基于复制交换嵌套采样的一阶原理相图主动学习潜力
Active learning potentials for first-principles phase diagrams using replica-exchange nested sampling
AI总结：
本文提出基于复制交换嵌套采样的主动学习策略，用于生成训练数据和计算相图，展示其在硅、锗和钛中的有效性。
摘要：
从第一原理准确预测材料相图仍然是计算材料科学中的核心挑战。机器学习的原子间势能可以提供接近DFT的精度，但其可靠性很大程度上取决于代表性训练数据的可用性，这些数据必须覆盖势能面的所有相关区域。本文提出了一种完全自动化的主动学习（AL）策略，基于复制交换嵌套采样（RENS），用于生成训练数据和计算完整的压力-温度相图。在我们的框架中，RENS同时充当探索引擎和获取机制：其固有的多样性和基于似然的采样确保所选用于DFT标记的配置既信息丰富又热力学代表性。我们应用该方法于硅、锗和钛，使用在r2SCAN理论水平上训练的势能。对于所有系统，AL过程在10-15次迭代内收敛，产生可转移的势能，能够再现已知的相变和热力学趋势。这些结果表明，基于RENS的AL提供了一种通用且自动的方法，用于构建机器学习原子间势能并预测广泛热力学条件下的第一原理相图。
英文摘要：
Accurate prediction of materials phase diagrams from first principles remains a central challenge in computational materials science. Machine-learning interatomic potentials can provide near-DFT accuracy at a fraction of the cost, but their reliability crucially depends on the availability of representative training data that span all relevant regions of the potential-energy surface. Here, we present a fully automated active-learning (AL) strategy based on replica-exchange nested sampling (RENS) for the generation of training data and the computation of complete pressure-temperature phase diagrams. In our framework, RENS acts as both the exploration engine and the acquisition mechanism: its intrinsic diversity and likelihood-constrained sampling ensure that the configurations selected for DFT labeling are both informative and thermodynamically representative. We apply the approach to silicon, germanium, and titanium using potentials trained at the r2SCAN level of theory. For all systems, the AL process converges within 10-15 iterations, yielding transferable potentials that reproduce known phase transitions and thermodynamic trends. These results demonstrate that RENS-based AL provides a general and autonomous route to constructing machine-learning interatomic potentials and predicting first-principles phase diagrams across broad thermodynamic conditions.
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