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Active learning (AL) can drastically accelerate materials discovery; itspower has been shown in various classes of materials and target properties.Prior efforts have used machine learning models for the optimal selection ofphysical experiments or physics-based simulations. However, the latter effortshave been mostly limited to the use of electronic structure calculations andproperties that can be obtained at the unit cell level and with negligiblenoise. We couple AL with molecular dynamics simulations to identify multipleprincipal component alloys (MPCAs) with high melting temperatures. Building oncloud computing services through nanoHUB, we present a fully autonomousworkflow for the efficient exploration of the high dimensional compositionalspace of MPCAs. We characterize how uncertainties arising from the stochasticnature of the simulations and the acquisition functions used to selectsimulations affect the convergence of the approach. Interestingly, we find thatrelatively short simulations with significant uncertainties can be used toefficiently find the desired alloys as the random forest models used for ALaverage out fluctuations.

Active learning and molecular dynamics simulations to find high melting temperature alloys