Deep learning-enabled property prediction and inverse design of DLP 3D printed nanocomposites

Digital Light Processing (DLP) 3D printing of nanocomposites holds great potential for manufacturing high-performance functional parts. However, balancing printing precision and mechanical properties, such as modulus and toughness, remains a significant challenge due to the complex nonlinear relationships between material composition and printing outcomes. As traditional trial-and-error optimisation is time-consuming and inefficient, here we propose a combined forward-inverse machine learning framework to accelerate the material design process. First, a MLP-based forward model is built to predict performance metrics from material formulas. Subsequently, a novel Gradient Refinement Loop (GRL) is developed for inverse design. By employing a fixed forward ensemble as the performance evaluator, this module iteratively optimises the input formulation through gradient descent to achieve the predefined target performance. The forward surrogate accurately maps the complex design space, achieving high predictive accuracy for printing precision (R² = 0.94), modulus (R² = 0.943), and toughness (R² = 0.90). Empowered by this surrogate, the gradient-refined inverse module identifies precise target formulations with exceptional fidelity, achieving R² values of 0.95, 0.96, and 0.915 for the respective metrics. The results demonstrate that the proposed data-driven approach effectively identifies optimised recipes that simultaneously satisfy stringent requirements for precision and mechanical strength.