Computational History of Chemistry – Part 1: Challenges and Opportunities

In this article, I argue that the computational history of chemistry offers a new way of engaging with the past: one that integrates narrative inquiry with algorithmic analysis, mathematical modelling, and large-scale data processing. I demonstrate how computational methods – distinguished by their digital foundations, algorithmic procedures, and computational purpose – can reveal patterns, model transformations, and test historical hypotheses across the social, semiotic, and material dimensions of chemistry. Crucially, I show how these methods allow historians to explore the interplay between history at different scales, from the micro to the macro: linking individual actors, local practices, and contested narratives with the longue durée of chemical knowledge. Yet I also reflect on the challenges that arise – particularly the need for historically meaningful categories, the risk of decontextualising data, and the persistence of bias in both sources and algorithms. By integrating historical narrative with computational analysis, I argue that the computational history of chemistry offers a critical and pluralist framework to reassess the unfolding of chemistry and to enrich its historiography for the digital age.