Marriage of High‐Throughput Gradient Surface Generation With Statistical Learning for the Rational Design of Functionalized Biomaterials

Functional biomaterial is already an important aspect in modern therapeutics; yet, the design of novel multi‐functional biomaterial is still a challenging task nowadays. When several biofunctional components are present, the complexity that arises from their combinations and interactions will lead to tedious trial‐and‐error screening. In this work, a novel strategy of biomaterial rational design through the marriage of gradient surface generation with statistical learning is presented. Not only can parameter combinations be screened in a high‐throughput fashion, but also the optimal conditions beyond the experimentally tested range can be extrapolated from the models. The power of the strategy is demonstrated in rationally designing an unprecedented ternary functionalized surface for orthopedic implant, with optimal osteogenic, angiogenic, and neurogenic activities, and its optimality and the best osteointegration promotion are confirmed in vitro and in vivo, respectively. The presented strategy is expected to open up new possibilities in the rational design of biomaterials. By the marriage of gradient surface generation with statistical learning, a strategy of biomaterial rational design is developed. The parameter combinations are screened in a high‐throughput fashion, and the optimal conditions beyond the experimentally tested range are extrapolated. Subsequently, an unprecedented ternary functionalized surface is designed for orthopedic implant, with optimal osteogenic, angiogenic, and neurogenic activities.