Novel high voltage polymer insulators using computational and data-driven techniques.

One of the key bottlenecks in the development of high voltage electrical systems is the identification of suitable insulating materials capable of supporting high voltages. Under high voltage scenarios, conventional polymer based insulators, which are one of the popular choices of insulators, suffer from the drawback of space charge accumulation, which leads to degradation in desirable electronic properties and facilitates dielectric breakdown. In this work, we aid the development of novel polymers for high voltage insulation applications by enabling the rapid prediction of properties that are correlated with dielectric breakdown, i.e.,the bandgap (Egap) of the polymer and electron injection barrier (Φe) at the electrode-insulator interface. To accomplish this, density functional theory based methods are used to develop large, chemically diverse datasets of Φe and Egap. The deviation of the computed properties from experimental observations is addressed using a statistical technique called Bayesian calibration. Furthermore, to enable rapid estimation of these properties for a large set of polymers, machine learning models are developed using the created dataset. These models are further used to predict Egap and Φe for a set of 13k previously known polymers. Polymers with high values of these properties are selected as potential high voltage insulators and are recommended for synthesis. Finally, the models developed here are deployed at www.polymergenome.org to enable the community use.

Machine-Guided Discovery of Acrylate Photopolymer Compositions.

Additive manufacturing (AM) can be advanced by the diverse characteristics offered by thermoplastic and thermoset polymers and the further benefits of copolymerization. However, the availability of suitable polymeric materials for AM is limited and may not always be ideal for specific applications. Additionally, the extensive number of potential monomers and their combinations make experimental determination of resin compositions extremely time-consuming and costly. To overcome these challenges, we develop an active learning (AL) approach to effectively choose compositions in a ternary monomer space ranging from rigid to elastomeric. Our AL algorithm dynamically suggests monomer composition ratios for the subsequent round of testing, allowing us to efficiently build a robust machine learning (ML) model capable of predicting polymer properties, including Young's modulus, peak stress, ultimate strain, and Shore A hardness based on composition while minimizing the number of experiments. As a demonstration of the effectiveness of our approach, we use the ML model to drive material selection for a specific property, namely, Young's modulus. The results indicate that the ML model can be used to select material compositions within at least 10% of a targeted value of Young's modulus. We then use the materials designed by the ML model to 3D print a multimaterial "hand" with soft "skin" and rigid "bones". This work presents a promising tool for enabling informed AM material selection tailored to user specifications and accelerating material discovery using a limited monomer space.

Statistical approach to quantifying the elastic deformation of nanomaterials.

Quantifying the mechanical properties of nanomaterials is challenged by its small size, difficulty of manipulation, lack of reliable measurement techniques, and grossly varying measurement conditions and environment. A recently proposed approach is to estimate the elastic modulus from a force-deflection physical model based on the continuous bridged-deformation of a nanobelt/nanowire using an atomic force microscope tip under different contact forces. However, the nanobelt may have some initial bending, surface roughness and imperfect physical boundary conditions during measurement, leading to large systematic errors and uncertainty in data quantification. In this article, a statistical modeling technique, sequential profile adjustment by regression (SPAR), is proposed to account for and eliminate the various experimental errors and artifacts. SPAR can automatically detect and remove the systematic errors and therefore gives more precise estimation of the elastic modulus. This research presents an innovative approach that can potentially have a broad impact in quantitative nanomechanics and nanoelectronics.