Abstract Data-driven material innovation has the potential to revolutionize the traditional Edisonian process and significantly shorten development cycles. However, the scarcity of data in materials science and the poor interpretability of machine learning pose serious obstacles to the adoption of this new paradigm. Here, we propose a pipeline that integrates data production, virtual screening, and theoretical innovation using high-throughput all-atom molecular dynamics (MD) as a data flywheel. Using this pipeline…