Rheo-PIV Investigation of Fracture and Self-Healing in a Triblock Copolymer Gel

Physically associating polymer gels have shown the ability to heal after failure, making them promising candidates for various medical applications or consumer products. However, the processes by which these materials self-heal is not well-understood. This study seeks to explain the self-healing behavior of the triblock copolymer poly(methyl methacrylate)-poly(n-butyl acrylate)-poly(methyl methacrylate), or PMMA-PnBA-PMMA, by probing the material’s post-fracture behavior with rheometry and particle image velocimetry (PIV). The self-healing behavior was studied by deforming each gel in shear until failure multiple times with “recovery” periods in-between. PIV was used to verify the occurrence of each fracture in both time and space. Stress relaxation experiments were also performed on the gels to give greater context to the results of the investigation into fracture recovery. Using these data, it was possible to determine the activation energy required for the network chain dissociation and re-association that transpires during the deformation and self-healing of the gel. Stress relaxation experiments yielded an activation energy of 359 kJ/mole for chain dissociation, while fracture-recovery experiments produced an activation energy of 439 kJ/mole for chain re-association. Building upon these insights could lead to a better understanding of the microscopic mechanisms that govern the behavior of intrinsic self-healing materials so that they can be used to their full potential

Ranking the synthesizability of hypothetical zeolites with the sorting hat

Zeolites are nanoporous alumino-silicate frameworks widely used as catalysts and adsorbents. Even though millions of siliceous networks can be generated by computer-aided searches, no new hypothetical framework has yet been synthesized. The needle-in-a-haystack problem of finding promising candidates among large databases of predicted structures has intrigued materials scientists for decades; yet, most work to date on the zeolite problem has been limited to intuitive structural descriptors. Here, we tackle this problem through a rigorous data science scheme—the “Zeolite Sorting Hat”—that exploits interatomic correlations to discriminate between real and hypothetical zeolites and to partition real zeolites into compositional classes that guide synthetic strategies for a given hypothetical framework. We find that, regardless of the structural descriptor used by the Zeolite Sorting Hat, there remain hypothetical frameworks that are incorrectly classified as real ones, suggesting that they might be good candidates for synthesis. We seek to minimize the number of such misclassified frameworks by using as complete a structural descriptor as possible, thus focusing on truly viable synthetic targets, while discovering structural features that distinguish real and hypothetical frameworks as an output of the Zeolite Sorting Hat. Further ranking of the candidates can be achieved based on thermodynamic stability and/or their suitability for the desired applications. Based on this workflow, we propose three hypothetical frameworks differing in their molar volume range as the top targets for synthesis, each with a composition suggested by the Zeolite Sorting Hat. Finally, we analyze the behavior of the Zeolite Sorting Hat with a hierarchy of structural descriptors including intuitive descriptors reported in previous studies, finding that intuitive descriptors produce significantly more misclassified hypothetical frameworks, and that more rigorous interatomic correlations point to second-neighbor Si–O distances around 3.2–3.4 Å as the key discriminatory factor

Ranking the synthesizability of hypothetical zeolites with the sorting hat

Zeolites are nanoporous alumino-silicate frameworks widely used as catalysts and adsorbents. Even though millions of siliceous networks can be generated by computer-aided searches, no new hypothetical framework has yet been synthesized. The needle-in-a-haystack problem of finding promising candidates among large databases of predicted structures has intrigued materials scientists for decades; yet, most work to date on the zeolite problem has been limited to intuitive structural descriptors. Here, we tackle this problem through a rigorous data science scheme—the “Zeolite Sorting Hat”—that exploits interatomic correlations to discriminate between real and hypothetical zeolites and to partition real zeolites into compositional classes that guide synthetic strategies for a given hypothetical framework. We find that, regardless of the structural descriptor used by the Zeolite Sorting Hat, there remain hypothetical frameworks that are incorrectly classified as real ones, suggesting that they might be good candidates for synthesis. We seek to minimize the number of such misclassified frameworks by using as complete a structural descriptor as possible, thus focusing on truly viable synthetic targets, while discovering structural features that distinguish real and hypothetical frameworks as an output of the Zeolite Sorting Hat. Further ranking of the candidates can be achieved based on thermodynamic stability and/or their suitability for the desired applications. Based on this workflow, we propose three hypothetical frameworks differing in their molar volume range as the top targets for synthesis, each with a composition suggested by the Zeolite Sorting Hat. Finally, we analyze the behavior of the Zeolite Sorting Hat with a hierarchy of structural descriptors including intuitive descriptors reported in previous studies, finding that intuitive descriptors produce significantly more misclassified hypothetical frameworks, and that more rigorous interatomic correlations point to second-neighbor Si–O distances around 3.2–3.4 Å as the key discriminatory factor

Atomic Motif Recognition in (Bio)Polymers: Benchmarks From the Protein Data Bank

Rationalizing the structure and structure–property relations for complex materials such as polymers or biomolecules relies heavily on the identification of local atomic motifs, e.g., hydrogen bonds and secondary structure patterns, that are seen as building blocks of more complex supramolecular and mesoscopic structures. Over the past few decades, several automated procedures have been developed to identify these motifs in proteins given the atomic structure. Being based on a very precise understanding of the specific interactions, these heuristic criteria formulate the question in a way that implies the answer, by defining a list of motifs based on those that are known to be naturally occurring. This makes them less likely to identify unexpected phenomena, such as the occurrence of recurrent motifs in disordered segments of proteins, and less suitable to be applied to different polymers whose structure is not driven by hydrogen bonds, or even to polypeptides when appearing in unusual, non-biological conditions. Here we discuss how unsupervised machine learning schemes can be used to recognize patterns based exclusively on the frequency with which different motifs occur, taking high-resolution structures from the Protein Data Bank as benchmarks. We first discuss the application of a density-based motif recognition scheme in combination with traditional representations of protein structure (namely, interatomic distances and backbone dihedrals). Then, we proceed one step further toward an entirely unbiased scheme by using as input a structural representation based on the atomic density and by employing supervised classification to objectively assess the role played by the representation in determining the nature of atomic-scale patterns

Improving sample and feature selection with principal covariates regression

Funder: Trinity College, University of Cambridge; doi: http://dx.doi.org/10.13039/501100000727Abstract: Selecting the most relevant features and samples out of a large set of candidates is a task that occurs very often in the context of automated data analysis, where it improves the computational performance and often the transferability of a model. Here we focus on two popular subselection schemes applied to this end: CUR decomposition, derived from a low-rank approximation of the feature matrix, and farthest point sampling (FPS), which relies on the iterative identification of the most diverse samples and discriminating features. We modify these unsupervised approaches, incorporating a supervised component following the same spirit as the principal covariates (PCov) regression method. We show how this results in selections that perform better in supervised tasks, demonstrating with models of increasing complexity, from ridge regression to kernel ridge regression and finally feed-forward neural networks. We also present adjustments to minimise the impact of any subselection when performing unsupervised tasks. We demonstrate the significant improvements associated with PCov-CUR and PCov-FPS selections for applications to chemistry and materials science, typically reducing by a factor of two the number of features and samples required to achieve a given level of regression accuracy

Improving sample and feature selection with principal covariates regression

Funder: Trinity College, University of Cambridge; doi: http://dx.doi.org/10.13039/501100000727Abstract: Selecting the most relevant features and samples out of a large set of candidates is a task that occurs very often in the context of automated data analysis, where it improves the computational performance and often the transferability of a model. Here we focus on two popular subselection schemes applied to this end: CUR decomposition, derived from a low-rank approximation of the feature matrix, and farthest point sampling (FPS), which relies on the iterative identification of the most diverse samples and discriminating features. We modify these unsupervised approaches, incorporating a supervised component following the same spirit as the principal covariates (PCov) regression method. We show how this results in selections that perform better in supervised tasks, demonstrating with models of increasing complexity, from ridge regression to kernel ridge regression and finally feed-forward neural networks. We also present adjustments to minimise the impact of any subselection when performing unsupervised tasks. We demonstrate the significant improvements associated with PCov-CUR and PCov-FPS selections for applications to chemistry and materials science, typically reducing by a factor of two the number of features and samples required to achieve a given level of regression accuracy

Evaluation of Transition Metal Dichalcogenide Encapsulation to Improve Copper Interconnects: An ab Initio Study

Two-dimensional materials, such as graphene, hexagonal boron nitride, and transition metal dichalcogenides (TMDs) have gained much interest for their applications in nanoscale devices, especially nanoelectronics. In this regard, TMDs are particularly interesting, as this class of materials contains several stable compounds with a wide variety of properties, existing as semiconductors with tunable band gaps, metals, superconductors, and/or ferromagnets. However, in order to incorporate TMDs into devices, a fundamental understanding of the interfaces TMDs form with other materials, including metals, is required. This is especially true for Group IV and V dichalcogenides, which have received less attention than the Group VI dichalcogenides to date. The goal of this work is to identify trends in properties most relevant to TMDencapsulated copper nanowires, which are envisioned as a candidate for ultra-scaled interconnect technology. In the spirit of this goal, the stability, adhesion, and electronic properties of interfaces between Cu(111) surfaces and TMDs of the form MX2, where M is a transition metal and X a chalcogen, are examined with ab initio computational methods. Select Group IV and V TMDs that are metals or small bandgap semiconductors (M = Ti, V, Nb) are studied alongside the Group VI semiconductors (M = Mo, W); all three chalcogenides (X = S, Se, Te) are studied for each transition metal. In addition, the ability of these TMDs to serve as diffusion barriers to copper and oxygen is also evaluated. Ground state TMDs forming an interface with a Cu(111) surface are predicted to be stable, with binding energies per unit area three to five times higher than hexagonal boron nitride or graphene. The interactions between the Cu and TMD are dominated by van der Waals forces, but also have some covalent or ionic character. As a TMD becomes adsorbed on the Cu surface, charge rearrangement occurs at the interface resulting in a net dipole and a modulation of the work function at the Cu surface that depends strongly on the chemistry of the TMD: increasing by up to ≈ 1 eV by encapsulating with a Group IV or V TMD and decreasing by up to ≈ 1 eV by encapsulating with a Group VI TMD. However, the Schottky barrier at the interface does not depend strongly on chemistry. All semiconductor TMDs studied form a Schottky barrier of roughly 0.5 eV with the Cu surface. No Schottky barrier is formed for the metallic TMDs. Many Group IV, V, and VI disulfides are also predicted to serve as adequate Cu and O diffusion barriers, with energy barriers of 2.5–5.5 eV. However, all TMDs studied are predicted to perform more poorly as diffusion barriers than h-BN and graphene, which exhibit energy barriers to Cu and O diffusion of 4–11 eV

Structure-property maps with Kernel principal covariates regression

Data analyses based on linear methods constitute the simplest, most robust, and transparent approaches to the automatic processing of large amounts of data for building supervised or unsupervised machine learning models. Principal covariates regression (PCovR) is an underappreciated method that interpolates between principal component analysis and linear regression and can be used conveniently to reveal structure-property relations in terms of simple-to-interpret, low-dimensional maps. Here we provide a pedagogic overview of these data analysis schemes, including the use of the kernel trick to introduce an element of non-linearity while maintaining most of the convenience and the simplicity of linear approaches. We then introduce a kernelized version of PCovR and a sparsified extension, and demonstrate the performance of this approach in revealing and predicting structure-property relations in chemistry and materials science, showing a variety of examples including elemental carbon, porous silicate frameworks, organic molecules, amino acid conformers, and molecular materials