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Learning hidden chemistry with deep neural networks
Computational Materials Science ( IF 3.3 ) Pub Date : 2021-08-17 , DOI: 10.1016/j.commatsci.2021.110784
Tien-Cuong Nguyen 1 , Van-Quyen Nguyen 2 , Van-Linh Ngo 3 , Quang-Khoat Than 3 , Tien-Lam Pham 2, 4
Affiliation  

We demonstrate a machine learning approach designed to extract hidden chemistry/physics to facilitate new materials discovery. In particular, we propose a novel method for learning latent knowledge from material structure data in which machine learning models are developed to present the possibility that an atom can be paired with a chemical environment in an observed materials. For this purpose, we trained deep neural networks acquiring information from the atom of interest and its environment to estimate the possibility. The models were then used to establish recommendation systems, which can suggest a list of atoms for an environment within a structure. The center atom of that environment was then replaced with the various recommended atoms to generate new structures. Based on these recommendations, we also propose a method of dissimilarity measurement between the atoms and, through hierarchical cluster analysis and visualization using the multidimensional scaling algorithm, illustrate that this dissimilarity can reflect the chemistry of the elements. Finally, our models were applied to the discovery of new structures in the well-known magnetic material Nd2Fe14B. Our models propose 108 new structures, 71 of which are confirmed to converge to local-minimum-energy structures with formation energy less than +0.1 eV by first-principles calculations.



中文翻译:

用深度神经网络学习隐藏的化学

我们展示了一种旨在提取隐藏的化学/物理以促进新材料发现的机器学习方法。特别是,我们提出了一种从材料结构数据中学习潜在知识的新方法,其中开发了机器学习模型,以呈现原子可以与观察到的材料中的化学环境配对的可能性。为此,我们训练了深度神经网络,从感兴趣的原子及其环境中获取信息以估计可能性。然后使用这些模型建立推荐系统,该系统可以为结构内的环境建议原子列表。然后将该环境的中心原子替换为各种推荐的原子以生成新结构。基于这些建议,我们还提出了一种原子之间相异性测量的方法,并通过层次聚类分析和使用多维缩放算法的可视化,说明这种相异性可以反映元素的化学性质。最后,我们的模型被应用于在众所周知的磁性材料 Nd 中发现新结构2 Fe 14 B。我们的模型提出了 108 种新结构,其中 71 种被证实通过第一性原理计算收敛到形成能小于 +0.1 eV 的局部最小能量结构。

更新日期:2021-08-17
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