecular dynamics simulation data to predict the effects of multiple point mutations on the fold improvement of the activity of bovine enterokinase variants. This study highlights how the combination of structural and dynamic data can provide predictive insights into protein functionality and address protein engineering challenges in industrial contexts.

Developing a machine learning model for accurate nucleoside hydrogels prediction based on descriptors

Supramolecular hydrogels derived from nucleosides have been gaining significant attention in the biomedical field due to their unique properties and excellent biocompatibility. However, a major challenge in this field is that there is no model for predicting whether nucleoside derivative will form a hydrogel. Here, we successfully develop a machine learning model to predict the hydrogel-forming ability of nucleoside derivatives. The optimal model with a 71% (95% Confidence Interval, 0.69-0.73) accuracy is established based on a dataset of 71 reported nucleoside derivatives. 24 molecules are selected via the optimal model external application and the hydrogel-forming ability is experimentally verified. Among these, two rarely reported cation-independent nucleoside hydrogels are found. Based on their self-assemble mechanisms, the cation-independent hydrogel is found to have potential applications in rapid visual detection of Ag+ and cysteine. Here, we show the machine learning model may provide a tool to predict nucleoside derivatives with hydrogel-forming ability.

Degradable and Multifunctional PEG-Based Hydrogels Formed by iEDDA Click Chemistry with Stable Click-Induced Supramolecular Interactions

The inverse electron demand Diels-Alder (iEDDA) reactions are highly efficient click chemistry increasingly utilized in bioconjugation, live cell labeling, and the synthesis and modification of biomaterials. iEDDA click reactions have also been used to cross-link tetrazine (Tz) and norbornene (NB) modified macromers [e.g., multiarm poly(ethylene glycol) or PEG]. In these hydrogels, Tz-NB adducts exhibit stable supramolecular interactions with a high hydrolytic stability. Toward engineering a new class of PEG-based click hydrogels with highly adaptable properties, we previously reported a new group of NB-derivatized PEG macromers via reacting hydroxyl-terminated PEG with carbic anhydride (CA). In this work, we show that hydrogels cross-linked by PEGNBCA or its derivatives exhibited fast and tunable hydrolytic degradation. Here, we show that PEGNBCA (either mono- or octafunctional) and its dopamine or tyramine conjugated derivatives (i.e., PEGNB-D and PEGNB-T) readily cross-link with 4-arm PEG-Tz to form a novel class of multifunctional iEDDA click hydrogels. Through modularly adjusting the macromers with unstable and stable iEDDA click-induced supramolecular interactions (iEDDA-CSI), we achieved highly tunable degradation, with full degradation in less than 2 weeks to over two months. We also show that secondary enzymatic reactions could dynamically stiffen these hydrogels. These hydrogels could also be spatiotemporally photopatterned through visible light-initiated photochemistry. Finally, the iEDDA-CSI hydrogels post ester hydrolysis displayed shear-thinning and self-healing properties, enabling injectable delivery.

Supramolecular gels - a panorama of low-molecular-weight gelators from ancient origins to next-generation technologies

Supramolecular gels, self-assembled from low-molecular-weight gelators (LMWGs), have a long history and a bright future. This review provides an overview of these materials, from their use in lubrication and personal care in the ancient world, through to next-generation technologies. In academic terms, colloid scientists in the 19th and early 20th centuries first understood such gels as being physically assembled as a result of weak interactions, combining a solid-like network having a degree of crystalline order with a highly mobile liquid-like phase. During the 20th century, industrial scientists began using these materials in new applications in the polymer, oil and food industries. The advent of supramolecular chemistry in the late 20th century, with its focus on non-covalent interactions and controlled self-assembly, saw the horizons for these materials shifted significantly beyond their historic rheological applications, expanding their potential. The ability to tune the LMWG chemical structure, manipulate hierarchical assembly, develop multi-component systems, and introduce new types of responsive and interactive behaviour, has been transformative. Furthermore, the dynamics of these materials are increasingly understood, creating metastable gels and transiently-fueled systems. New approaches to shaping and patterning gels are providing a unique opportunity for more sophisticated uses. These supramolecular advances are increasingly underpinning and informing next-generation applications - from drug delivery and regenerative medicine to environmental remediation and sustainable energy. In summary, this article presents a panorama over the field of supramolecular gels, emphasising how both academic and industrial scientists are building on the past, and engaging new fundamental insights and innovative concepts to open up exciting horizons for their future use.

Tools to enable the study and translation of supramolecular amphiphiles

This tutorial review focuses on providing a summary of the key techniques used for the characterisation of supramolecular amphiphiles and their self-assembled aggregates; from the understanding of low-level molecular interactions, to materials analysis, use of data to support computer-aided molecular design and finally, the translation of this class of compounds for real world application, specifically within the clinical setting. We highlight the common methodologies used for the study of traditional amphiphiles and build to provide specific examples that enable the study of specialist supramolecular systems. This includes the use of nuclear magnetic resonance spectroscopy, mass spectrometry, X-ray scattering techniques (small- and wide-angle X-ray scattering and single crystal X-ray diffraction), critical aggregation (or micelle) concentration determination methodologies, machine learning, and various microscopy techniques. Furthermore, this review provides guidance for working with supramolecular amphiphiles in in vitro and in vivo settings, as well as the use of accessible software programs, to facilitate screening and selection of druggable molecules. Each section provides: a methodology overview - information that may be derived from the use of the methodology described; a case study - examples for the application of these methodologies; and a summary section - providing methodology specific benefits, limitations and future applications.

Macrocycle Self-Assembly Hydrogel for High-Efficient Oil-Water Separation

Supramolecular hydrogels involved macrocycles have been explored widely in recent years, but it remains challenging to develop hydrogel based on solitary macrocycle with super gelation capability. Here, the construction of lantern[33 ]arene-based hydrogel with low critical gelation concentration (0.05 wt%), which can be used for efficient oil-water separation, is reported. The lantern[33 ]arenes self-assemble into hydrogen-bonded organic nanoribbons, which intertwine into entangled fibers to form hydrogel. This hydrogel which exhibits reversible pH-responsiveness characteristics can be coated on stainless-steel mesh by in situ sol-gel transformation. The resultant mesh exhibits excellent oil-water separation efficiency (>99%) and flux (>6 × 104 L m-2 h-1 ). This lantern[33 ]arene-based hydrogel not only sheds additional light on the gelation mechanisms for supramolecular hydrogels, but also extends the application of macrocycle-based hydrogels as functional interfacial materials.

Charge-induced low-temperature gelation of mixed proteins and the effect of pH on the gelation: A spectroscopic, rheological and coarse-grained molecular dynamics study

We report the gelation of mixed proteins consisting of oppositely charged lysozyme and serum albumins at various pH. The results from rheological tests showed that at a pH of 7, the gelation temperature (Tgel) of the oppositely charged proteins was lower than the melting temperature (Tm) of the individual protein. To ascertain the conformational state of the proteins at the observed Tgel, the attenuated total-reflectance Fourier-transform infrared (ATR-FTIR) spectra of the proteins were acquired. The recorded spectra showed that the proteins were predominantly alpha helical, suggesting that the observed gelation was electrostatically triggered. However, as the solution pH was changed to acid or alkaline regime, all the proteins became similarly charged and showed Tgel < Tm which was attributed to pH-induced denaturation. Surprisingly, however, the serum albumins were remarkably stable at the alkaline pH of 9 and 10 but very labile at the acidic pH. In contrast, the LYZ was more stable at the acidic than alkaline pH. To understand the role of the opposite charges in the gelation, coarse-grained molecular dynamics (CGMD) simulations revealed an increase in the aggregation of the oppositely charged proteins compared with the pure or similarly charged protein mixture.

Accelerating the prediction and discovery of peptide hydrogels with human-in-the-loop

The amino acid sequences of peptides determine their self-assembling properties. Accurate prediction of peptidic hydrogel formation, however, remains a challenging task. This work describes an interactive approach involving the mutual information exchange between experiment and machine learning for robust prediction and design of (tetra)peptide hydrogels. We chemically synthesize more than 160 natural tetrapeptides and evaluate their hydrogel-forming ability, and then employ machine learning-experiment iterative loops to improve the accuracy of the gelation prediction. We construct a score function coupling the aggregation propensity, hydrophobicity, and gelation corrector Cg, and generate an 8,000-sequence library, within which the success rate of predicting hydrogel formation reaches 87.1%. Notably, the de novo-designed peptide hydrogel selected from this work boosts the immune response of the receptor binding domain of SARS-CoV-2 in the mice model. Our approach taps into the potential of machine learning for predicting peptide hydrogelator and significantly expands the scope of natural peptide hydrogels.

Short Peptide Self-Assembly in the Martini Coarse-Grain Force Field Family

ConspectusPivotal to the success of any computational experiment is the ability to make reliable predictions about the system under study and the time required to yield these results. Biomolecular interactions is one area of research that sits in every camp of resolution vs the time required, from the quantum mechanical level to in vivo studies. At an approximate midpoint, there is coarse-grained molecular dynamics, for which the Martini force fields have become the most widely used, fast enough to simulate the entire membrane of a mitochondrion though lacking atom-specific precision. While many force fields have been parametrized to account for a specific system under study, the Martini force field has aimed at casting a wider net with more generalized bead types that have demonstrated suitability for broad use and reuse in applications from protein-graphene oxide coassembly to polysaccharides interactions.In this Account, the progressive (Martini versions 1 through 3) and peripheral (Sour Martini, constant pH, Martini Straight, Dry Martini, etc.) developmental trajectory of the Martini force field will be analyzed in terms of self-assembling systems with a focus on short (two to three amino acids) peptide self-assembly in aqueous environments. In particular, this will focus on the effects of the Martini solvent model and compare how changes in bead definitions and mapping have effects on different systems. Considerable effort in the development of Martini has been expended to reduce the "stickiness" of amino acids to better simulate proteins in bilayers. We have included in this Account a short study of dipeptide self-assembly in water, using all mainstream Martini force fields, to examine their ability to reproduce this behavior. The three most recently released versions of Martini and variations in their solvents are used to simulate in triplicate all 400 dipeptides of the 20 gene-encoded amino acids. The ability of the force fields to model the self-assembly of the dipeptides in aqueoues environments is determined by the measurement of the aggregation propensity, and additional descriptors are used to gain further insight into the dipeptide aggregates.

Nucleoside-Based Supramolecular Hydrogels: From Synthesis and Structural Properties to Biomedical and Tissue Engineering Applications

Supramolecular hydrogels are of great interest in tissue scaffolding, diagnostics, and drug delivery due to their biocompatibility and stimuli-responsive properties. In particular, nucleosides are promising candidates as building blocks due to their manifold noncovalent interactions and ease of chemical modification. Significant progress in the field has been made over recent years to allow the use of nucleoside-based supramolecular hydrogels in the biomedical field, namely drug delivery and 3D bioprinting. For example, their long-term stability, printability, functionality, and bioactivity have been greatly improved by employing more than one gelator, incorporating different cations, including silver for antibacterial activity, or using additives such as boric acid or even biomolecules. This now permits their use as bioinks for 3D printing to produce cell-laden scaffolds with specified geometries and pore sizes as well as a homogeneous distribution of living cells and bioactive molecules. We have summarized the latest advances in nucleoside-based supramolecular hydrogels. Additionally, we discuss their synthesis, structural properties, and potential applications in tissue engineering and provide an outlook and future perspective on ongoing developments in the field.

Does Supramolecular Gelation Require an External Trigger?

The supramolecular gelation of small molecules is typically preceded by an external stimulus to trigger the self-assembly. The need for this trigger stems from the metastable nature of most supramolecular gels and can limit their applicability. Herein, we present a small urea-based molecule that spontaneously forms a stable hydrogel by simple mixing without the addition of an external trigger. Single particle tracking experiments and observations made from scanning electron microscopy indicated that triggerless gelation occurred in a similar fashion as the archetypical heat-triggered gelation. These results could stimulate the search for other supramolecular hydrogels that can be obtained by simple mixing. Furthermore, the mechanism of the heat-triggered supramolecular gelation was elucidated by a combination of molecular dynamics simulations and quantitative NMR experiments. Surprisingly, hydrogelation seemingly occurs via a stepwise self-assembly in which spherical nanoparticles mature into an entangled fibrillary network.

Methionine-Derived Organogels as Lubricant Additives Enhance the Continuity of the Oil Film through Dynamic Self-Healing Assembly

(S)-2-((1-(Hexadecylamino)-4-(methylthio)-1-oxobutan-2-yl)carbamoyl)benzoic acid (HMTA) was efficiently synthesized and successfully applied as an additive to several types of blank lubricant oils. Initially, HMTA self-assembles to fibrous structures and traps blank lubricant oils to form gel lubricants. The prepared gel lubricants show thermo-reversible properties and enhanced lubricating performance by 3∼5-fold. X-ray photoelectron spectrometry of the metal surface and the quartz crystal microbalance illustrated that there are no obvious interactions between HMTA and the metal surface. The results of Fourier transform infrared spectroscopy and X-ray diffraction further confirm that inter/intro-molecular H-bonding interactions are the main driving force for the self-healing of HMTA. Finally, molecular dynamics (MD) simulations show that the number of noncovalent H-bonding interactions fluctuates with time, and this highly dynamic H-bonding network could regulate the self-assembly process and result in the self-healing property of the HMTA organogel, which is consistent with the results of the step-strain tests. Especially, the Hirshfeld independent gradient model method at the quantum level demonstrated that C8/C9 aromatics of 500SN have strong π-π stacking interactions with the aromatic heads of HMTA and van der Waals interactions with the hydrophobic tails of HMTA, which disrupt the self-assembly behavior of the 500SN model. Therefore, the calculation studies offer a rational explanation for the superior lubricant property of the PAO10 gel as compared to that for 500SN.

Personal Perspective on Understanding Low Molecular Weight Gels

Low molecular weight gels are formed by the self-assembly of small molecules into anisotropic structures that form a network capable of immobilizing the solvent. Such gels are common, with a huge number of different examples existing, and they have many applications. However, there are still significant gaps in our understanding of these systems and challenges that need to be addressed if we are to be able to fully design such systems. Here, a number of these challenges are discussed.

Effect of Solvent on the Mechanical and Structural Properties of N-Alkyldiamide Organogels

Here, we study organogels prepared thanks to a new organogelator, the N-oleyldiamide molecule, which shows a remarkable propensity to gelify a large scope of solvents, from aprotic to high protic solvents. The solvent plays a key role in the formation and stability of supramolecular self-assemblies. However, the understanding and the control of its effects can be complex as many parameters are a priori involved. This study aims to understand the effect of solvent on the structures of organogels and on their final mechanical properties. Five solvent classes have been selected ranking from low protic to high protic, according to the Hansen H-bond parameter δh. The solvent proticity appears to be one of the main parameters that affect the organogel internal structure and therefore the final rheological properties. For a given organogelator fraction, the terminal elastic modulus measured by oscillatory rheology is observed to increase significantly with the Hansen H-bond solvent parameter δh. Materials of different mechanical properties are then shown to display various structures, which are investigated thanks to cryo-SEM. Besides, wide-angle X-ray scattering (WAXS) has been used to probe the gelator organization at the molecular scale with regard to the solvent nature, to understand the supramolecular self-assembly of this promising molecule.

Understanding gel-to-crystal transitions in supramolecular gels

Most supramolecular gels are stable or assumed to be stable over time, and aging effects are often not studied. However, some gels do show clear changes on aging, and a small number of systems exhibit gel-to-crystal transitions. In these cases, crystals form over time, typically at the expense of the network underpinning the gel; this leads to the gel falling apart. These systems are rare, and little is known about how these gel-to-crystal transitions occur. Here, we use a range of techniques to understand in detail a gel-to-crystal transition for a specific functionalised dipeptide based gelator. We show that the gel-to-crystal transition depends on the final pH of the medium which we control by varying the amount of glucon-δ-lactone (GdL) added. In the gel phase, at low concentrations of GdL, and at early time points with high concentrations of GdL, we are able to show the nanometre scale dimensions of the self-assembled fibre using SAXS; however there is no evidence of molecular ordering of the gel fibres in the WAXS. At low concentrations of GdL, these self-assembled fibres stiffen with time but do not crystallise over the timescale of the SAXS experiment. At high concentrations of GdL, the fibres are already stiffened, and then, as the pH drops further, give way to the presence of crystals which appear to grow preferentially along the direction of the fibre axis. We definitively show therefore that the gel and crystal phase are not the same. Our work shows that many assumptions in the literature are incorrect. Finally, we also show that the sample holder geometry is an important parameter for these experiments, with the rate of crystallisation depending on the holder in which the experiment is carried out.

Computational Tools to Rationalize and Predict the Self-Assembly Behavior of Supramolecular Gels

Supramolecular gels form a class of soft materials that has been heavily explored by the chemical community in the past 20 years. While a multitude of experimental techniques has demonstrated its usefulness when characterizing these materials, the potential value of computational techniques has received much less attention. This review aims to provide a complete overview of studies that employ computational tools to obtain a better fundamental understanding of the self-assembly behavior of supramolecular gels or to accelerate their development by means of prediction. As such, we hope to stimulate researchers to consider using computational tools when investigating these intriguing materials. In the concluding remarks, we address future challenges faced by the field and formulate our vision on how computational methods could help overcoming them.

Novel Descriptors Derived from the Aggregation Propensity of Di- and Tripeptides Can Predict the Critical Aggregation Concentration of Longer Peptides

Self-assembling amphiphilic peptides have recently received special attention in medicine. Nonetheless, testing the myriad of combinations generated from at least 20 coded and several hundreds of noncoded amino acids to obtain candidate sequences for each application, if possible, is time-consuming and expensive. Therefore, rapid and accurate approaches are needed to select candidates from countless combinations. In the current study, we examined three conventional descriptor sets along with a novel descriptor set derived from the simulated aggregation propensity of di- and tripeptides to model the critical aggregation concentration (CAC) of amphiphilic peptides. In contrast to the conventional descriptors, the radial kernel model derived from the novel descriptor set accurately predicted the critical aggregation concentration of the test set with a residual standard error of 0.10. The importance of aromatic side chains, as well as neighboring amino acids in the self-assembly, was emphasized by analysis of the influential descriptors. The addition of very long peptides (70-100 residues) to the data set decreased the model accuracy and changed the influential descriptors. The developed model can be used to predict the CAC of self-assembling amphiphilic peptides and also to derive rules to apply in designing novel amphiphilic peptides with desired properties.

Pyromellitic-Based Low Molecular Weight Gelators and Computational Studies of Intermolecular Interactions: A Potential Additive for Lubricant

Low molecular weight gelators (LMWG) have been extensively explored in many research fields due to their unique reversible gel-sol transformation. Intermolecular interactions between LMWG are known as the main driving force for self-assembly. During this self-assembly process, individually analyzing the contribution difference between various intermolecular interactions is crucial to understand the gel properties. Herein, we report 2,5-bis(hexadecylcarbamoyl)terephthalic acid (BHTA) as a LMWG, which could efficiently form a stable organogel with n-hexadecane, diesel, liquid paraffin, and base lubricant oil at a relatively low concentration. To investigate the contribution difference of intermolecular interactions, we first finished FT-IR spectroscopy and XRD experiments. On the basis of the d-spacing, a crude simulation model was built and then subjected to molecular dynamics (MD) simulations. Then, we knocked out the energy contribution of the H-bonding interactions and π-π stacking, respectively, to evaluate the intermolecular interactions significantly influencing the stability of the gel system. MD simulations results suggest that the self-assembly of the aggregates was mainly driven by dense H-bonding interactions between carbonyl acid and amide moieties of BHTA, which is consistent with FT-IR data. Moreover, wave function analysis at a quantum level suggested these electrostatic interactions located in the middle of the BHTA molecule were surrounded by strong dispersion attraction originating from a hydrophobic environment. Furthermore, we also confirmed that 2 wt % BHTA was able to form gel lubricant with 150BS. The coefficient of friction (COF) data show that the gel lubricant has a better tribological performance than 150BS base lubricant oil. Finally, XPS was performed and offered valuable information about the lubrication mechanism during the friction.

Self-Assembly of Nucleoside-Derived Low-Molecular-Weight Gelators: A Thermodynamics and Kinetics Study on Different Length Scales

Biocompatible materials are of paramount importance in numerous fields. Unlike chemically bridge polymer-based hydrogels, low-molecular-weight gelators can form a reversible hydrogel as their structures rely on noncovalent interaction. Although many applications with this type of hydrogel can be envisioned, we still lack their understanding due to the complexity of their self-assembly process and the difficulty in predicting their behaviors (transition temperature, gelation kinetics, the impact of solvent, etc.). In this study, we extend the investigations of a series of nucleoside-derived gelators, which only differ by subtle chemical modifications. Using a multitechnique approach, we determined their thermodynamic and kinetic features on various scale (molecular to macro) in different conditions. Monitored at the supramolecular level by circular dichroism as well as macroscopic scales by rheology and turbidimetry, we found out that the sol-gel and gel-sol transitions are greatly dependent on the concentration and on the mechanisms that are probed. Self-assembly kinetics depends on hydrogel molecules and is modulated by temperature and solvent. This fundamental study provides insight on the impact of some parameters on the gelation process, such as concentration, cooling rate, and the nature of the solvent.