2025/07/01 by Jack Foxabbott, Foxabbott, Jack, Arush Tagade +14 · 1 citation
Computer Science · Decision Sciences · Materials Science · #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #H.2.8 #I.2.3 #I.2.6 #I.5.1 #J.2 #J.3 #J.4 #Machine Learning (cs.LG) #Machine Learning in Materials Science #Scientific Computing and Data Management
paper · pdf · doi:10.48550/arxiv.2507.00964
openalex publication_date 2025/07/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The Discovery Engine is a general purpose automated system for scientific discovery, which combines machine learning with state-of-the-art ML interpretability to enable rapid and robust scientific insight across diverse datasets. In this paper, we benchmark the Discovery Engine against five recent peer-reviewed scientific publications applying machine learning across medicine, materials science, social science, and environmental science. In each case, the Discovery Engine matches or exceeds prior predictive performance while also generating deeper, more actionable insights through rich interpretability artefacts. These results demonstrate its potential as a new standard for automated, interpretable scientific modelling that enables complex knowledge discovery from data.