A framework for fully autonomous design of materials via multiobjective optimization and active learning: challenges and next steps

Data-driven automated laboratories, also called self-driving laboratories, can significantly accelerate molecular synthesis and materials discovery. A key technical challenge of fully autonomous and artificial intelligenceassisted laboratory design is to effectively collect and utilize data from multiple complex processes in order to inform future experimentation. One long-standing template for utilizing experimental and simulation data is the multiresponse surface methodology (RSM) [13], whereby initial data sets are gathered through design of experiments and then statistical models are built for each quantity of interest, analyzed, and hypothesis tested iteratively. In modern scientific and engineering settings, several common paradigms could be considered as specific implementations of this discovery framework, the most common of which are active learning and modelbased optimization techniques such as Bayesian optimization. To account for multiple competing criteria, we utilize an active learning framework based in multiobjective optimization, which utilizes surrogates (such as Gaussian processes), optimization solvers, and multicriteria data acquisition in a closed feedback loop.