Phase Diagrams of Alloys and Their Hydrides via On-Lattice Graph Neural Networks and Limited Training Data
Phase Diagrams of Alloys and Their Hydrides via On-Lattice Graph Neural Networks and Limited Training Data
复制标题
通过点阵图神经网络和有限训练数据绘制合金及其氢化物的相图
DOI：
10.1021/acs.jpclett.3c03369
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发表时间：
2024
期刊：
影响因子：
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通讯作者：
Witman M
中科院分区：
文献类型：
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作者：
Witman M
需要对采样密集型热力学性质进行有效预测，以评估材料性能并允许针对各种技术应用进行高通量材料建模。为了减轻采用密度泛函理论 (DFT) 的高通量配置采样的高昂计算费用，簇扩展等替代建模策略的效率要高出许多数量级，但在组成复杂性较高的系统中可能难以构建。因此，我们采用最小复杂度的图神经网络模型，可以准确预测甚至可以从理想（非松弛）晶体学表示推断出 DFT 松弛结构的训练外分布形成能。这使得各种热力学性质预测所需的大规模采样成为可能，否则这些预测可能会很棘手，并且可以通过小型训练数据集来实现。优化低密度高熵合金的热力学稳定性和调节金属合金中氢的平台压力两个示例证明了这种方法的强大功能，可以扩展到各种材料发现和建模问题。
Efficient prediction of sampling-intensive thermodynamic properties is needed to evaluate material performance and permit high-throughput materials modeling for a diverse array of technology applications. To alleviate the prohibitive computational expense of high-throughput configurational sampling with density functional theory (DFT), surrogate modeling strategies like cluster expansion are many orders of magnitude more efficient but can be difficult to construct in systems with high compositional complexity. We therefore employ minimal-complexity graph neural network models that accurately predict and can even extrapolate to out-of-train distribution formation energies of DFT-relaxed structures from an ideal (unrelaxed) crystallographic representation. This enables the large-scale sampling necessary for various thermodynamic property predictions that may otherwise be intractable and can be achieved with small training data sets. Two exemplars, optimizing the thermodynamic stability of low-density high-entropy alloys and modulating the plateau pressure of hydrogen in metal alloys, demonstrate the power of this approach, which can be extended to a variety of materials discovery and modeling problems.