AI 中文总结
该研究指出当前多数机器学习模型局限于零温度材料描述的缺陷，提出需开发热力学引导的机器学习，以实现现实条件下能源材料的精准设计。
AI 中文摘要
机器学习（ML）正通过快速预测此前需计算昂贵的第一性原理计算的材料性能，改变材料发现领域。但当前多数ML模型仍局限于零温度描述，仅学习静态晶格能，忽略了决定材料在有限温度下行为的热力学效应。由于相稳定性、功能响应及性能由自由能景观而非仅静态能量决定，这一限制成为现实操作条件下预测性材料设计的主要障碍。在本观点文章中，我们认为开发热力学引导的ML是材料发现领域最重要且探索最少的前沿之一。我们分析了基于能量的模型的根本缺陷，强调熵与非简谐性在决定自由能及材料功能中的关键作用。我们综述了新兴策略，包括机器学习原子间势、混合ML-统计力学框架，同时明确了与数据可用性、可迁移性、热力学一致性相关的关键挑战。基于这些进展，我们勾勒出以直接自由能学习、熵感知表征、跨温度自适应采样为核心的热力学引导ML路线图。我们强调该范式为能源材料提供的变革性机遇，并指出下一代ML模型必须超越静态能量预测，转向对材料在现实操作条件下行为的热力学描述。
英文摘要
Machine learning (ML) is transforming materials discovery by enabling rapid prediction of properties that previously required computationally expensive first-principles calculations. Yet most current ML models remain fundamentally limited to zero-temperature descriptions, learning static lattice energies while neglecting the thermodynamic effects that govern materials behaviour at finite temperature. Because phase stability, functional response, and performance are governed by free-energy landscapes rather than static energies alone, this limitation represents a major barrier to predictive materials design under realistic operating conditions. In this Perspective, we argue that developing thermodynamics-informed ML constitutes one of the most important and least explored frontiers in materials discovery. We examine the fundamental shortcomings of energy-based models, highlighting the essential roles of entropy and anharmonicity in determining free energies and materials functionality. We review emerging strategies, including machine-learned interatomic potentials and hybrid ML-statistical mechanics frameworks, while identifying key challenges related to data availability, transferability, and thermodynamic consistency. Building on these advances, we outline a roadmap for thermodynamics-informed ML centred on direct free-energy learning, entropy-aware representations, and adaptive sampling across temperature. We highlight the transformative opportunities this paradigm offers for energy materials and argue that the next generation of ML models must move beyond static energy predictions towards a thermodynamic description of materials behaviour under realistic operating conditions.
Comments19 pages, 4 figures
