Abstract: With the growing amount of chemical data stored digitally, it has become crucial to represent chemical compounds consistently. Harmonized representations facilitate the extraction of insightful information from datasets, and are advantageous for machine learning applications. Compound standardization is typically accomplished using rule-based algorithms that modify undesirable descriptions of functional groups, resulting in a consistent representation throughout the dataset. Here, we present the first deep-learning model for molecular standardization. We enable custom standardization schemes based solely on data, as well as standardization options that are difficult to encode in rules. Our model achieves $>98\%$ accuracy in learning two popular rule-based protocols. When fine-tuned on a relatively small dataset of catalysts (for which there is currently no automated standardization practice), the model predicts the expected standardized molecular format with a test accuracy of $62\%$ on average. We show that our model learns not only the grammar and syntax of molecular representations, but also the details of atom ordering, types of bonds, and representations of charged species. In addition, we demonstrate the model's ability to reproduce a canonicalization algorithm with a $95.6\%$ success rate.