Materials discovery through machine learning formation energy
Materials discovery through machine learning formation energy
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DOI：
10.1088/2515-7655/abe425
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发表时间：
2021-04
期刊：
影响因子：
--
通讯作者：
Gordon G C Peterson;Jakoah Brgoch
中科院分区：
文献类型：
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作者：
Gordon G C Peterson;Jakoah Brgoch
材料信息学的萌芽领域恰逢人们转向人工智能，以发现新的固态化合物。结晶学和计算数据存储库的稳步扩大，为开发能够预测一系列物理性质的数据驱动模型奠定了基础。特别是，机器学习方法已经显示出通过筛选晶体结构数据库来识别具有接近理想性能的材料用于能源相关应用的能力。然而，在数据指导下发现全新的、以前从未报道过的化合物的例子仍然有限。确定未知化合物是否可合成的关键步骤是获得形成能并构造相关的凸壳。幸运的是，通过密度泛函理论(DFT)数据仓库，这些信息已经变得广泛可用，以至于可以用来开发机器学习模型。在这篇综述中，我们讨论了开发能够预测形成能的机器学习模型的具体设计选择，包括控制材料稳定性的热力学参数。我们研究了文献中提出的几个模型，这些模型涵盖了各种可能的体系结构和特征集，并发现它们已经成功地发现了新的DFT稳定化合物，并指导了材料合成。为了扩大合成固态化学家对机器学习模型的访问，我们还提供了MatLearn。这一基于网络的应用程序旨在引导对组成图的探索，指向可能包含热力学上可访问的无机化合物的区域。最后，我们讨论了机器学习形成能的未来，并强调了提高预测能力对新能源相关材料的综合实现的机会。
The budding field of materials informatics has coincided with a shift towards artificial intelligence to discover new solid-state compounds. The steady expansion of repositories for crystallographic and computational data has set the stage for developing data-driven models capable of predicting a bevy of physical properties. Machine learning methods, in particular, have already shown the ability to identify materials with near ideal properties for energy-related applications by screening crystal structure databases. However, examples of the data-guided discovery of entirely new, never-before-reported compounds remain limited. The critical step for determining if an unknown compound is synthetically accessible is obtaining the formation energy and constructing the associated convex hull. Fortunately, this information has become widely available through density functional theory (DFT) data repositories to the point that they can be used to develop machine learning models. In this Review, we discuss the specific design choices for developing a machine learning model capable of predicting formation energy, including the thermodynamic quantities governing material stability. We investigate several models presented in the literature that cover various possible architectures and feature sets and find that they have succeeded in uncovering new DFT-stable compounds and directing materials synthesis. To expand access to machine learning models for synthetic solid-state chemists, we additionally present MatLearn. This web-based application is intended to guide the exploration of a composition diagram towards regions likely to contain thermodynamically accessible inorganic compounds. Finally, we discuss the future of machine-learned formation energy and highlight the opportunities for improved predictive power toward the synthetic realization of new energy-related materials.