• A predictive design strategy is introduced to guide the selection of nanodiamond functionalization interfaces within large combinatorial design spaces. • Model-guided selection enables the synthesis and experimental validation of high-performance hybrid nanodiamond systems. • The approach reduces empirical trial-and-error and accelerates rational design of advanced functional nanodiamonds. • Practical design rules linking nanodiamond core properties, surface chemistry and application response are established. The rational design of biofunctionalized nanodiamonds (NDs) remains challenging due to the vast combinatorial space arising from ND core properties and surface functionalization strategies. Here, we introduce ND.PTML, a perturbation-theory-based machine learning framework developed to guide the selection and prioritization of functionalized nanodiamond systems using descriptors of both the ND core and functionalizing agents. A curated dataset of 426 experimentally reported nanosystems was used to train interpretable classification models. The optimized Decision Tree achieved the best predictive performance (training: Sp = 95.1%, Sn = 71.6%; validation: Sp = 78.1%, Sn = 71.4%) with AUROC values above 0.80, indicating robust classification capability. Model-guided screening generated 103 virtual configurations and supported the experimental synthesis of six representative nanosystems incorporating synthetic linkers and plant-derived extracts. Surface functionalization was confirmed by FTIR, Raman, and XPS analyses, while DLS and ζ-potential measurements demonstrated improved colloidal stability relative to pristine nanodiamonds. Optoelectronic and cytotoxicity assays validated predicted performance trends. These results establish an experimentally validated machine-learning-assisted materials-by-design strategy that reduces empirical trial-and-error and enables interface-engineered functional nanomaterials.
Nocedo-Mena et al. (Sun,) studied this question.