pyeCE：嵌入式簇展开的Python实现
pyeCE: A Python Implementation of the Embedded Cluster Expansion
- École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）
- Institute of Materials, École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院材料研究所）
- Laboratory of materials design and simulation (MADES), Institute of Materials, École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院材料研究所材料设计与模拟实验室）
- National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院新材料计算设计与发现国家中心）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
pyeCE是一个开源Python库，实现嵌入式簇展开，通过机器学习映射化学组分，支持多组分合金的有限温度热力学模拟，并在难熔合金和氢溶解系统中得到验证。
AI中文摘要：
簇展开是一种广泛使用的方法，用于从零开尔文第一性原理计算预测合金的有限温度热力学性质，但其传统形式对于含有超过三或四种化学组分的材料变得难以处理。因此，高熵合金在很大程度上仍难以触及。我们提出了pyeCE，一个开源的Python库，实现了嵌入式簇展开（eCE），其中机器学习将多种化学组分映射到一组较小的有效组分上，从而限制了簇函数数量的增长。pyeCE提供了完整的建模工作流程，包括构建具有可学习的每子晶格化学嵌入的对称适应描述符、神经网络能量模型、基于阶梯的训练、不确定性量化，以及通过蒙特卡洛采样的有限温度模拟。该库基于PyTorch和pymatgen库构建，支持在多个子晶格上具有多种组分的系统，并可在图形处理单元上运行。我们在两个材料系统上演示了该软件包。在第一个系统中，一个跨越9组分难熔合金全成分范围的单一模型解析了短程有序和有序-无序行为。该模型能够快速筛选具有强Cr聚集的成分，这种特征与连续、耐腐蚀氧化皮的形成有关。在第二个系统中，一个关于氢在Mo-Nb-W合金中溶解的模型重现了氢吸收的成分依赖性，并解析了氢占据的间隙环境。pyeCE的模块化设计使其能够扩展到合金热力学之外的问题，包括动力学、缺陷能量学，以及化学有序与磁性和振动自由度的耦合。
英文摘要：
The cluster expansion is a widely used approach for predicting the finite-temperature thermodynamics of alloys from zero-kelvin first-principles calculations, but its conventional formulation becomes intractable for materials with more than three or four chemical species. High-entropy alloys have therefore remained largely out of reach. We present pyeCE, an open-source Python library that implements the embedded cluster expansion (eCE), in which machine learning maps many chemical species onto a smaller set of effective species and thereby limits the growth in the number of cluster functions. pyeCE provides the complete modeling workflow, including the construction of symmetry-adapted descriptors with a learnable per-sublattice chemical embedding, a neural-network energy model, ladder-based training, uncertainty quantification, and finite-temperature simulations through Monte Carlo sampling. Built on the PyTorch and pymatgen libraries, it supports systems with multiple species on multiple sublattices and runs on graphics processing units. We demonstrate the package on two material systems. In the first, a single model spanning the full composition space of a 9-component refractory alloy resolves short-range order and order--disorder behavior. This model enables rapid screening for compositions with strong Cr clustering, a feature linked to the formation of a continuous, corrosion-resistant oxide scale. In the second, a model of hydrogen dissolution in a Mo--Nb--W alloy reproduces the composition dependence of hydrogen uptake and resolves the interstitial environments that hydrogen occupies. The modular design of pyeCE allows it to be extended to problems beyond alloy thermodynamics, including kinetics, defect energetics, and the coupling of chemical order to magnetic and vibrational degrees of freedom.
