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

尽管最近大量使用了机器学习 (ML) 方法，但预测的材料特性通常不能超出原始训练数据的范围。我们部署了一种无边界的无目标探索方法，将传统的机器学习和密度泛函理论 (DFT) 结合起来搜索极端材料特性。这种组合不仅提高了用最少的 DFT 查询筛选大规模材料的效率，而且还产生了超出原始训练范围的属性。我们使用 Stein 新颖性来推荐异常值，然后使用 DFT 进行验证。然后将经过验证的数据添加到训练数据集中进行下一轮迭代。我们在机械性能空间中测试了训练-推荐-验证的循环。通过筛选 85,707 个晶体结构，我们确定了 21 个超高硬度结构和 11 个负泊松比结构。该算法对于未来的材料发现非常有希望，它可以将材料特性推到极限，只需对筛选池中约 1% 的结构进行最小的 DFT 计算。