Efficiently searching extreme mechanical properties via boundless objective-free exploration and minimal first-principles calculations

Abstract Despite the machine learning (ML) methods have been largely used recently, predicted materials properties usually cannot exceed range of original training data. We deployed a boundless objective-free exploration approach to combine traditional ML and density functional theory (DFT) in searching extreme material properties. This combination not only improves efficiency for screening large-scale with minimal DFT inquiry, but also yields beyond range. use Stein novelty recommend outliers then verify using DFT. Validated data are added into dataset next round iteration. test loop training-recommendation-validation mechanical property space. By 85,707 crystal structures, we identify 21 ultrahigh hardness structures 11 negative Poisson’s ratio structures. The algorithm is very promising future discovery that can push limit calculations on ~1% pool.