Srinivasan Srilok, Batra Rohit, Luo Duan, Loeffler Troy, Manna Sukriti, Chan Henry, Yang Liuxiang, Yang Wenge, Wen Jianguo, Darancet Pierre, K R S Sankaranarayanan Subramanian
Center for Nanoscale Materials, Argonne National Laboratory, Lemont, IL, 60439, USA.
Department of Mechanical and Industrial Engineering, University of Illinois, Chicago, IL, 60607, USA.
Nat Commun. 2022 Jun 6;13(1):3251. doi: 10.1038/s41467-022-30820-8.
Conventional phase diagram generation involves experimentation to provide an initial estimate of the set of thermodynamically accessible phases and their boundaries, followed by use of phenomenological models to interpolate between the available experimental data points and extrapolate to experimentally inaccessible regions. Such an approach, combined with high throughput first-principles calculations and data-mining techniques, has led to exhaustive thermodynamic databases (e.g. compatible with the CALPHAD method), albeit focused on the reduced set of phases observed at distinct thermodynamic equilibria. In contrast, materials during their synthesis, operation, or processing, may not reach their thermodynamic equilibrium state but, instead, remain trapped in a local (metastable) free energy minimum, which may exhibit desirable properties. Here, we introduce an automated workflow that integrates first-principles physics and atomistic simulations with machine learning (ML), and high-performance computing to allow rapid exploration of the metastable phases to construct "metastable" phase diagrams for materials far-from-equilibrium. Using carbon as a prototypical system, we demonstrate automated metastable phase diagram construction to map hundreds of metastable states ranging from near equilibrium to far-from-equilibrium (400 meV/atom). We incorporate the free energy calculations into a neural-network-based learning of the equations of state that allows for efficient construction of metastable phase diagrams. We use the metastable phase diagram and identify domains of relative stability and synthesizability of metastable materials. High temperature high pressure experiments using a diamond anvil cell on graphite sample coupled with high-resolution transmission electron microscopy (HRTEM) confirm our metastable phase predictions. In particular, we identify the previously ambiguous structure of n-diamond as a cubic-analog of diaphite-like lonsdaelite phase.
传统相图的生成需要进行实验，以初步估计热力学可及相及其边界的集合，然后使用唯象模型在可用的实验数据点之间进行插值，并外推到实验无法达到的区域。这种方法与高通量第一性原理计算和数据挖掘技术相结合，已经产生了详尽的热力学数据库（例如与CALPHAD方法兼容），尽管这些数据库侧重于在不同热力学平衡下观察到的简化相集。相比之下，材料在合成、运行或加工过程中，可能不会达到其热力学平衡状态，而是被困在局部（亚稳）自由能最小值中，这可能表现出理想的性能。在这里，我们引入了一种自动化工作流程，将第一性原理物理和原子模拟与机器学习（ML）以及高性能计算相结合，以便快速探索亚稳相，为远离平衡的材料构建“亚稳”相图。以碳作为典型体系，我们展示了自动化亚稳相图构建，以绘制数百个从近平衡到远离平衡（400毫电子伏特/原子）的亚稳态。我们将自由能计算纳入基于神经网络的状态方程学习中，从而能够高效构建亚稳相图。我们使用亚稳相图来识别亚稳材料的相对稳定性和可合成性区域。使用金刚石对顶砧对石墨样品进行高温高压实验，并结合高分辨率透射电子显微镜（HRTEM），证实了我们的亚稳相预测。特别是，我们确定了之前结构不明确的n-金刚石为类似透辉石的六方金刚石相的立方类似物。
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