探索极限：复杂热力学条件下多元素材料科学的原子基础
Exploring the extremes: atomic basis for multi-elemental materials science under complex thermodynamic conditions
AI总结：
本文提出了一种基于信息熵最大化的方法，通过生成覆盖周期表广泛区域的数据集，提升在极端热力学条件下多元素材料模拟的鲁棒性与发现能力。
摘要：
现代材料科学历史上一直基于结合周期表中受限的子集，倾向于高纯度、少元素系统。然而，随着循环经济的需求增加，以及对材料在行星和工业极端条件下行为的理解需求，越来越多地要求掌握门捷列夫材料——化学和结构复杂的系统，跨越周期表的大部分区域。在这些情况下，当前通用的机器学习原子间势能函数往往失效，主要是由于传统训练数据集存在系统性缺口，这些数据集强调低能、近平衡结构。我们通过引入一种不依赖化学的、信息熵最大化协议来生成数据，解决了这一限制。通过将结构采样与热力学偏倚解耦，我们的方法为原子相互作用提供了稳健的物理先验，覆盖整个周期表，包括远离平衡和极端条件的区域。在由此产生的统计最大化熵（SMAX）数据集上训练图原子簇扩展（GRACE）模型，在一系列严格基准测试中表现出明显改进的鲁棒性。这些包括锡的大应变相变、钨基合金的缺陷演变以及催化反应势垒预测。更广泛地说，我们的方法建立了一种可扩展且原则性的方法，用于导航未来材料设计相关的大化学和构型空间。它使一种通过模拟发现的范式成为可能，在这种范式中，无偏采样协议能够自主解析多元素混合物中的新兴结构——例如含有地球地壳中九种最丰富元素的系统——而无需依赖先验化学假设。
英文摘要：
Modern materials science has historically been founded on combining restricted subsets of the periodic table, favoring high-purity, few-element systems. However, the demands of an emerging circular economy, together with the need to understand materials behavior under planetary and industrial extremes, increasingly require mastering Mendeleev materials - chemically and structurally complex systems that span large portions of the periodic table. In these regimes, current universal machine-learning interatomic potentials often fail, largely due to systematic gaps in traditional training datasets that heavily emphasize low-energy, near-equilibrium structures. We address this limitation by introducing a chemistry-agnostic, information-entropy-maximization protocol for data generation. By decoupling structural sampling from thermodynamic bias, our approach provides a robust physical prior for atomic interactions across the entire periodic table, including regimes far from equilibrium and under extreme conditions. Training a Graph Atomic Cluster Expansion (GRACE) model on the resulting statistically maximized entropy (SMAX) dataset yields markedly improved robustness across a range of stringent benchmarks. These include large-strain phase transformations in tin, defect evolution in tungsten-based alloys, and catalytic reaction barrier prediction. More broadly, our approach establishes a scalable and principled methodology for navigating the vast chemical and configurational space relevant to future materials design. It enables a paradigm of discovery by simulation in which unbiased sampling protocols autonomously resolve emergent structures in multi-elemental mixtures-such as systems containing the nine most abundant elements in the Earth's crust-without reliance on a priori chemical assumptions.
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