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High-throughput computational materials discovery has promised significantacceleration of the design and discovery of new materials for many years.Despite a surge in interest and activity, the constraints imposed bylarge-scale computational resources present a significant bottleneck.Furthermore, examples of large-scale computational discovery carried throughexperimental validation remain scarce, especially for materials with productapplicability. Here we demonstrate how this vision became reality by firstcombining state-of-the-art artificial intelligence (AI) models and traditionalphysics-based models on cloud high-performance computing (HPC) resources toquickly navigate through more than 32 million candidates and predict aroundhalf a million potentially stable materials. By focusing on solid-stateelectrolytes for battery applications, our discovery pipeline furtheridentified 18 promising candidates with new compositions and rediscovered adecade's worth of collective knowledge in the field as a byproduct. Byemploying around one thousand virtual machines (VMs) in the cloud, this processtook less than 80 hours. We then synthesized and experimentally characterizedthe structures and conductivities of our top candidates, theNa$_x$Li$_{3-x}$YCl$_6$ ($0 < x < 3$) series, demonstrating the potential ofthese compounds to serve as solid electrolytes. Additional candidate materialsthat are currently under experimental investigation could offer more examplesof the computational discovery of new phases of Li- and Na-conducting solidelectrolytes. We believe that this unprecedented approach of synergisticallyintegrating AI models and cloud HPC not only accelerates materials discoverybut also showcases the potency of AI-guided experimentation in unlockingtransformative scientific breakthroughs with real-world applications.

Accelerating computational materials discovery with artificial  intelligence and cloud high-performance computing: from large-scale screening  to experimental validation