TY - JOUR A1 - Liu, Yuanbin A1 - Morrow, Joe D. A1 - Ertural, Christina A1 - Fragapane, Natascia L. A1 - Gardner, John L. A. A1 - Naik, Aakash A. A1 - Zhou, Yuxing A1 - George, Janine A1 - Deringer, Volker L. T1 - An automated framework for exploring and learning potential-energy surfaces N2 - Machine learning has become ubiquitous in materials modelling and now routinely enables large-scale atomistic simulations with quantum-mechanical accuracy. However, developing machine-learned interatomic potentials requires high-quality training data, and the manual generation and curation of such data can be a major bottleneck. Here, we introduce an automated framework for the exploration and fitting of potential-energy surfaces, implemented in an openly available software package that we call (‘automatic potential-landscape explorer’). We discuss design choices, particularly the interoperability with existing software architectures, and the ability for the end user to easily use the computational workflows provided. We show wide-ranging capability demonstrations: for the titanium–oxygen system, SiO2, crystalline and liquid water, as well as phase-change memory materials. More generally, our study illustrates how automation can speed up atomistic machine learning in computational materials science. KW - Automation KW - Machine Learning KW - Machine learning potentials KW - Amorphous materials KW - High-throughput KW - Ab initio KW - Materials property prediction PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-639882 DO - https://doi.org/10.1038/s41467-025-62510-6 SN - 2041-1723 VL - 16 IS - 1 SP - 1 EP - 12 PB - Springer Science and Business Media LLC AN - OPUS4-63988 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -