Compositional descriptor-based recommender system for the materials discovery.
Compositional descriptor-based recommender system for the materials discovery.
复制标题
用于材料发现的基于成分描述符的推荐系统。
DOI：
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复制
发表时间：
2017
影响因子：
4.4
通讯作者：
I. Tanaka
中科院分区：
文献类型：
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作者：
Atsuto Seko;Hiroyuki Hayashi;I. Tanaka
许多无机化合物的结构和性质已被历史地收集。然而，它只涵盖了一小部分可能的无机晶体，这意味着存在许多目前未知的化合物。强大的机器学习策略是从所有化学组合中发现新无机化合物的必要条件。在本文中，我们提出了一种基于推荐器的推荐系统方法来估计可以形成晶体的化学成分的相关性[即，化学相关成分（CRC）。除了文献中使用的数据驱动的组成相似性之外，使用组成描述符作为先验知识有助于发现新化合物。我们通过两种方式来验证我们的推荐系统。首先，一个数据库用于构建模型，而另一个数据库用于验证。其次，我们估计的相稳定性的化合物在预期的CRC使用密度泛函理论计算。
Structures and properties of many inorganic compounds have been collected historically. However, it only covers a very small portion of possible inorganic crystals, which implies the presence of numerous currently unknown compounds. A powerful machine-learning strategy is mandatory to discover new inorganic compounds from all chemical combinations. Herein we propose a descriptor-based recommender-system approach to estimate the relevance of chemical compositions where crystals can be formed [i.e., chemically relevant compositions (CRCs)]. In addition to data-driven compositional similarity used in the literature, the use of compositional descriptors as a prior knowledge is helpful for the discovery of new compounds. We validate our recommender systems in two ways. First, one database is used to construct a model, while another is used for the validation. Second, we estimate the phase stability for compounds at expected CRCs using density functional theory calculations.