Controlling Biofilm Transport with Porous Metamaterials Designed with Bayesian Learning

Biofilm growth and transport in confined systems frequently occur in natural and engineered systems. Designing customizable engineered porous materials for controllable biofilm transportation properties could significantly improve the rapid utilization of biofilms as engineered living materials for applications in pollution alleviation, material self-healing, energy production, and many more. We combine Bayesian optimization (BO) and individual-based modeling to conduct design optimizations for maximizing different porous materials' (PM) biofilm transportation capability. We first characterize the acquisition function in BO for designing 2-dimensional porous membranes. We use the expected improvement acquisition function for designing lattice metamaterials (LM) and 3-dimensional porous media (3DPM). We find that BO is 92.89% more efficient than the uniform grid search method for LM and 223.04% more efficient for 3DPM. For all three types of structures, the selected characterization simulation tests are in good agreement with the design spaces approximated with Gaussian process regression. All the extracted optimal designs exhibit better biofilm growth and transportability than unconfined space without substrates. Our comparison study shows that PM stimulates biofilm growth by taking up volumetric space and pushing biofilms' upward growth, as evidenced by a 20% increase in bacteria cell numbers in unconfined space compared to porous materials, and 128% more bacteria cells in the target growth region for PM-induced biofilm growth compared with unconfined growth. Our work provides deeper insights into the design of substrates to tune biofilm growth, analyzing the optimization process and characterizing the design space, and understanding biophysical mechanisms governing the growth of biofilms.Comment: 23 pages, 7 main figures, accepte

Benchmarking Inverse Optimization Algorithms for Molecular Materials Discovery

Machine learning-based molecular materials discovery has attracted enormous attention recently due to its flexibility in dealing with black box models. Yet, metaheuristic algorithms are not as widely applied to materials discovery applications. We comprehensively compare 11 different optimization algorithms for molecular materials design with targeted properties. These algorithms include Bayesian Optimization (BO) and multiple metaheuristic algorithms. We performed 5000 material evaluations repeated 5 times with different randomized initialization to optimize defined target properties. By maximizing the bulk modulus and minimizing the Fermi energy through perturbing parameterized molecular representations, we estimated the unique counts of molecular materials, mean density scan of the objectives space, mean objectives, and frequency distributed over the materials' representations and objectives. GA, GWO, and BWO exhibit higher variances for materials count, density scan, and mean objectives; and BO and Runge Kutta optimization (RUN) display generally lower variances. These results unveil that nature-inspired algorithms contain more uncertainties in the defined molecular design tasks, which correspond to their dependency on multiple hyperparameters. RUN exhibits higher mean objectives whereas BO displayed low mean objectives compared with other benchmarked methods. Combined with materials count and density scan, we propose that BO strives to approximate a more accurate surrogate of the design space by sampling more molecular materials and hence have lower mean objectives, yet RUN will repeatedly sample the targeted molecules with higher objective values. Our work shed light on automated digital molecular materials design and is expected to elicit future studies on materials optimization such as composite and alloy design based on specific desired properties.Comment: 15 pages, 5 figures, for the main manuscrip

Unraveling the molecular mechanisms of thermo-responsive properties of silk-elastin-like proteins by integrating multiscale modeling and experiment

Adaptive hydrogels tailor-made from silk-elastin-like proteins (SELPs) possess excellent biocompatibility and biodegradability with properties that are tunable and responsive to multiple simultaneous external stimuli. To unravel the molecular mechanisms of their physical response to external stimuli in tandem with experiments, here we predict and measure the variation in structural properties as a function of temperature through coarse-grained (CG) modeling of individual and crosslinked SE8Y and S4E8Y molecules, which have ratios of 1:8 and 4:8 of silk to elastin blocks respectively. Extensive structural reshuffling in single SE8Y molecules led to the increased compactness of the structure, whereas S4E8Y molecules did not experience any significant changes as they already adopted very compact structures at low temperatures. Crosslinking of SE8Y molecules at high concentrations impeded their structural transition at high temperatures that drastically reduced the degree of deswelling through extensive suppression of the structural shuffling and the trapping of the molecules in high potential energy states due to inter-molecular constraints. This integrative experimental and computational understanding of the thermal response in single molecules of SELPs and their crosslinked networks should lead to further improvements in the properties of SELP hydrogels through predictive designs and their wider applications in biomaterials and tissue engineering.United States. Department of Defense. Office of Naval Research (Grant N00014-16-1-233)United States. National Institutes of Health (Grant U01 EB014976)Singapore. Agency for Science, Technology and Research (Grant A1786a0031)United States. National Science Foundation. (Grant ACI-1053575

Benchmarking inverse optimization algorithms for materials design

Machine learning-based inverse materials discovery has attracted enormous attention recently due to its flexibility in dealing with black box models. Yet, many metaheuristic algorithms are not as widely applied to materials discovery applications as machine learning methods. There are ongoing challenges in applying different optimization algorithms to discover materials with single- or multi-elemental compositions and how these algorithms differ in mining the ideal materials. We comprehensively compare 11 different optimization algorithms for the design of single- and multi-elemental crystals with targeted properties. By maximizing the bulk modulus and minimizing the Fermi energy through perturbing the parameterized elemental composition representations, we estimated the unique counts of elemental compositions, mean density scan of the objectives space, mean objectives, and frequency distributed over the materials’ representations and objectives. We found that nature-inspired algorithms contain more uncertainties in the defined elemental composition design tasks, which correspond to their dependency on multiple hyperparameters. Runge–Kutta optimization (RUN) exhibits higher mean objectives, whereas Bayesian optimization (BO) displayed low mean objectives compared with other methods. Combined with materials count and density scan, we propose that BO strives to approximate a more accurate surrogate of the design space by sampling more elemental compositions and hence have lower mean objectives, yet RUN will repeatedly sample the targeted elemental compositions with higher objective values. Our work sheds light on the automated digital design of materials with single- and multi-elemental compositions and is expected to elicit future studies on materials optimization, such as composite and alloy design based on specific desired properties

Understanding How Metal-Ligand Coordination Enables Solvent Free Ionic Conductivity in PDMS

Ionically conductive polymers are commonly made of monomers containing high polarity moieties to promote high ion dissociation, like poly(ethylene oxide) (PEO), polyvinylidene difluoride (PVDF), poly(vinyl alcohol) (PVA). However, the glass transition temperature ($T_g$) of these polymers are relatively high, and therefore yields a glassy state at room temperature and limits the mechanical flexibility of the material. Although polydimethylsiloxane (PDMS) has many attractive physical and chemical properties, including low glass transition temperature, mechanical flexibility, and good biocompatibility, its low dielectric constant suppresses ion dissociation. In this paper, we overcome this shortage by functionalizing the PDMS with ligands that can form labile coordination with metal ions, which greatly promotes the ion dissociation and improves the ionic conductivity by orders of magnitude. By combining an experimental study with a fully atomistic molecular dynamics simulation, we systematically investigated the ion transport mechanisms in this low $T_g$, low intrinsic conductivity material

Dissipative Particle Dynamics Study of Ultraviolet Ink Agglomeration in 3D Inkjet Printing

Due to its outstanding properties, ultraviolet (UV) ink is currently a major ink type used in 3D inkjet printing applications and additive manufacturing (AM) in general. However, there exists a major challenge which has to be addressed and overcome, namely the agglomeration issue, which can potentially lead to nozzle clogging. To understand the underlying physics and chemistry of the agglomeration phenomenon, numerical characterisation provides a low-cost high resolution solution, if the correct numerical methodology is appropriately exploited. F or this meso-scale agglomeration problem, dissipative particle dynamics (DPD) is a highly suitable simulation technique, and in this preliminary study, the commercial solver Material Studio 8.0 from Accelrys Inc is utilized. Here, the coarse-grained models are generated by directly coarse-grained from the atomistic model. Commercial UV inks used in AM applications today are usually composed of oligomers, monomers, photo-initiators, pigments, and other additives such as stabilizers and surfactants. Among these components. oligomers have the highest tendency to agglomerate, which can agitate the stability and quality of the printing fluid, and possibly lead to nozzle clogging. Specifically we study and examine the morphological characteristics of an UV ink composing of photopolymers of polystyrene (PS) and polyethylene glycol (PEG) as the main components in the simulation model. In this case, styrene is chosen as it is one of the most prevalent commercial photopolymers in present 3D inkjet applications, while ethylene glycol is a photopolymers known to improve ink viscosity. The preliminary results for different models considered show that the kind of photopolymers and their constituent ratios affect the agglomeration morphology of the system, and the existence of both oligomers and monomers results in mutual morphological benefits against agglomeration. The results also reveal the importance of other additives in the ink composition to prevent, reduce and control various forms of agglomeration to achieve enhanced print quality.ASTAR (Agency for Sci., Tech. and Research, S’pore)Published versio

Superlubricity-activated thinning of graphite flakes compressed by passivated crystalline silicon substrates for graphene exfoliation

A special thinning phenomenon is observed through molecular dynamics, where compression of AB-stacked graphite flakes between two hydrogen-terminated silicon substrates leads to the exfoliation of graphene layers. We have used multiple molecular dynamics simulations to study how this thinning phenomenon is affected by parameters such as size, number of graphene layers, and the crystalline orientation of the substrate surface. It is shown that this thinning phenomenon occurs through the activation of an inter-layer superlubricity regime, caused by torque-induced spontaneous rotations of the layers which are initiated by in-plane shear modes of graphite during compression.ASTAR (Agency for Sci., Tech. and Research, S’pore)Accepted versio

Adsorption and conformational evolution of alpha-helical BSA segments on graphene : a molecular dynamics study

Molecular dynamics (MD) simulations are performed to investigate the adsorption mechanics and conformational dynamics of single and multiple bovine serum albumin (BSA) peptide segments on single-layer graphene through analysis of parameters such as the root-mean-square displacements, number of hydrogen bonds, helical content, interaction energies, and motions of mass center of the peptides. It is found that for the single segment system, destabilization of the helical structures in the form of the reduction in hydrogen bond numbers and α-helical content of the peptides occurred due to the strong interactions between BSA peptides and graphene. Similar destabilizations of the individual segments in the multi-segment system can occur as well, albeit with greater complexity and in a lesser degree due to the inter-segment interactions. Alleviation of decreases in the total helical content in the multi-segment system indicates protective capabilities of segment–segment interactions, which weaken their interactions with graphene. Diffusive motion upon adsorption of the segment(s) onto graphene is found to be highly confined, and the distance traversed by each segment in the multi-segment system was more significant than that in the single segment system, similarly attributable to reductions in their interactions with graphene due to inter-segment interactions

Carbon nanotube arrays as multilayer transverse flow carbon nanotube membrane for efficient desalination

Although single layer transverse flow carbon nanotube (CNT) membrane (TFCM) has been shown to be ultrapermeable with high salt rejection, its physical fabrication with sub-nanometre slits remains a significant challenge to its development. This work presents the multilayer TFCM, which resembles vertically aligned CNT arrays, as an alternative candidate for efficient desalination. Using molecular dynamics, this work shows that multilayer TFCM can provide permeability and salt rejection on par with its single layer counterpart. By multilayering, the slit size between neighbouring CNTs can be increased to nanometre range while still maintaining high salt rejection. The increase in slit size counteracts the reduction in permeability due to multilayering, thereby allowing multilayer TFCM to achieve permeability performance comparable to the single layer TFCM. The effects of the number of layers n and other design parameters (interlayer distance d, CNT diameter DCNT , offset h) on the desalination performance of multilayer TFCM are investigated thoroughly using results from non-equilibrium and equilibrium molecular dynamics. It was found that the desalination performance is not sensitive to variations in d, DCNT or h. Finally, this work provides computational evidence that the multilayer TFCM, which could be fabricated using techniques for current dense vertically aligned CNT arrays, can make an efficient design for future low dimensional materials membrane