Using simulation to accelerate autonomous experimentation: A case study using mechanics

Autonomous experimentation (AE) accelerates research by combining automation and machine learning to perform experiments intelligently and rapidly in a sequential fashion. While AE systems are most needed to study properties that cannot be predicted analytically or computationally, even imperfect predictions can in principle be useful. Here, we investigate whether imperfect data from simulation can accelerate AE using a case study on the mechanics of additively manufactured structures. Initially, we study resilience, a property that is well-predicted by finite element analysis (FEA), and find that FEA can be used to build a Bayesian prior and experimental data can be integrated using discrepancy modeling to reduce the number of needed experiments ten-fold. Next, we study toughness, a property not well-predicted by FEA and find that FEA can still improve learning by transforming experimental data and guiding experiment selection. These results highlight multiple ways that simulation can improve AE through transfer learning. [Display omitted] •Simulation and autonomous experimentation were combined to accelerate research•Resilience, which was accurately simulated, was learned in 10 x fewer experiments•Simulating related properties, i.e. yield force, accelerated learning toughness•Simulation was introduced to an experimental learning loop using transfer learning Mechanical Property; Computational Method in Materials Science; Simulation in Materials Science