铈氢化物的化学计量比依赖性质：一种主动学习开发的相互原子势研究
Stoichiometry Dependent Properties of Cerium Hydride: An Active Learning Developed Interatomic Potential Study
AI总结：
本文通过主动学习开发了适用于铈氢化物的相互原子势，研究了化学计量比对晶格收缩等性质的影响机制。
摘要：
铈氢化物具有多种有趣的性质，包括随着氢含量增加而已知的晶格收缩和致密化。然而，精确的化学计量比控制在实验上并不容易，并且对于许多性质如熔化和低温扩散，从头计算方法在计算上不可行。因此，我们开发了一种适用于氢到铈比值从2.0到3.0的机器学习相互原子势。采用查询-委员会主动学习方法来开发训练集。利用经典分子动力学模拟，我们评估了一系列性质，并提供了与化学计量比相关的趋势的基本机制。大多数性质遵循晶格收缩的趋势，这由添加八面体原子引起的更强的晶格结合所支配。
英文摘要：
Cerium hydride has a variety of interesting properties, including a known lattice contraction and densification with increasing hydrogen content. However, precise stoichiometric control is not experimentally straightforward and {\it ab initio} approaches are not computationally feasible for many properties such as melting and low temperature diffusion. Therefore, we develop a machine-learned interatomic potential for cerium hydride that is valid for H to Ce ratios from 2.0 to 3.0. A query-by-committee active learning approach is used to develop the training set. Leveraging classical molecular dynamics simulations, we assess a range of properties and provide fundamental mechanisms for the trends with stoichiometry. A majority of the properties follow the trend of lattice contraction, being governed by the stronger lattice binding induced by adding octahedral atoms.
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