用AI方法高效估算无序材料热力学性质，大幅降低计算成本。
Thermodynamic properties of chemically disordered compounds via AI-driven estimation of partition function with the PULSE method
- 通过生成式采样估计系统配分函数，替代传统蒙特卡洛方法。
- 在二维伊辛模型上实现高精度与高效率的平均性质预测。
- 适合研究化学无序材料的低成本热力学分析，尤其适用于传统方法耗时过长的场景。
本文提出改进版PULSE方法（配分函数无监督学习采样与评估），用于估算化学无序化合物的热力学性质。该方法旨在降低此类材料蒙特卡洛计算的高成本，并验证生成工具可通过采样与估计系统配分函数来预测热力学性质。为验证该方法的有效性，采用二维伊辛模型作为基准。结果表明，相较于传统蒙特卡洛采样方法，PULSE方法在保持高精度的同时显著提升计算效率。研究表明，PULSE方法具有高效性与可扩展性，是研究因化学无序影响而难以用常规方法低成本计算性质材料的重要工具。
原文摘要 · Abstract (English)
In this article, we present an improved version of the PULSE method (Partition function Unsupervised Learning Sampling and Evaluation) for estimating the thermodynamic properties of chemically disordered compounds. The aim is to reduce the computational cost of Monte Carlo approaches for this type of material and to demonstrate that this generative tool can estimate thermodynamic properties by sampling and estimating the partition function of the system. To validate this innovative approach, we use the 2D Ising model as a benchmark. We demonstrate that our method accurately reproduces average properties with high precision and efficiency compared to traditional Monte Carlo sampling methods. Our results highlight the efficiency and adaptability of the PULSE method, making it a valuable tool for studying materials for which conventional methods are too inefficient to compute properties affected by chemical disorder at low cost.
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