Browsing by Author "Jayaraman, Arthi"
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Item A computational method for rapid analysis polymer structure and inverse design strategy (RAPSIDY)(Soft Matter, 2024-09-30) Liao, Vinson; Myers, Tristan; Jayaraman, ArthiTailoring polymers for target applications often involves selecting candidates from a large design parameter space including polymer chemistry, molar mass, sequence, and architecture, and linking each candidate to their assembled structures and in turn their properties. To accelerate this process, there is a critical need for inverse design of polymers and fast exploration of the structures they can form. This need has been particularly challenging to fulfill due to the multiple length scales and time scales of structural arrangements found in polymers that together give rise to the materials’ properties. In this work, we tackle this challenge by introducing a computational framework called RAPSIDY – Rapid Analysis of Polymer Structure and Inverse Design strategY. RAPSIDY enables inverse design of polymers by accelerating the evaluation of stability of multiscale structure for any given polymer design (sequence, composition, length). We use molecular dynamics simulations as the base method and apply a guiding potential to initialize polymers chains of a selected design within target morphologies. After initialization, the guiding potential is turned off, and we allow the chains and structure to relax. By evaluating similarity between the target morphology and the relaxed morphology for that polymer design, we can screen many polymer designs in a highly parallelized manner to rank designs that are likely to remain in that target morphology. We demonstrate how this method works using an example of a symmetric, linear pentablock, AxByAzByAx, copolymer system for which we determine polymer sequences that exhibit stable double gyroid morphology. Rather than trying to identify the global free-energy minimum morphology for a specific polymer design, we aim to identify candidates of polymer design parameter space that are more stable in the desired morphology than others. Our approach reduces computational costs for design parameter exploration by up to two orders-of-magnitude compared to traditional MD methods, thus accelerating design and engineering of novel polymer materials for target applications.Item DARWIN - A Resource for Computational and Data-intensive Research at the University of Delaware and in the Delaware Region(Data Science Institute [DSI], University of Delaware, Newark, DE, 2021) Eigenmann, Rudolf; Bagozzi, Benjamin E.; Jayaraman, Arthi; Totten, William; Wu, Cathy H.Item Impact of collagen-like peptide (CLP) heterotrimeric triple helix design on helical thermal stability and hierarchical assembly: a coarse-grained molecular dynamics simulation study(Soft Matter, 2022-04-05) Taylor, Phillip A.; Kloxin, April M.; Jayaraman, ArthiCollagen-like peptides (CLP) are multifunctional materials garnering a lot of recent interest from the biomaterials community due to their hierarchical assembly and tunable physicochemical properties. In this work, we present a computational study that links the design of CLP heterotrimers to the thermal stability of the triple helix and their self-assembly into fibrillar aggregates and percolated networks. Unlike homotrimeric helices, the CLP heterotrimeric triple helices in this study are made of CLP strands of different chain lengths that result in ‘sticky’ ends with available hydrogen bonding groups. These ‘sticky’ ends at one end or both ends of the CLP heterotrimer then facilitate inter-helix hydrogen bonding leading to self-assembly into fibrils (clusters) and percolated networks. We consider the cases of three sticky end lengths – two, four, and six repeat units – present entirely on one end or split between two ends of the CLP heterotrimer. We observe in CLP heterotrimer melting curves generated using coarse grained Langevin dynamics simulations at low CLP concentration that increasing sticky end length results in lower melting temperatures for both one and two sticky ended CLP designs. At higher CLP concentrations, we observe non-monotonic trends in cluster sizes with increasing sticky end length with one sticky end but not for two sticky ends with the same number of available hydrogen bonding groups as the one sticky end; this nonmonotonicity stems from the formation of turn structures stabilized by hydrogen bonds at the single, sticky end for sticky end lengths greater than four repeat units. With increasing CLP concentration, heterotrimers also form percolated networks with increasing sticky end length with a minimum sticky end length of four repeat units required to observe percolation. Overall, this work informs the design of thermoresponsive, peptide-based biomaterials with desired morphologies using strand length and dispersity as a handle for tuning thermal stability and formation of supramolecular structures.Item Machine learning for analyzing atomic force microscopy (AFM) images generated from polymer blends(Digital Discovery, 2024-10-21) Paruchuri, Aanish; Wang, Yunfei; Gu, Xiaodan; Jayaraman, ArthiIn this paper, we present a new machine learning (ML) workflow with unsupervised learning techniques to identify domains within atomic force microscopy (AFM) images obtained from polymer films. The goal of the workflow is to (i) identify the spatial location of two types of polymer domains with little to no manual intervention (Task 1) and (ii) calculate the domain size distributions, which in turn can help qualify the phase separated state of the material as macrophase or microphase ordered/disordered domains (Task 2). We briefly review existing approaches used in other fields – computer vision and signal processing – that can be applicable to the above tasks frequently encountered in the field of polymer science and engineering. We then test these approaches from computer vision and signal processing on the AFM image dataset to identify the strengths and limitations of each of these approaches for our first task. For our first domain segmentation task, we found that the workflow using discrete Fourier transform (DFT) or discrete cosine transform (DCT) with variance statistics as the feature works the best. The popular ResNet50 deep learning approach from the computer vision field exhibited relatively poorer performance in the domain segmentation task for our AFM images as compared to the DFT and DCT based workflows. For the second task, for each of the 144 input AFM images, we then used an existing Porespy Python package to calculate the domain size distribution from the output of that image from the DFT-based workflow. The information and open-source codes we share in this paper can serve as a guide for researchers in the fields of polymers and soft materials who need ML modeling and workflows for automated analyses of AFM images from polymer samples that may have crystalline/amorphous domains, sharp/rough interfaces between domains, or micro- or macro-phase separated domains.Item Machine Learning-Enhanced Computational Reverse-Engineering Analysis for Scattering Experiments (CREASE) for Analyzing Fibrillar Structures in Polymer Solutions(Macromolecules, 2022-12-27) Wu, Zijie; Jayaraman, ArthiIn this work, we present a machine learning (ML)-enhanced computational reverse-engineering analysis of scattering experiments (CREASE) approach to analyze the small-angle scattering profiles from polymer solutions with assembled semiflexible fibrils with dispersity in fibril diameters (e.g., aqueous solutions of methylcellulose fibrils). This work is an improvement over the original CREASE method [Beltran-Villegas, D. J.; J. Am. Chem. Soc., 2019, 141, 14916−14930], which identifies relevant dimensions of assembled structures in polymer solutions from their small-angle scattering profiles without relying on traditional analytical models. Here, we improve the original CREASE approach by incorporating ML for analyzing assembled semiflexible fibrillar structures with disperse fibril diameters. We first validate our CREASE approach without ML by taking as input the scattering profiles of in silico structures with known dimensions (diameter, Kuhn length) and reproducing as output those known dimensions within error. We then show how the incorporation of ML (specifically an artificial neural network, denoted as NN) within the CREASE approach improves the speed of workflow without sacrificing the accuracy of the determined fibrillar dimensions. Finally, we apply NN-enhanced CREASE to experimental small-angle X-ray scattering profiles from methylcellulose fibrils obtained by Lodge, Bates, and co-workers [Schmidt, P. W.; Macromolecules, 2018, 51, 7767−7775] to determine fibril diameter distribution and compare NN-enhanced CREASE’s output with their fibril diameter distribution fitted using analytical models. The diameter distributions of methylcellulose fibrils from NN-enhanced CREASE are similar to those obtained from analytical model fits, confirming the results by Lodge, Bates, and co-workers that methylcellulose form fibrils with consistent average diameters of ∼15–20 nm regardless of the molecular weight of methylcellulose chains. The successful implementation of NN-enhanced CREASE in handling experimental scattering profiles of complex macromolecular assembled structures with dispersity in dimensions demonstrates its potential for application toward other unconventional fibrillar systems that may not have appropriate analytical models.Item Modeling Structural Colors from Disordered One-Component Colloidal Nanoparticle-Based Supraballs Using Combined Experimental and Simulation Techniques(ACS Materials Letters, 2022-09-05) Patil, Anvay; Heil, Christian M.; Vanthournout, Bram; Singla, Saranshu; Hu, Ziying; Ilavsky, Jan; Gianneschi, Nathan C.; Shawkey, Matthew D.; Sinha, Sunil K.; Jayaraman, Arthi; Dhinojwala, AliBright, saturated structural colors in birds have inspired synthesis of self-assembled, disordered arrays of assembled nanoparticles with varied particle spacings and refractive indices. However, predicting colors of assembled nanoparticles, and thereby guiding their synthesis, remains challenging due to the effects of multiple scattering and strong absorption. Here, we use a computational approach to first reconstruct the nanoparticles’ assembled structures from small-angle scattering measurements and then input the reconstructed structures to a finite-difference time-domain method to predict their color and reflectance. This computational approach is successfully validated by comparing its predictions against experimentally measured reflectance and provides a pathway for reverse engineering colloidal assemblies with desired optical and photothermal properties.Item Polymer solution structure and dynamics within pores of hexagonally close-packed nanoparticles(Soft Matter, 2022-10-20) Heil, Christian M.; Jayaraman, ArthiUsing coarse-grained molecular dynamics simulations, we examine structure and dynamics of polymer solutions under confinement within the pores of a hexagonally close-packed (HCP) nanoparticle system with nanoparticle diameter fifty times that of the polymer Kuhn segment size. We model a condition where the polymer chain is in a good solvent (i.e., polymer–polymer interaction is purely repulsive and polymer–solvent and solvent–solvent interactions are attractive) and the polymer–nanoparticle and solvent–nanoparticle interactions are purely repulsive. We probe three polymer lengths (N = 10, 114, and 228 Kuhn segments) and three solution concentrations (1, 10, and 25%v) to understand how the polymer chain conformations and chain center-of-mass diffusion change under confinement within the pores of the HCP nanoparticle structure from those seen in bulk. The known trend of bulk polymer Rg2 decreasing with increasing concentration no longer holds when confined in the pores of HCP nanoparticle structure; for example, for the 114-mer, the HCP 〈Rg2〉 at 1%v concentration is lower than HCP 〈Rg2〉 at 10%v concentration. The 〈Rg2〉 of the 114-mer and 228-mer exhibit the largest percent decline going from bulk to HCP at the 1%v concentration and the smallest percent decline at the 25%v concentration. We also provide insight into how the confinement ratio (CR) of polymer chain size to pore size within tetrahedral and octahedral pores in the HCP arrangement of nanoparticles affects the chain conformation and diffusion at various concentrations. At the same concentration, the N = 114 has significantly more movement between pores than the N = 228 chains. For the N = 114 polymer, the diffusion between pores (i.e., inter-pore diffusion) accelerates the overall diffusion rate for the confined HCP system while for the N = 228 polymer, the polymer diffusion in the entire HCP is dominated by the diffusion within the tetrahedral or octahedral pores with minor contributions from inter-pore diffusion. These findings augment the fundamental understanding of macromolecular diffusion through large, densely packed nanoparticle assemblies and are relevant to research focused on fabrication of polymer composite materials for chemical separations, storage, optics, and photonics. We perform coarse-grained molecular dynamics simulations to understand structure and dynamics of polymer solutions under confinement within hexagonal close packed nanoparticles with radii much larger than the polymer chain's bulk radius of gyration.Item Proceedings of the 2021 DARWIN Computing Symposium(Data Science Institute of the University of Delaware, 2021-02-12) Bagozzi, Benjamin E.; Eigenmann, Rudolf; Jayaraman, Arthi; Totten, William; Wu, Cathy H.The DARWIN Computing Symposium 2021—sponsored by the Data Science Institute of the University of Delaware—was held on February 12, 2021. It represented the second event in a series of Symposia motivated by a National Science Foundation (NSF) MRI Award, also known as the Delaware Advanced Research Workforce and Innovation Network (DARWIN). As part of an NSF Major Research Instrumentation award (OAC-1919839), DARWIN has the goal of catalyzing "research and education at the University of Delaware (UD) and partners by acquiring a big data and high-performance computing system and making this instrument available to the community." This particular Symposium showcased recent computational and data-enabled research across the Delaware region, offered perspectives on broadening participation in computational and data-intensive research, and facilitated opportunities for forming collaborations among future DARWIN users at UD and regional partners. It also provided an overview of the newly operational DARWIN big data and high-performance computing machine via a panel on “early user mode” experiences. The 2021 DARWIN Computing Symposium was supported by the NSF and UD’s Data Science Institute. Dr. Benjamin E. Bagozzi, UD Associate Professor and DARWIN Co-PI, served as chair of the 2021 Symposium.Item Proceedings of the 2022 DARWIN Computing Symposium(Data Science Institute of the University of Delaware, 2022-03-24) Hadden-Perilla, Jodi A.; Perilla, Juan R.; Bagozzi, Benjamin E.; Eigenmann, Rudolf; Jayaraman, Arthi; Totten, William; Wu, Cathy H.The DARWIN Computing Symposium 2022—sponsored by the Data Science Institute of the University of Delaware—was held on March 24, 2022. It represented the third event in a series of Symposia motivated by a National Science Foundation (NSF) MRI Award, also known as the Delaware Advanced Research Workforce and Innovation Network (DARWIN). As part of an NSF Major Research Instrumentation award (OAC-1919839), DARWIN has the goal of catalyzing "research and education at the University of Delaware (UD) and partners by acquiring a big data and high-performance computing system and making this instrument available to the community." This third DARWIN Computing Symposium presented a wide variety of research enabled by the DARWIN machine to the Delaware community. Alongside this, it showcased additional computational and dataenabled research, provided details on accessing DARWIN for University of Delaware (UD) and partner institutions, and facilitated opportunities for forming collaborations among future users at UD and regional partners. In addition to the NSF and the Data Science Institute, the 2022 DARWIN Computing Symposium was sponsored by DELL and Nemours Children's Health. Drs. Jodi Haden-Perilla and Juan Perilla, both of the University of Delaware, served as co-chairs of the 2022 DARWIN Computing Symposium.Item Proceedings of the 2023 DARWIN Computing Symposium(Data Science Institute of the University of Delaware, 2023-02-23) Safronova, Marianna S.; Bagozzi, Benjamin E.; Eigenmann, Rudolf; Jayaraman, Arthi; Totten, William; Wu, Cathy H.The DARWIN Computing Symposium 2023—sponsored by the Data Science Institute of the University of Delaware—was held on February 23, 2023. It represented the fourth event in a series of Symposia motivated by a National Science Foundation (NSF) MRI Award, also known as the Delaware Advanced Research Workforce and Innovation Network (DARWIN). As part of an NSF Major Research Instrumentation award (OAC-1919839), DARWIN has the goal of catalyzing "research and education at the University of Delaware (UD) and partners by acquiring a big data and high-performance computing system and making this instrument available to the community." This fourth DARWIN Computing Symposium presented a wide variety of research enabled by the DARWIN machine to the Delaware community. It also showcased additional computational and data-enabled research, provided perspectives on broadening participation in computational and data-intensive research, and facilitated opportunities for forming collaborations among future users at UD and regional partners. In addition to the NSF and the Data Science Institute, the 2023 DARWIN Computing Symposium was sponsored by AMD, BioCurie, Chemours, and Tech Impact. Dr. Marianna Safronova, Professor of Physics at the Department of Physics and Astronomy, University of Delaware, served as chair of the 2023 DARWIN Computing Symposium.Item Proceedings of the 2024 DARWIN Computing Symposium(Data Science Institute of the University of Delaware, 2024-02-12) Hsu, Tian-Jian; Bagozzi, Benjamin E.; Eigenmann, Rudolf; Jayaraman, Arthi; Totten, William; Wu, Cathy H.; Blaustein, Michael; Blinova, Daria; Carney, Lynette; Huffman, John; Smith, Samantha; Zhang, JiayeThe DARWIN Computing Symposium 2024—sponsored by the Data Science Institute (DSI) of the University of Delaware—was held on February 12, 2024. It represented the fifth event in a series of Symposia motivated by a National Science Foundation (NSF) MRI Award, also known as the Delaware Advanced Research Workforce and Innovation Network (DARWIN). As part of an NSF Major Research Instrumentation award (OAC-1919839), DARWIN focuses on catalyzing "research and education at the University of Delaware (UD) and partners by acquiring a big data and high-performance computing system and making this instrument available to the community." In an effort to identify and advance future computing needs for artificial intelligence, to reduce the overhead for domain scientists utilizing HPC, and to develop regional partnerships, this fifth DARWIN Computing Symposium more specifically featured a panel and a keynote talk, as well as a series of research talks on DARWIN-enabled research, on computational and data-intensive (CDI) research/training needs, and on AI-focused CDI research more generally. These talks highlighted the use of AI in HPC to advance sciences and predictive capabilities with societal relevance across a wide range of domains. A panel discussion then facilitated interactions between research software engineers and domain scientists with an eye towards advancing scientific progress in different disciplines. In addition, 30 poster presentations by students and postdocs highlighted a number of relevant CDI research projects. Alongside the NSF and the Data Science Institute, the 2023 DARWIN Computing Symposium was sponsored by Tech Impact, UD’s Delaware Environmental Institute, UD’s Center for Applied Coastal Research, UD Information Technologies, and the University of Delaware Faculty Senate. Dr. Tian-Jian Hsu, University of Delaware Professor of Civil & Environmental Engineering and Director of the Center for Applied Coastal Research served as chair of the 2024 DARWIN Computing Symposium.Item Random field reconstruction of three-phase polymer structures with anisotropy from 2D-small-angle scattering data(Soft Matter, 2024-10-14) Kronenberger, Stephen; Gupta, Nitant; Gould, Benjamin; Peterson, Colin; Jayaraman, ArthiIn this paper we present a computational method to analyze 2-dimensional (2D) small-angle scattering data obtained from phase-separated soft materials and output three-dimensional (3D) real-space structures of the three types of domains/phases. Specifically, we use 2D small-angle X-ray scattering (SAXS) data obtained from hydrated NafionTM membranes and develop a workflow using random fields to build the 3D real-space structure comprised of amorphous hydrophilic domains, amorphous polymer domains, and crystalline polymer domains. We demonstrate the method works well by showing that the reconstructed 3D NafionTM structures have a computed scattering profile that matches the input experimental scattering profile. Though not demonstrated in this work, such reconstructions can be used for further analysis of domain shapes and sizes, as well as prediction of transport properties through the structure. Our method in this work extends capabilities beyond the previously published random field small angle scattering reconstruction method introduced by Berk [Phys. Rev. Lett. 1987, 58 (25), 2718–2721] that had been used to reconstruct structures from 1D small angle scattering data of two-phase systems. The method in this work can be used to generate isotropic, two-phase reconstructions, but can also handle 2D SAXS profiles from three-phase systems that have structural anisotropy resulting from material processing effects.Item Self-consistent field theory and coarse-grained molecular dynamics simulations of pentablock copolymer melt phase behavior(Molecular Systems Design & Engineering, 2024-09-24) Park, So Jung; Myers, Tristan; Liao, Vinson; Jayaraman, ArthiBlock copolymer (BCP) self-assembly leads to nanostructured materials with diverse ordered morphologies, some of which are attractive for transport applications. Multiblock AB copolymers are of interest as they offer a larger design parameter space than diblock copolymers allowing researchers to tailor their self-assembly to achieve target morphologies. In this study, we investigate the phase behavior of symmetric AxByAzByAx and BxAyBzAyBx pentablock copolymers (pentaBCPs) where A and B monomers have the same statistical segment length. We use a combination of self-consistent field theory (SCFT) calculations and molecular dynamics (MD) simulations to link the polymer design parameters, namely the fraction of middle block volume to the volume of all blocks of same type, τ, overall volume fraction of A block, fA, and segregation strength, χN, to the equilibrium morphologies and the distributions of chain conformations in these morphologies. In the phase diagrams calculated using SCFT, we observe broader double gyroid windows and the existence of lamellar morphologies even at small values fA in contrast to what has been seen for diblock copolymers. We also see a reentrant phase sequence of double gyroid → cylinder → lamellae → cylinder → double gyroid with increasing τ at fixed fA. The chain conformations adopted in these morphologies are sampled in coarse-grained MD simulations and quantified with distributions of the chain end-to-end distance and fractions of chains whose middle (A or B) and end (A or B) blocks remain within domains of same chemistry (A or B). These analyses show that the pentaBCP chains adopt “looping”, “bridging”, and “hybrid” (both looping and bridging) conformations, with a majority of the chains adopting the hybrid conformation. The spatial distributions for each of the blocks in the pentaBCPs show that blocks of the same type in a chain locally segregate within the same domains, with shorter blocks segregating towards the domain boundaries and longer blocks filling the domain interior. This combined SCFT-MD approach enables us to rapidly screen the extensive pentaBCP design space to identify design rules for transport-favorable morphologies as well as verify the chain conformations and spatial arrangements associated with the theory predicted reentrant phase behavior. Design, System, Application Block copolymers (BCPs) self-assemble into a variety of nanostructures, such as lamellae, hexagonal-packed cylinders, and double gyroid, which in turn enable engineering of materials with desired transport and mechanical properties. The morphology formed by a given BCP is highly dependent on its design in terms of the monomer chemistry, number of blocks (diblock to multiblock), block lengths, and block sequence. As compared to diblock copolymers, multiblock copolymers have been studied to a smaller extent due to their larger design parameter space. However, it is noteworthy that a handful of computational studies of multiBCPs have uncovered novel nanostructures and phase behavior not seen in the well-studied diblock copolymers. In this study, we focus on linking the design of pentablock copolymers (pentaBCPs) to their morphology in the melt state using self-consistent field theory (SCFT) and molecular dynamics (MD) simulations. Our goal is to identify design rules for experimentalists looking to broaden phase windows of transport-friendly morphologies, such as double gyroid. The combination of theory and simulation allows for faster screening of design parameter space using SCFT as compared to MD simulations and quantification of chain conformations using MD simulations especially when getting distribution of chain conformations from SCFT is non-trivial.