Autonomous materials discovery through a tabula rasa active learning framework

Reproducibility package for the paper: Autonomous materials discovery through a tabula rasa active learning framework. Developing high-dimensional multi-element alloy electrocatalysts is crucial for clean energy, yet the discovery process is fundamentally hindered by the combinatorial explosion of the vast chemical space. Traditional trial-and-error methodologies are prohibitively slow, while current active learning approaches heavily rely on human-engineered features and frequently fall into the exploitation trap of localized optima. Here, we present an autonomous materials discovery framework operating from a tabula rasa state by coupling a descriptor-free surrogate model (PC-BAN) with a neuroscience-inspired acquisition function (LCBDS) that enforces surprise-driven exploration. Within a design space of 6074 quaternary combinations, our framework autonomously identified the absolute global optimum catalyst exhibiting an exceptional overpotential of 206 mV using only 220 queries to an experimental oracle, substantially outperforming traditional baselines and human-guided searches. Contents: PC-BAN model, LCBDS acquisition function, all experiment configs (26 YAML), precomputed results, and journal figures (Fig. 2–5). Note on dataset: The OER experimental data (NiFeCoCe, 6,074 compositions) is not included as it originates from the publicly available supplementary material of Haber et al., Energy Environ. Sci. 2014, 7, 682–688 (DOI: 10.1039/C3EE43683G). Please download from that source and place as data/OER_database.csv before running full experiments. Figures 2–5 can be reproduced without the dataset using the precomputed results.