Prediction of Synthesis of 2D Metal Carbides and Nitrides (MXenes) and Their Precursors with Positive and Unlabeled Machine Learning
Prediction of Synthesis of 2D Metal Carbides and Nitrides (MXenes) and Their Precursors with Positive and Unlabeled Machine Learning
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DOI：
10.1021/acsnano.8b08014
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发表时间：
2019-03-01
期刊：
影响因子：
17.1
通讯作者：
Shenoy, Vivek B.
中科院分区：
文献类型：
--
作者：
Frey, Nathan C.;Wang, Jin;Shenoy, Vivek B.
随着人们对二维材料应用前景的日益关注，具有奇异性质的二维材料系统的识别研究取得了长足的进展。这一领域的瓶颈越来越多地是这些材料的合成。尽管理论计算已经预测了无数有前途的2D材料，但自石墨烯最初发现以来，只有几十种材料在实验上得以实现。在这里，我们采用了最先进的积极和无标记（PU）机器学习框架来预测理论上提出的2D材料最有可能成功合成。利用高通量密度泛函理论计算的元素信息和数据，我们将PU学习方法应用于2D过渡金属碳化物，碳氮化物和氮化物的MXene家族及其层状前体MAX相，并确定了18种非常有前途的合成候选物MXene化合物。通过考虑MXene及其前体，我们进一步提出了20种可合成的MAX相，这些相可以化学剥离以产生MXene。
Growing interest in the potential applications of two-dimensional (2D) materials has fueled advancement in the identification of 2D systems with exotic properties. Increasingly, the bottleneck in this field is the synthesis of these materials. Although theoretical calculations have predicted a myriad of promising 2D materials, only a few dozen have been experimentally realized since the initial discovery of graphene. Here, we adapt the state-of-the-art positive and unlabeled (PU) machine learning framework to predict which theoretically proposed 2D materials have the highest likelihood of being successfully synthesized. Using elemental information and data from high-throughput density functional theory calculations, we apply the PU learning method to the MXene family of 2D transition metal carbides, carbonitrides, and nitrides, and their layered precursor MAX phases, and identify 18 MXene compounds that are highly promising candidates for synthesis. By considering both the MXenes and their precursors, we further propose 20 synthesizable MAX phases that can be chemically exfoliated to produce MXenes.