Machine Learning-Assisted Screening of High-Entropy Sub-1 nm Nanowires for Ultrasound-Augmented Pancatalytic Tumor Therapy

The rational design of catalytic nanomaterials, particularly high-entropy alloy (HEA) nanomaterials with their unique structures, is crucial for advancing pancatalytic therapy and holds promise for catalytic biomedical applications. Nevertheless, conventional trial-and-error approaches to HEA development persist in being inefficient and resource-demanding, underscoring the critical need for data-driven strategies to accelerate the design of next-generation catalytic platforms. Herein, we report a machine learning (ML)-assisted framework for the rational design of subnanometer HEAs with enhanced multicatalytic activities for the treatment of triple-negative breast cancer. Using an extensively curated dataset of nanozyme compositions and activities, ML algorithms identified key metal elements with the highest contribution to catalytic performance. Guided by these insights, a distinct PtFeMoCoNiRu HEA subnanowire (HESNW) was synthesized, in which the subnanostructure confers maximized active-site exposure and superior atomic utilization, enabling exceptional reactive oxygen species (ROS) generation. Ultrasound (US), characterized by its noninvasive and deep penetration, is employed to activate catalytic reactions. Excessive ROS production with HESNW under US irradiation induces extensive DNA damage, activating the cGAS-STING signaling pathway and leading to PANoptosis. This ML-guided design strategy enables the precise tailoring of multicatalytic HEA nanomaterials and provides a generalizable blueprint for accelerating the discovery of multifunctional nanocatalysts through data-driven methodologies.