发表机构
FZU - Institute of Physics of the Czech Academy of Sciences; Department of Materials, Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University in Prague; Department of Solid State Physics, Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University in Prague（捷克科学院物理研究所; 布拉格捷克理工大学核科学与物理工程学院材料系; 布拉格捷克理工大学核科学与物理工程学院凝聚态物理系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种结合物理描述符与对比学习的机器学习框架，通过两阶段数据生成实现未知相结构系统的相图构建，并在PbZrO3模型上验证了其有效性。
AI 中文摘要
提出了一种用于构建凝聚态系统中未知相结构相图的机器学习（ML）框架。该方法将物理启发的描述符与对比表示学习以及两阶段数据生成工作流相结合。首先，通过蒙特卡罗模拟退火生成一组稀疏配置，以训练一个产生低维嵌入的对比神经网络。随后，代表性的相配置被用作密集参数网格上基于梯度优化的初始条件，从而能够高效生成用于高分辨率相图构建的精炼数据集。为了优化工作流，还考察了该方法的一种变体，其中派生描述符在对比目标中用作软标签。该框架在受PbZrO3启发的模型势上进行了演示，该势以耦合序参量场的形式表述，并作为探索配置多样性和相行为的物理启发测试平台。所提出的方法为机器学习辅助的相图构建提供了一条物理信息丰富的途径，将数据分析与自适应数据集精炼相结合。
英文摘要
A machine learning (ML) framework for phase-diagram construction in condensed matter systems with unknown phase structure is presented. The method combines physically motivated descriptors with contrastive representation learning and a two-stage data-generation workflow. First, a sparse set of configurations is generated by Monte Carlo simulated annealing in order to train a contrastive neural network that produces a low-dimensional embedding. Consequently, representative phase configurations are used as initial conditions for gradient-based optimization on a dense parameter grid, enabling efficient generation of refined datasets for high-resolution phase-diagram construction. In order to optimize the workflow, a variant of the method, in which derived descriptors are used as soft labels in the contrastive objective, is examined as well. The framework is demonstrated on a PbZrO3-inspired model potential formulated in terms of coupled order-parameter fields and used as a physically motivated testbed for exploring configuration diversity and phase behavior. The proposed approach provides a physically informed route to ML-assisted phase-diagram construction that integrates data analysis with adaptive dataset refinement.
