  High-throughput computational materials discovery has promised significant
acceleration of the design and discovery of new materials for many years.
Despite a surge in interest and activity, the constraints imposed by
large-scale computational resources present a significant bottleneck.
Furthermore, examples of large-scale computational discovery carried through
experimental validation remain scarce, especially for materials with product
applicability. Here we demonstrate how this vision became reality by first
combining state-of-the-art artificial intelligence (AI) models and traditional
physics-based models on cloud high-performance computing (HPC) resources to
quickly navigate through more than 32 million candidates and predict around
half a million potentially stable materials. By focusing on solid-state
electrolytes for battery applications, our discovery pipeline further
identified 18 promising candidates with new compositions and rediscovered a
decade's worth of collective knowledge in the field as a byproduct. By
employing around one thousand virtual machines (VMs) in the cloud, this process
took less than 80 hours. We then synthesized and experimentally characterized
the structures and conductivities of our top candidates, the
Na$_x$Li$_{3-x}$YCl$_6$ ($0 < x < 3$) series, demonstrating the potential of
these compounds to serve as solid electrolytes. Additional candidate materials
that are currently under experimental investigation could offer more examples
of the computational discovery of new phases of Li- and Na-conducting solid
electrolytes. We believe that this unprecedented approach of synergistically
integrating AI models and cloud HPC not only accelerates materials discovery
but also showcases the potency of AI-guided experimentation in unlocking
transformative scientific breakthroughs with real-world applications.
