DMREF/Collaborative Research: Active Learning-Based Material Discovery for 3D Printed Solids with Locally-Tunable Electrical and Mechanical Properties

Georgia Tech Research Corporation, Atlanta GA

Investigators

Abstract

This Designing Materials to Revolutionize and Engineer our Future (DMREF) project will establish a multi-disciplinary active learning method to discover new materials chemistries for additive manufacturing (AM; or 3D printing) wherein the electrical and mechanical properties can be locally controlled. This is a multi-disciplinary approach with an integration of advanced synthesis, characterization, simulation, and data science protocols. AM has seen exponential growth in recent years. One emerging area is to use AM for functional devices. However, the current AM techniques face challenges in fabricating these devices due to the lack of multi-material capability. Digital light processing (DLP) 3D printing is a rapidly developing AM technique due to its advantage of high speed and high resolution. The research will develop advanced data-driven approaches which utilize both experimental and computational training data to address the multiple design objectives and guide successive rounds of experiments. These approaches will be used to discover new resin formulations for DLP 3D printing where the local material properties can be controlled from soft to stiff and conductive to non-conductive. The active learning method will greatly expedite the development of new materials for AM. The research will have significant societal and economic impacts serving to maintain and enhance the US leadership position in AM. The research will be disseminated to undergraduate, graduate, and high school students and involve them in research. The research will involve students from underrepresented groups and promoting divergence, equity, and inclusion in the STEM field. Discovery of new polymers for AM has been hindered by the existence of a large number of ingredient monomers, large property differences among printed polymers, and the lack of an efficient approach to rapidly select these monomers at proper ratios to make polymers with properties that can meet the application needs. Preliminary work has demonstrated the feasibility to use an active learning approach to discover new resins with different monomer compositions for targeted mechanical properties. This work has shown that it is possible to use the copolymer ink design to print a monolithic part with locally variable mechanical properties and conductivity. The research will first establish a suite of high-throughput methods for polymer property evaluation by experiments and simulations. These include rapidly synthesizing polymers composed of different monomers at different ratios, characterizing their mechanical and electrical properties, and predicting these properties using molecular dynamics simulations with classical and machine learning based force fields trained on density functional theory calculation data. Assisted by these high-throughput methods, the research will establish an advanced multi-task active learning model that uses data from molecular dynamics simulations and from a limited number of experiments to predict polymer electrical and mechanical properties. A combined iterative approach between experiments and simulations will provide insight into effects of polymer composition and structure on polymer conductivity, mechanical properties, and property gradients. Finally, the active learning model will be used to guide the selection of monomers to design and fabricate 3D functional devices. This project is supported by the Division of Civil, Mechanical and Manufacturing Innovation (CMMI) of the Directorate for Engineering (ENG) and the Division of Materials Research (DMR) of the Directorate for Mathematical and Physical Sciences (MPS). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.