The likelihood of an element to adopt a specific oxidation state in a solid, given a certain set of neighbours, might often be obvious to a trained chemist. However, encoding this information for use in high-throughput searches presents a significant challenge. We carry out a statistical analysis of the occurrence of oxidation states in 16 735 ordered, inorganic compounds and show that a large number of cations are only likely to exhibit certain oxidation states in combination with particular anions. We use this data to build a model that ascribes probabilities to the formation of hypothetical compounds, given the proposed oxidation states of their constituent species. The model is then used as part of a high-throughput materials design process, which significantly narrows down the vast compositional search space for new ternary metal halide compounds. Finally, we employ a machine learning analysis of existing compounds to suggest likely structures for a small subset of the candidate compositions. We predict two new compounds, MnZnBr₄ and YSnF₇, that are thermodynamically stable according to density functional theory, as well as four compounds, MnCdBr₄, MnRu₂Br₈, ScZnF₅ and ZnCoBr₄, which lie within the window of metastability.

中文翻译：

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通过化学类比发现材料：氧化态在结构预测中的作用

给定一组特定的邻居，元素在固体中采用特定氧化态的可能性对于受过训练的化学家而言通常是显而易见的。然而，编码该信息以用于高通量搜索提出了巨大的挑战。我们对16 735种有序无机化合物中氧化态的发生情况进行了统计分析，结果表明，大量阳离子仅与特定阴离子结合，才可能表现出某些氧化态。我们使用这些数据来建立一个模型，该模型根据给定的构成物种的氧化态，将其归因于假设化合物的形成。然后将该模型用作高通量材料设计过程的一部分，这极大地缩小了新的三元金属卤化物化合物的巨大组成搜索空间。最后，我们对现有化合物进行了机器学习分析，以提出一小部分候选化合物的可能结构。我们预测了两种新化合物MnZnBr根据密度泛函理论，它们在热力学上是稳定的₄和YSnF ₇，以及位于亚稳态窗口内的四种化合物MnCdBr ₄，MnRu ₂ Br ₈，ScZnF ₅和ZnCoBr ₄。