Oxidation states are the charges of atoms after their ionic approximation of their bonds, which have been widely used in charge-neutrality verification, crystal structure determination, and reaction estimation. Currently only heuristic rules exist for guessing the oxidation states of a given compound with many exceptions. Recent work has developed machine learning models based on heuristic structural features for predicting the oxidation states of metal ions. However, composition based oxidation state prediction still remains elusive so far, which is more important in new material discovery for which the structures are not even available. This work proposes a novel deep learning based BERT transformer language model BERTOS for predicting the oxidation states of all elements of inorganic compounds given only their chemical composition. Our model achieves 96.82\% accuracy for all-element oxidation states prediction benchmarked on the cleaned ICSD dataset and achieves 97.61\% accuracy for oxide materials. We also demonstrate how it can be used to conduct large-scale screening of hypothetical material compositions for materials discovery.

NLP系统的解释性方法遇到了因果推断的基本问题的版本：对于给定的基础真相输入文本，我们从未真正观察到隔离模型表示对输出的因果影响所必需的反事实文本。作为回应，许多解释性方法不使用反事实文本，假设它们将是不可用的。在本文中，我们表明可以使用近似反事实来创建强大的因果解释方法，该方法可以由人类写成近似特定的反事实或简单地使用元数据指导的启发式启发式启示术进行采样。我们提案的核心是因果替代模型（CPM）。 CPM解释了一个黑框$ \ Mathcal {n} $，因为它经过培训可以具有与$ \ Mathcal {n} $相同的实际输入/输出行为，而创建可以介入的神经表示，以模拟反事实输入/$ \ MATHCAL {N} $的输出行为。此外，我们证明了$ \ Mathcal {n} $的最佳CPM在做出事实预测时性能与$ \ Mathcal {n} $相当地执行，这意味着CPM可以简单地替换$ \ Mathcal {n} $，从而导致更多信息可解释的部署模型。我们的代码可在https://github.com/frankaging/causal-proxy-model上找到。