Multiscale prediction of functional self-assembled materials using machine learning: high-performance surfactant molecules

Advancing micro-nano supramolecular assembly mechanisms of natural organic matter by machine learning for unveiling environmental geochemical processes

The nano-self-assembly of natural organic matter (NOM) profoundly influences the occurrence and fate of NOM and pollutants in large-scale complex environments. Machine learning (ML) offers a promising and robust tool for interpreting and predicting the processes, structures and environmental effects of NOM self-assembly. This review seeks to provide a tutorial-like compilation of data source determination, algorithm selection, model construction, interpretability analyses, applications and challenges for big-data-based ML aiming at elucidating NOM self-assembly mechanisms in environments. The results from advanced nano-submicron-scale spatial chemical analytical technologies are suggested as input data which provide the combined information of molecular interactions and structural visualization. The existing ML algorithms need to handle multi-scale and multi-modal data, necessitating the development of new algorithmic frameworks. Interpretable supervised models are crucial owing to their strong capacity of quantifying the structure-property-effect relationships and bridging the gap between simply data-driven ML and complicated NOM assembly practice. Then, the necessity and challenges are discussed and emphasized on adopting ML to understand the geochemical behaviors and bioavailability of pollutants as well as the elemental cycling processes in environments resulting from the NOM self-assembly patterns. Finally, a research framework integrating ML, experiments and theoretical simulation is proposed for comprehensively and efficiently understanding the NOM self-assembly-involved environmental issues.

Influence of Molecular Structures of Organic Foulants on the Antifouling Properties of Poly(2-methoxyethyl acrylate) and Its Analogs: A Molecular Dynamics Study

Elucidating the fouling phenomena of polymer surfaces will facilitate the molecular design of high-performance biomedical devices. Here, we investigated the remarkable antifouling properties of two acrylate materials, poly(2-methoxyethyl acrylate) (PMEA) and poly(3-methoxypropionic acid vinyl ester) (PMePVE), which have a terminal methoxy group on the side chain, via molecular dynamics simulations of binary mixtures of acrylate/methacrylate trimers with n-pentane or 2,2-dimethylpropane (neopentane), that serve as the nonpolar organic probe (organic foulants). The second virial coefficient (B₂) was determined to assess the aggregation/dispersion properties in the binary mixtures. The order of the B₂ values for the trimer/pentane mixtures indicated that the terminal methoxy group of the side chain plays an important role in enhancing the fouling resistance to nonpolar organic foulants. Here, we hypothesized that the antifouling properties of PMEA/PMePVE surfaces originate from the resistance. To evaluate the molecular-level accessibility of organic foulants to acrylate/methacrylate materials, we examined the radial distribution functions (RDFs) of the terminal methyl groups of neopentane around the main chains of the acrylate/methacrylate trimers. As a result, the third distinct RDF peaks are observed only for the methacrylate trimers. The peaks are attributed to the hydrophobic interactions between the methyl group of neopentane and that of the main chain of the trimer. Accordingly, the methyl group of the main chain of methacrylate materials, such as poly(2-hydroxyethyl methacrylate) and poly(2-methoxyethyl methacrylate), unfavorably induces fouling with organic foulants. In this study, we clarify that preventing hydrophobic interactions between an organic foulant and polymeric material is essential for enhancing the antifouling property. Our approach has great potential for evaluating the molecular-level affinities of organic foulant with polymer surfaces for the molecular design of excellent antifouling polymeric materials.

Digital Innovation Enabled Nanomaterial Manufacturing; Machine Learning Strategies and Green Perspectives

Machine learning has been an emerging scientific field serving the modern multidisciplinary needs in the Materials Science and Manufacturing sector. The taxonomy and mapping of nanomaterial properties based on data analytics is going to ensure safe and green manufacturing with consciousness raised on effective resource management. The utilization of predictive modelling tools empowered with artificial intelligence (AI) has proposed novel paths in materials discovery and optimization, while it can further stimulate the cutting-edge and data-driven design of a tailored behavioral profile of nanomaterials to serve the special needs of application environments. The previous knowledge of the physics and mathematical representation of material behaviors, as well as the utilization of already generated testing data, received specific attention by scientists. However, the exploration of available information is not always manageable, and machine intelligence can efficiently (computational resources, time) meet this challenge via high-throughput multidimensional search exploration capabilities. Moreover, the modelling of bio-chemical interactions with the environment and living organisms has been demonstrated to connect chemical structure with acute or tolerable effects upon exposure. Thus, in this review, a summary of recent computational developments is provided with the aim to cover excelling research and present challenges towards unbiased, decentralized, and data-driven decision-making, in relation to increased impact in the field of advanced nanomaterials manufacturing and nanoinformatics, and to indicate the steps required to realize rapid, safe, and circular-by-design nanomaterials.

Interactions between Acrylate/Methacrylate Biomaterials and Organic Foulants Evaluated by Molecular Dynamics Simulations of Simplified Binary Mixtures

Improving hydrophilicity is a key factor for enhancing the biocompatibility of polymer surfaces. Nevertheless, previous studies have reported that poly(2-methoxyethyl acrylate) (PMEA) surfaces demonstrate markedly better biocompatibility than more hydrophilic poly(2-hydroxyethyl methacrylate) (PHEMA) surfaces. In this work, the origins of the excellent biocompatibility of the PMEA surface are investigated using molecular dynamics (MD) simulations of simplified binary mixtures of acrylate/methacrylate trimers and organic solvents, with n-nonane, 1,5-pentanediol, or 1-octanol serving as the probe organic foulants. The interactions between the acrylate/methacrylate trimers and solvent molecules were evaluated by calculating the radial distribution function (RDF), with the resulting curves indicating that the 2-methoxyethyl acrylate (MEA) trimer has a lower affinity for n-nonane molecules than the 2-hydroxyethyl methacrylate (HEMA) trimer. This result agrees with the experimental consensus that the biocompatibility of PMEA surfaces is better than that of PHEMA surfaces, supporting the hypothesis that the affinity between an acrylate/methacrylate trimer and a foulant molecule in a simplified binary mixture is a significant factor in determining a surface's antifouling properties. The RDF curves obtained for the other two solvent systems exhibited behavior that further highlighted the advantages of the PMEA surfaces as biocompatible polymers. In addition, the validity of employing the second virial coefficient (B₂) as a predictor of antifouling properties was explored. The order of the B₂ values of different binary mixtures indicated that the MEA trimers have the lowest affinities with n-nonane molecules, which confirms that although PMEA is more hydrophobic than PHEMA, it exhibits better biocompatibility. This analysis demonstrates that the MEA's weaker miscibility with nonpolar foulants contributes to the excellent biocompatibility of PMEA. Thus, B₂ is a promising criterion for assessing the miscibility between acrylate/methacrylate materials and nonpolar organic foulants, which indicates the potential for predicting the antifouling properties of acrylate/methacrylate polymer materials by evaluating the value of B₂.

Surface science of cosmetic substrates, cleansing actives and formulations

The development of shampoo and cleansing formulations in cosmetics is at a crossroads due to consumer demands for better performing, more natural products and also the strong commitment of cosmetic companies to improve the sustainability of cosmetic products. In order to go beyond traditional formulations, it is of great importance to clearly establish the science behind cleansing technologies and appreciate the specificity of cleansing biological surfaces such as hair and skin. In this review, we present recent advances in our knowledge of the physicochemical properties of the hair surface from both an experimental and a theoretical point of view. We discuss the opportunities and challenges that newer, sustainable formulations bring compared to petroleum-based ingredients. The inevitable evolution towards more bio-based, eco-friendly ingredients and sustainable formulations requires a complete rethink of many well-known physicochemical principles. The pivotal role of digital sciences and modelling in the understanding and conception of new ingredients and formulations is discussed. We describe recent numerical approaches that take into account the specificities of the hair surface in terms of structuration, different methods that study the adsorption of formulation ingredients and finally the success of new data-driven approaches. We conclude with practical examples on current formulation efforts incorporating bio-surfactants, controlling foaming and searching for new rheological properties.

Effect of the Janus Amphiphilic Wall on the Viscosity Behavior of Aqueous Surfactant Solutions

The effects of the chemical nature of an interface are one of the key parameters which can affect self-assembly and rheological behavior. To date, several studies have reported self-assembled structures and rheological behaviors in the development of various functional materials. In this study, we investigated the self-assembly and viscosity behavior of aqueous surfactant solutions confined in three types of Janus amphiphilic nanotubes (JANTs), which have two, four, and eight sequential domains, respectively, using molecular simulation. We found that the viscosity behavior depends on the surfactant concentration and the chemical nature of the wall surface. For instance, although the concentration levels of the surfactants are the same (c = 10%), completely different viscosity behaviors were observed in the two sequential domains (Newtonian-like) and the four and eight sequential domains (strong shear-thinning) of the JANTs. Our simulations demonstrated how the rheological properties of aqueous surfactant solutions, including viscosity and velocity profiles, can be controlled by the chemical nature of the JANT wall surface, effect of confinement, and their self-assembly structures. Considering the foregoing, we hope that our study offers new knowledge on nanofluid systems.

Toward the Prediction of Multi-Spin State Charges of a Heme Model by Random Forest Regression

The random forest regression (RFR) model was introduced to predict the multiple spin state charges of a heme model, which is important for the molecular dynamic simulation of the spin crossover phenomenon. In this work, a multiple spin state structure data set with 39,368 structures of the simplified heme-oxygen binding model was built from the non-adiabatic dynamic simulation trajectories. The ESP charges of each atom were calculated and used as the real-valued response. The conformational adapted charge model (CAC) of three spin states was constructed by an RFR model using symmetry functions. The results show that our RFR model can effectively predict the on the fly atomic charges with the varying conformations as well as the atomic charge of different spin states in the same conformation, thus achieving the balance of accuracy and efficiency. The average mean absolute error of the predicted charges of each spin state is <0.02 e. The comparison studies on descriptors showed a maximum 0.06 e improvement in prediction of the charge of Fe 2+ by using 11 manually selected structural parameters. We hope that this model can not only provide variable parameters for developing the force field of the multi-spin state but also facilitate automation, thus enabling large-scale simulations of atomistic systems.

Design and Development of Lubricating Material Database and Research on Performance Prediction Method of Machine Learning

Long developing period and cumbersome evaluation for the lubricating materials performance seriously jeopardize the successful development and application of any database system in tribological field. Such major setback can be solved effectively by implementing approaches with high throughput calculation. However, it often involves with vast number of output files, which are computed on the basis of first principle computation, having different data format from that of their experimental counterparts. Commonly, the input, storage and management of first principle calculation files and their individually test counterparts, implementing fast query and display in the database, adding to the use of physical parameters, as predicted with the performance estimated by first principle approach, may solve such setbacks. Investigation is thus performed for establishing database website specifically for lubricating materials, which satisfies both data: (i) as calculated on the basis of first principles and (ii) as obtained by practical experiment. It further explores preliminarily the likely relationship between calculated physical parameters of lubricating oil and its respectively tribological and anti-oxidative performance as predicted by lubricant machine learning model. Success of the method facilitates in instructing the obtainment of optimal design, preparation and application for any new lubricating material so that accomplishment of high performance is possible.

Janus Nanoparticles Enable Entropy-Driven Mixing of Bicomponent Hydrogels

Mixing incompatible polymers in water to form homogeneous hydrogels possessing both hydrophilic and lipophilic components is challenging due to high enthalpic penalty and negligible entropic gain in total Gibbs free energy. Here we performed dissipative particle dynamics simulations and machine learning to uncover the influence of Janus nanoparticles on immiscible polymer mixtures with high water content and to predict the phase behavior of bicomponent hydrogels. An intriguing transition from kinetically arrested demixing to spontaneous mixing was observed with increasing particle concentration and decreasing particle size. The analysis reveals that the mixing is driven by a significant entropic gain of small nanoparticles being well dispersed in aqueous solvent of high-volume fraction. This finding highlights an entropy-driven mixing mechanism for nanocomposite bicomponent hydrogels. Supervised machine learning algorithms were used to establish a microstructure phase diagram with respect to particle concentration and radius, in which homogeneous, percolated, clustered, and separated phases, as well as corresponding phase boundaries, were clearly identified.

Theoretical analyses on water cluster structures in polymer electrolyte membrane by using dissipative particle dynamics simulations with fragment molecular orbital based effective parameters

The mesoscopic structures of polymer electrolyte membrane (PEM) affect the performances of fuel cells. Nafion® with the Teflon® backbone has been the most widely used of all PEMs, but sulfonated poly-ether ether-ketone (SPEEK) having an aromatic backbone has drawn interest as an alternative to Nafion. In the present study, a series of dissipative particle dynamics (DPD) simulations were performed to compare Nafion and SPEEK. These PEM polymers were modeled by connected particles corresponding to the hydrophobic backbone and the hydrophilic moiety of sulfonic acid group. The water particle interacting with Nafion particles was prepared as well. The crucial interaction parameters among DPD particles were evaluated by a series of calculations based on the fragment molecular orbital (FMO) method in a non-empirical way (Okuwaki et al., J. Phys. Chem. B, 2018, 122, 338–347). Through the DPD simulations, the water and hydrophilic particles aggregated, forming cluster networks surrounded by the hydrophobic phase. The structural features of formed water clusters were investigated in detail. Furthermore, the differences in percolation behaviors between Nafion and SPEEK revealed much better connectivity among water clusters by Nafion. The present FMO-DPD simulation results were in good agreement with available experimental data.

The mesoscopic structures of polymer electrolyte membrane (PEM) affect the performances of fuel cells.