Autonomous Phase Mapping of Gold Nanoparticles Synthesis with Differentiable Models of Spectral Shape

Autonomous experimentation, or self-driving labs as they are popularly known, is a novel way of experimental planning and scheduling to systematically integrate automated material synthesis, characterization, and data analysis for accelerated materials design and discovery. This paper presents an autonomous experimentation workflow to conduct an iterative, on-demand synthesis, and structural characterization of colloidal gold nanoparticles to map composition information to nano-scale structure in a closed loop. We introduce a unified experimental planning framework based on differentiable models of the shape of a spectrum to solve the two categories of problems tackled using autonomous experimentation: a) quantitatively mapping compositional parameters to regions with particular behavior in structure or property i.e. phase mapping; b) inverse design of materials with a target structure or function i.e. material retrosynthesis. Using functional data analysis, we describe a data-driven computational model to map correlations between the compositions (i.e., synthesis or processing conditions) to the shape of a characterization curve (e.g.: UV-Vis spectroscopy). By integrating generative pre-training, active learning, and high-throughput experimentation, we train a surrogate model capable of predicting characterization curves across compositional spaces. The model-based approach to experimental planning provides convergence criteria for terminating iterative experimentation by measuring the model's test accuracy. We demonstrate the utility of trained differentiable models in generating phase maps of a seed-mediated growth of colloidal gold nanoparticles for extracting design rules, secondary interaction effects (between synthesis components and their impact on nanoparticle structure), and efficient navigation of different gold nanoparticle morphologies space. We then describe an application of the differentiable models to colloidal gold nanoparticle retrosynthesis using a gradient-based optimization to showcase the construction of a self-driving lab doubly capable of structure optimization and phase map generation.