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  -