Efficiently searching extreme mechanical properties via boundless objective-free exploration and minimal first-principles calculations
Efficiently searching extreme mechanical properties via boundless objective-free exploration and minimal first-principles calculations
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DOI：
10.1038/s41524-022-00836-1
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发表时间：
2022-07
影响因子：
9.7
通讯作者：
Joshua Ojih;Mohammed Al-Fahdi;Alejandro Rodriguez;K. Choudhary;Ming Hu
中科院分区：
文献类型：
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作者：
Joshua Ojih;Mohammed Al-Fahdi;Alejandro Rodriguez;K. Choudhary;Ming Hu
尽管机器学习（ML）方法近年来得到了广泛的应用，但预测的材料性能通常不能超过原始训练数据的范围。我们部署了一种无边界无目标的探索方法，将联合收割机传统ML和密度泛函理论（DFT）结合起来，搜索极端的材料性质。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，而且产生了超出原始训练范围的性质。我们使用Stein新奇来推荐离群值，然后使用DFT进行验证。然后将验证的数据添加到训练数据集中，用于下一轮迭代。我们在机械属性空间中测试了训练-推荐-验证的循环。通过对85，707个晶体结构的筛选，我们确定了21个硬度为100的结构和11个负泊松比结构。该算法是非常有前途的未来材料发现，可以推动材料性能的极限与最小的DFT计算只有~1%的结构在筛选池。
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.