Data-Driven Study of Polymer-Based Nanocomposites (PNC) – FAIR Online Data Resource Development and ML-Facilitated Material Design

Abstract

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.