TY - JOUR A1 - Karafiludis, Stephanos A1 - Standl, Jacob A1 - Ryll, Tom W. A1 - Schwab, Alexander A1 - Prinz, Carsten A1 - Wolf, Jakob B. A1 - Kruschwitz, Sabine A1 - Emmerling, Franziska A1 - Völker, Christoph A1 - Stawski, Tomasz M. T1 - High-Entropy Phosphate Synthesis: Advancements through Automation and Sequential Learning Optimization N2 - Transition metal phosphates (TMPs) are extensively explored for electrochemical and catalytical applications due to their structural versatility and chemical stability. Within this material class, novel high-entropy metal phosphates (HEMPs)─containing multiple transition metals combined into a single-phase structure─are particularly promising, as their compositional complexity can significantly enhance functional properties. However, the discovery of suitable HEMP compositions is hindered by the vast compositional design space and complex or very specific synthesis conditions. Here, we present a data-driven strategy combining automated wet-chemical synthesis with a Sequential Learning App for Materials Discovery (SLAMD) framework (Random Forest regression model) to efficiently explore and optimize HEMP compositions. Using a limited set of initial experiments, we identified multimetal compositions in a single-phase crystalline solid. The model successfully predicted a novel Co0.3Ni0.3Fe0.2Cd0.1Mn0.1 phosphate octahydrate phase, validated experimentally, demonstrating the effectiveness of the machine learning approach. This work highlights the potential of integrating automated synthesis platforms with data-driven algorithms to accelerate the discovery of high-entropy materials, offering an efficient design pathway to advanced functional materials. KW - Metal phosphates KW - High entropy KW - Sequential learning KW - Automated synthesis KW - MAP KW - Random forest KW - Machine learning PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-641554 DO - https://doi.org/10.1021/acs.cgd.5c00549 SN - 1528-7483 VL - 25 IS - 19 SP - 7989 EP - 8001 PB - American Chemical Society (ACS) CY - Washington, DC AN - OPUS4-64155 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Stawski, Tomasz A1 - Karafiludis, Stephanos A1 - Standl, Jakob A1 - Ryll, Tom A1 - Schwab, Alexander A1 - Prinz, Carsten A1 - Wolf, Jakob A1 - Kruschwitz, Sabine A1 - Emmerling, Franziska A1 - Völker, Christoph T1 - High-Entropy Metal Phosphate Synthesis: Advancements through Automation and Sequential Learning Optimization N2 - To accelerate high-entropy metal phosphate (HEMP) discovery, we employed a Random Forest regression model within a SLAMD framework. Trained on limited initial data, the model efficiently explored the vast compositional space to predict a novel five-metal phosphate, which was then successfully synthesized and validated experimentally. T2 - AI4 Materials Science and Testing 2025 CY - Berlin, Germany DA - 06.11.2025 KW - Metal phosphates KW - High-entropy KW - Sequential learning PY - 2025 AN - OPUS4-64686 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -