Martin Ruland the Younger's Lexicon Alchemiae (1612) is one of the most influential alchemical dictionaries of the early modern period, yet its sources and compilation methods remain poorly understood. This study applies computational approaches to investigate the vocabulary underlying Ruland's lexicon and to identify potential textual influences. Using a TEI-XML encoded version of the Lexicon Alchemiae, a standard data format for encoding textual data in the digital humanities, we extract its headwords and compare them against large-scale digital corpora of Latin literature, including the Early Modern Latin Alchemical Prints (EMLAP) dataset and the broader GreLa database. By combining frequency-based lexical comparison with contextual word embeddings generated through a Latin BERT (Bidirectional Encoder Representations from Transformers) model, the analysis traces both the distribution and semantic behaviour of terms across earlier alchemical and scientific texts. The article is accompanied by an interactive web application allowing readers to explore additional case studies. Our analysis indicates that Ruland drew not only on earlier Paracelsian word lists, but also on large-scale contemporary compilations such as Andreas Libavius's Alchemia (1597), suggesting that the Lexicon Alchemiae should be understood within a broader movement to systematise and professionalise alchemical knowledge in the late sixteenth and early seventeenth centuries.
The promise of computational methods opens new epistemic horizons. Yet, as we discuss in this introduction to the special issue on computational approaches to the histories of alchemy and chemistry, it also brings new epistemic responsibilities. We situate this work within a broader digital and computational history, exploring its relationship with earlier traditions of quantitative history as well as with developments in digital humanities and computational humanities. We distinguish between digital, computational, algorithmic, and AI-driven approaches and we clarify methodological and epistemological concerns that keep the historian firmly in the loop. Claims about computational methods often imply that they enable entirely new modes of inquiry. Yet such sweeping claims can also obscure the limitations and conditions under which these methods operate. This introduction therefore explores both the opportunities and the constraints of computational approaches that more celebratory accounts sometimes overlook. It introduces the individual contributions to the special issue and concludes with a call for responsibility in the adoption of computational methods. We argue for making the histories of alchemy and chemistry more critical, global, and reflexive, while remaining attentive to the assumptions, limitations, and implications of the computational tools we employ.
This article demonstrates the power of computational methods to illuminate the history of chemistry across multiple scales - from the micro-level of individual scientific careers to the macro-level of global knowledge systems and geopolitical shifts. Grounded in a tripartite framework integrating the material, semiotic, and social dimensions of chemistry, five cases are analysed. These cases reveal how computational history enables historians to corroborate established narratives while generating new questions through the interplay of digital data, computation, and mathematical models. By examining long-term patterns in substance discovery, the evolution of the periodic system, the linguistic transformation of chemical discourse, the politics of the Nobel Prize, the influence of individual chemists, and the geopolitical reconfiguration of chemical innovation, the study shows how large-scale data analysis can validate historical narratives, uncover structural patterns, test hypotheses, and generate novel research questions. The article concludes by reflecting on the epistemological and methodological implications of the computational history of chemistry, arguing that its practice does not offer definitive answers, but rather reshapes the terrain of historical inquiry - opening new pathways for understanding science as a dynamic, distributed, and historically contingent enterprise.
With digital and computational methods becoming more visible both in scholarly and everyday contexts, it is timely to ask whether a distinct field of digital and computational history of science, knowledge, and technology can meaningfully be said to exist. This epilogue to the special issue on computational approaches to the history of alchemy and chemistry addresses this question, arguing that such an overarching field has not yet materialised. To fulfil its programmatic promises, a digital and computational history of science, knowledge, and technology must focus on conceptual clarity regarding its disciplinary position. If digital and computational approaches are to contribute meaningfully to the history of science and knowledge, they should support efforts to produce more global and inclusive historiographies rather than reproducing existing biases in available datasets and algorithms. Indeed, one of the most important tasks for the future may be the creation of datasets for under-resourced subfields and historical questions. Thus, this paper argues for a commitment to diversity in the subjects, cultures, languages, periods, and forms of knowledge considered: Only through employing a plurality of methods can a computational history of science, knowledge, and technology adequately reflect the diversity of knowledges represented within the broader field.
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.
