High-Throughput Screening Approach for Nanoporous Materials Genome Using Topological Data Analysis: Application to Zeolites.
High-Throughput Screening Approach for Nanoporous Materials Genome Using Topological Data Analysis: Application to Zeolites.
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DOI：
10.1021/acs.jctc.8b00253
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发表时间：
2018-08-14
影响因子：
5.5
通讯作者：
Smit B
中科院分区：
文献类型：
--
作者：
Lee Y;Barthel SD;Dłotko P;Moosavi SM;Hess K;Smit B
材料基因组倡议创建了一个包含不同类别纳米多孔材料的大型（超过一百万）数据库。由于原则上可以通过实验合成的假设材料的数量是无限的，因此使用这些数据库发现新材料的瓶颈是缺乏有效的计算工具来分析它们。目前的方法使用强力分子模拟来生成预测这些材料在不同应用中的性能所需的热力学数据，但由于计算困难，这种方法仅限于分析数万个结构。因此，可以想象，甚至很可能，对于任何给定应用来说，最好的纳米多孔材料尚未在实验和理论上被发现。在本文中，我们以沸石数据库为例，寻求一种计算方法来解决这个问题，从强力表征过渡到基于大数据分析的高通量筛选方法。为了识别和比较沸石，我们使用了基于拓扑数据分析的描述符（TD）来识别孔隙形状。对于甲烷储存和碳捕获应用，我们对高度相似的沸石对进行分析，发现种子沸石和相应的沸石对的性能特性之间存在良好的相关性，这证明了 TD 预测性能特性的能力。研究还表明，当某些顶级沸石已知时，TD 可用于以高概率检测其他高性能材料作为其邻居。最后，我们基于TD进行了沸石的高通量筛选。对于甲烷储存（或碳捕获）应用，我们筛选出的有前景的系列包含高比例的顶级性能沸石：整个系列中排名前 1% 的沸石占 45%（或 23%）。这一结果表明，我们使用 TD 的筛选方法在寻找高性能材料方面非常有效。我们希望通过简单地调整一个参数（目标气体分子的大小），这种方法可以轻松扩展到其他应用。
The materials genome initiative has led to the creation of a large (over a million) database of different classes of nanoporous materials. As the number of hypothetical materials that can, in principle, be experimentally synthesized is infinite, a bottleneck in the use of these databases for the discovery of novel materials is the lack of efficient computational tools to analyze them. Current approaches use brute-force molecular simulations to generate thermodynamic data needed to predict the performance of these materials in different applications, but this approach is limited to the analysis of tens of thousands of structures due to computational intractability. As such, it is conceivable and even likely that the best nanoporous materials for any given application have yet to be discovered both experimentally and theoretically. In this article, we seek a computational approach to tackle this issue by transitioning away from brute-force characterization to high-throughput screening methods based on big-data analysis, using the zeolite database as an example. For identifying and comparing zeolites, we used a topological data analysis-based descriptor (TD) recognizing pore shapes. For methane storage and carbon capture applications, our analyses seeking pairs of highly similar zeolites discovered good correlations between performance properties of a seed zeolite and the corresponding pair, which demonstrates the capability of TD to predict performance properties. It was also shown that when some top zeolites are known, TD can be used to detect other high-performing materials as their neighbors with high probability. Finally, we performed high-throughput screening of zeolites based on TD. For methane storage (or carbon capture) applications, the promising sets from our screenings contained high-percentages of top-performing zeolites: 45% (or 23%) of the top 1% zeolites in the entire set. This result shows that our screening approach using TD is highly efficient in finding high-performing materials. We expect that this approach could easily be extended to other applications by simply adjusting one parameter, the size of the target gas molecule.
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