Machine Learning Design of Tungsten Alloys With Strength–Ductility Synergy

ABSTRACT In the artificial intelligence (AI) era, knowledge‐based machine learning (ML) models can accelerate the design and modification of existing materials toward target properties. Therefore, an integrated framework combining ML with solid solution softening knowledge was introduced to design multielement tungsten alloys with strength–ductility synergy. The chemical short‐range ordered (CSRO) body‐centered cubic (BCC) phases, B32, B2, C11 b , and D0 3 , are chosen as training dataset generated from high‐throughput first‐principle calculations. After meticulous feature engineering and hyperparameter optimization, AdaBoost and Gradient Boosting Regression algorithms are identified as the ML models for describing the thermodynamical and mechanical properties of multielement tungsten alloys. Spanning from binary to quaternary tungsten‐based systems, W–Ta–Re is predicted by ML models and displays promising feature in overcoming the inherent strength‐ductility trade‐off. To validate the ML prediction, a W–10Ta–10Re alloy is synthesized via mechanical alloying and spark plasma sintering. TEM characterization confirms the formation of a uniform solid solution with a strong CSRO tendency. An exceptional compressive strength of 3586.7 MPa and a high work hardening rate are obtained, which surpass current tungsten alloy systems. This study not only establishes a reliable ML pathway for the design of high‐performance tungsten alloys but also provides insights into the role of CSRO.