Browsing by Author "Brinson, L Catherine"

A polymer nanocomposite (PNC) is a composite material consisting of a polymer matrix and stiff fillers with at least one dimension smaller than 100 nm. With the addition of a small amount of filler to the polymer matrix, PNC demonstrates large reinforcement of mechanical, viscoelastic, dielectric, thermal, optical, and other physiochemical properties as compared to pure polymer or pure fillers acting alone. PNCs have thus attracted significant amounts of research interest over recent years. To accelerate materials design, we need findable, accessible, interoperable, and reusable (FAIR) data resources to provide sufficient data for data-driven approaches to replace the traditional trial-and-error style of exploration in a lab. With the goal to build a FAIR data resource for the PNC community, we built NanoMine in 2016, which later evolves into MaterialsMine with the extension of MetaMine in the metamaterial domain. To be FAIR, we need a clear and extensible data representation to enable the interoperable knowledge exchange. We thus designed the NanoMine XML schema. With the data framework and data representation in place, we still need tools and a user-friendly interface for data curation. This dissertation describes in detail the tools and data interfaces we developed to ensure a smooth data curation pathway for NanoMine/MaterialsMine. To reduce and prevent curation errors and thus improve data quality, we need data validation mechanisms. To address the need, we discuss the validation mechanisms embedded both during and after the curation. On many occasions, even without human-caused curation errors, the data resource cannot perform to its full capacity due to data inconsistencies. For example, the inconsistency of polymer indexing caused by the lack of uniformity in expression of polymer names and the inconsistent use of mass fraction and volume fraction in specifying the composite composition. To address the need for data standardization, tools developed to bypass manual curation, the mass fraction – volume fraction conversion agent, and ChemProps, a RESTful API-enabled multi-algorithm-based polymer/filler name mapping methodology, are discussed in detail in this dissertation. To create truly powerful and transformative materials design paradigms and towards a sustainable future for MaterialsMine, we need to harness the power of AI to efficiently extract a significant set of data from the published, archival literature. Natural Language Processing (NLP) offers an opportunity to make this data accessible and readily reusable by humans and machines. The first step is to generate a sample list where curators can easily find the number of samples, their compositions, and properties reported in the paper. The task is handled in a pretraining-finetuning fashion. Downstream tasks include Named Entity Recognition (NER) to detect sample code, sample composition, property, and group reference to samples in the captions, and Relation Extraction (RE) which predicts the relations between pairs of detected named entities. In this dissertation, a detailed discussion of how the two corpora for pretraining and finetuning are constructed is provided. A T5-base model pretrained on the caption-mention corpus and finetuned for the NER and RE tasks is proposed. We evaluated it along with an array of BERT-based models and seq2seq models for potential applications in semi-automated curation pipeline for MaterialsMine.

Polymer-based nanocomposites (PNCs) are materials consisting of nanoparticles and polymers. The enhancement of the mechanical, thermal, electrical, and other properties of the PNCs brought by the nanoparticles makes it a useful material in various applications. The huge amount of surface area brought by the nanoparticles interacts with polymer chains to form an interphase, which drives the property change. The presence of the interphase adds to the complexity of the processing-structure-property (p-s-p) relationship of PNCs that guide material design. As conventional trial-and-error approaches in the laboratory prove time-consuming and resource-intensive, an alternative approach is to utilize data-driven methods for PNC design. However, data-driven material design suffers from data scarcity issues.To tackle the data scarcity issue on a cross-community level, there has been a growing emphasis on the adoption of the Findable, Accessible, Interoperable, and Reusable (FAIR) data principles. In 2016, the NanoMine data resource, later evolved into MaterialsMine with the inclusion of metamaterials, and its accompanying schema were introduced to handle PNC data, offering a user-friendly and FAIR approach to manage these complex PNC data, facilitating data-driven material design. To make this schema more accessible to curators and material scientists, an Excel-based customizable master template was designed for experimental data. In parallel to the long-lasting cumulative effort of curating experimental PNC data from literature, simulation data can be generated and curated much faster due to its computational nature. Thus, the NanoMine schema and template for experimental data were expanded to support popular simulation methods like Finite Element Analysis (FEA) with high utilization of existing fields, demonstrating the flexibility of the schema/template approach. With the schema and template in place for NanoMine to host FEA data, an efficient and highly automated end-to-end pipeline was developed for FEA data generation. A data management system was implemented to capture the FEA data and the associated metadata, which are critical for the data to be FAIR. A resource management system was implemented to address the system restrictions. Starting from microstructure generation, all the way to packaging the data into a curation-ready format, the pipeline lives in a standardized Jupyter notebook for easier usage and better bookkeeping. FEA simulations, while faster than laboratory experiments, remain resource-intensive and are often constrained by commercial software licenses. Thus, the last part of this research aims to develop an efficient, reliable, and lightweight surrogate model for FEA simulation of the viscoelastic response of PNCs, named ViscoNet, with machine learning (ML). Drawing inspiration from NLP models like GPT, ViscoNet utilizes pre-training and fine-tuning techniques to reproduce FEA simulations, achieving a mean absolute percentage error (MAPE) of < 5% for rubbery modulus, < 1% for glassy modulus, and 1.22% for tan delta peak, with as few as 500 FEA simulation data for fine-tuning. ViscoNet demonstrates impressive generalization capabilities from thermoplastics to thermosets. ViscoNet enables the generation of over 20k VE responses in under 2 minutes, making it a versatile tool for high-throughput PNC design and optimization. Notably, ViscoNet does not require a GPU for training, allowing anyone with Internet access to download 500 FEA data from NanoMine and fine-tune ViscoNet on a personal laptop, thereby making data-driven materials design accessible to a broader scientific community.