Calculating Pairwise Similarity of Polymer Ensembles via Earth Mover's Distance.

Synthetic polymers, in contrast to small molecules and deterministic biomacromolecules, are typically ensembles composed of polymer chains with varying numbers, lengths, sequences, chemistry, and topologies. While numerous approaches exist for measuring pairwise similarity among small molecules and sequence-defined biomacromolecules, accurately determining the pairwise similarity between two polymer ensembles remains challenging. This work proposes the earth mover's distance (EMD) metric to calculate the pairwise similarity score between two polymer ensembles. EMD offers a greater resolution of chemical differences between polymer ensembles than the averaging method and provides a quantitative numeric value representing the pairwise similarity between polymer ensembles in alignment with chemical intuition. The EMD approach for assessing polymer similarity enhances the development of accurate chemical search algorithms within polymer databases and can improve machine learning techniques for polymer design, optimization, and property prediction.

Machine-Learning-Assisted Understanding of Polymer Nanocomposites Composition-Property Relationship: A Case Study of NanoMine Database.

The NanoMine database, one of two nodes in the MaterialsMine database, is a new materials data resource that collects annotated data on polymer nanocomposites (PNCs). This work showcases the potential of NanoMine and other materials data resources to assist fundamental materials understanding and therefore rational materials design. This specific case study is built around studying the relationship between the change in the glass transition temperature Tg (ΔTg) and key descriptors of the nanofillers and the polymer matrix in PNCs. We sifted through data from over 2000 experimental samples curated into NanoMine, trained a decision tree classifier to predict the sign of PNC ΔTg, and built a multiple power regression metamodel to predict ΔTg. The successful model used key descriptors including composition, nanoparticle volume fraction, and interfacial surface energy. The results demonstrate the power of using aggregated materials data to gain insight and predictive capability. Further analysis points to the importance of additional analysis of parameters from processing methodologies and continuously adding curated data sets to increase the sample pool size.

Community Resource for Innovation in Polymer Technology (CRIPT): A Scalable Polymer Material Data Structure.

The Community Resource for Innovation in Polymer Technology (CRIPT) data model is designed to address the high complexity in defining a polymer structure and the intricacies involved with characterizing material properties.

The materials tetrahedron has a "digital twin".

Abstract: For over three decades, the materials tetrahedron has captured the essence of materials science and engineering with its interdependent elements of processing, structure, properties, and performance. As modern computational and statistical techniques usher in a new paradigm of data-intensive scientific research and discovery, the rate at which the field of materials science and engineering capitalizes on these advances hinges on collaboration between numerous stakeholders. Here, we provide a contemporary extension to the classic materials tetrahedron with a dual framework-adapted from the concept of a "digital twin"-which offers a nexus joining materials science and information science. We believe this high-level framework, the materials-information twin tetrahedra (MITT), will provide stakeholders with a platform to contextualize, translate, and direct efforts in the pursuit of propelling materials science and technology forward. Impact statement: This article provides a contemporary reimagination of the classic materials tetrahedron by augmenting it with parallel notions from information science. Since the materials tetrahedron (processing, structure, properties, performance) made its first debut, advances in computational and informational tools have transformed the landscape and outlook of materials research and development. Drawing inspiration from the notion of a digital twin, the materials-information twin tetrahedra (MITT) framework captures a holistic perspective of materials science and engineering in the presence of modern digital tools and infrastructures. This high-level framework incorporates sustainability and FAIR data principles (Findable, Accessible, Interoperable, Reusable)-factors that recognize how systems impact and interact with other systems-in addition to the data and information flows that play a pivotal role in knowledge generation. The goal of the MITT framework is to give stakeholders from academia, industry, and government a communication tool for focusing efforts around the design, development, and deployment of materials in the years ahead.

FAIR and Interactive Data Graphics from a Scientific Knowledge Graph.

Graph databases capture richly linked domain knowledge by integrating heterogeneous data and metadata into a unified representation. Here, we present the use of bespoke, interactive data graphics (bar charts, scatter plots, etc.) for visual exploration of a knowledge graph. By modeling a chart as a set of metadata that describes semantic context (SPARQL query) separately from visual context (Vega-Lite specification), we leverage the high-level, declarative nature of the SPARQL and Vega-Lite grammars to concisely specify web-based, interactive data graphics synchronized to a knowledge graph. Resources with dereferenceable URIs (uniform resource identifiers) can employ the hyperlink encoding channel or image marks in Vega-Lite to amplify the information content of a given data graphic, and published charts populate a browsable gallery of the database. We discuss design considerations that arise in relation to portability, persistence, and performance. Altogether, this pairing of SPARQL and Vega-Lite-demonstrated here in the domain of polymer nanocomposite materials science-offers an extensible approach to FAIR (findable, accessible, interoperable, reusable) scientific data visualization within a knowledge graph framework.

Size effects in plasma-enhanced nano-transfer adhesion.

Plasma bonding and layer-by-layer transfer molding have co-existed for decades, and here we offer a combination of the two that drives both techniques to the nanoscale. Using fluorinated elastomeric stamps, lines of plasma-treated poly(dimethylsiloxane) (PDMS) were stacked into multi-layer woodpile structures via transfer molding, and we observe a pronounced size effect wherein nanoscale lines (≤280 nm period) require ultra-low plasma dose (<20 J) and fail to bond at the much higher range of plasma dose (600 J to 900 J) recommended in the PDMS plasma bonding literature. The size effect appears to be related to the thickness of the oxide film that develops on the PDMS surface during treatment, and we employ an empirical relationship, , to estimate the thickness of this film in the low plasma dose (<100 J) regime. The empirical relationship shows good agreement with existing studies on plasma-treated PDMS oxide film thickness, and the transition between successful transfer and delamination coincides well with a critical value of the oxide thickness relative to the thickness of the transferred layer. Through peel testing, we identified a transition in failure mode of flat plasma-bonded PDMS consistent with the optimal plasma dose in previous literature but otherwise observed strong, irreversible adhesion even at ultra-low plasma dose. By demonstrating the importance of low plasma dose for plasma-enhanced nano-transfer adhesion, these results advance our understanding of irreversible adhesion of soft materials at the nanoscale and open up new opportunities within the relatively unstudied ultra-low dose plasma treatment regime.

Wetting Regimes for Residual-Layer-Free Transfer Molding at Micro- and Nanoscales.

Transfer molding offers a low-cost approach to large-area fabrication of isolated structures in a variety of materials when recessed features of the open-faced mold are filled without leaving a residual layer on the plateaus of the mold. Considering both macroscale dewetting and microscale capillary flow, a proposed map of wetting regimes for blade meniscus coating provides a guide for achieving discontinuous dewetting at maximum throughput. Dependence of meniscus morphology on the azimuthal orientation of the stamp provides insight into the dominant mechanisms for discontinuous dewetting of one-dimensional (1-D) patterns. Critical meniscus velocity is measured and residual-layer-free filling is demonstrated for 1-D patterned soft molds (stamps) with periods ranging from 140 nm to 6 µm. Transfer of isolated lines, and multilayer woodpile structures were achieved through plasma bonding. These results are relevant to other roll-to-roll compatible processes for scalable production of high-resolution structures across large areas.