Machine Intelligence Accelerated Design of Conductive MXene Aerogels with Programmable Properties

Abstract Designing ultralight conductive aerogels with tailored electrical and mechanical properties is critical for various applications. Conventional approaches rely on iterative optimization experiments, which are time-consuming when exploring a vast parameter space. Herein, an integrated workflow is developed to combine collaborative robotics with machine learning to accelerate the design of conductive aerogels with programmable electrical and mechanical properties. First, an automated pipetting robot is operated to prepare 264 mixtures using four building blocks at different ratios/loadings (including Ti3C2Tx MXene, cellulose nanofibers, gelatin, glutaraldehyde). After freeze-drying, the structural integrity of conductive aerogels is evaluated to train a support vector machine classifier. Through 8 active learning cycles with data augmentation, 162 kinds of conductive aerogels are fabricated/characterized via robotics-automated platforms, enabling the construction of an artificial neural network prediction model. The prediction model can conduct two-way design tasks: (1) predicting the physicochemical properties of conductive aerogels from fabrication parameters and (2) automating the inverse design of conductive aerogels for specific property requirements. The combined use of model interpretation and finite element simulations validates a pronounced correlation between aerogel density and its compressive strength. The model-suggested conductive aerogels with high electrical conductivity, customized compression resilience, and pressure insensitivity allow for compression-stable Joule heating for wearable thermal management. The fusion of robotics-accelerated experimentation, machine intelligence, and simulation tools expedites the tailored design and scalable production of conductive aerogels.