High-entropy alloys are promising advanced materials for demanding applications, but discovering useful compositions is difficult and expensive due to the vast number of possible element combinations. Now, researchers have developed a novel AI-driven framework that integrates experimental data, computational modeling, and cross-disciplinary expert knowledge extracted from scientific literature. By combining these sources in a way that accounts for uncertainty, their approach can make reliable predictions even for poorly studied alloy compositions, outperforming conventional data-driven machine learning methods that rely on training data alone.
