Network analysis of synthesizable materials discovery
Network analysis of synthesizable materials discovery
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DOI：
10.1038/s41467-019-10030-5
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发表时间：
2019-05-01
影响因子：
16.6
通讯作者：
Hummelshoj, Jens S.
中科院分区：
文献类型：
--
作者：
Aykol, Muratahan;Hegde, Vinay I.;Hummelshoj, Jens S.
评估无机材料的可合成性是使用计算加速其发现的巨大挑战。材料的合成是一个复杂的过程，不仅取决于其相对于其他材料的热力学稳定性，而且还取决于从动力学到合成技术的进步以及前体的可用性的因素。这种复杂性使得发展一个通用的理论或第一原理的方法，目前不切实际的综合性。在这里，我们展示了如何从材料稳定性网络的动态中预测可合成性的替代途径：通过结合高通量密度泛函理论计算的无机材料的凸自由能表面和从引用中提取的实验发现时间线构建的无标度网络。底层网络属性的时间演化使我们能够使用机器学习来预测假设的计算机生成材料是否适合成功的实验合成。
Assessing the synthesizability of inorganic materials is a grand challenge for accelerating their discovery using computations. Synthesis of a material is a complex process that depends not only on its thermodynamic stability with respect to others, but also on factors from kinetics, to advances in synthesis techniques, to the availability of precursors. This complexity makes the development of a general theory or first-principles approach to synthesizability currently impractical. Here we show how an alternative pathway to predicting synthesizability emerges from the dynamics of the materials stability network: a scale-free network constructed by combining the convex free-energy surface of inorganic materials computed by high-throughput density functional theory and their experimental discovery timelines extracted from citations. The time-evolution of the underlying network properties allows us to use machine-learning to predict the likelihood that hypothetical, computer-generated materials will be amenable to successful experimental synthesis.