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The accelerated exploration of the materials space in order to identifyconfigurations with optimal properties is an ongoing challenge. Currentparadigms are typically centered around the idea of performing this explorationthrough high-throughput experimentation/computation. Such approaches, however,do not account fo the always present constraints in resources available.Recently, this problem has been addressed by framing materials discovery as anoptimal experiment design. This work augments earlier efforts by puttingforward a framework that efficiently explores the materials design space notonly accounting for resource constraints but also incorporating the notion ofmodel uncertainty. The resulting approach combines Bayesian Model Averagingwithin Bayesian Optimization in order to realize a system capable ofautonomously and adaptively learning not only the most promising regions in thematerials space but also the models that most efficiently guide suchexploration. The framework is demonstrated by efficiently exploring the MAXternary carbide/nitride space through Density Functional Theory (DFT)calculations.

Autonomous Efficient Experiment Design for Materials Discovery with Bayesian Model Averaging