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◆ npj Advanced Manufacturing2026-06-23· Active learning (machine learning)

Active learning for the accelerated discovery of complex concentrated NiCoCr alloys in additive manufacturing

Ajay Talbot, Soumya S. Dash, Jialu Li, Changjun Cheng, Madhi Ramesh, Xiao Shang, Jiahui Zhang, Cristian Cojocaru, Jason Hattrick-Simpers, Yu Zou

原始摘要(英文原文)· Original abstract
Many conventional alloys are poorly suited to laser-based additive manufacturing (also known as 3D printing) because of complex laser-material interactions and thermal histories. Identifying new printable alloys out of vast compositional spaces is a daunting task, particularly for complex concentrated alloys of three or more elements. Here, we integrate active learning with additive manufacturing to accelerate the discovery of NiCoCr complex concentrated alloys via a high-throughput highway. We identify six new printable alloys that are up to ~40% harder than the popular equiatomic NiCoCr at room temperature. Among them, two new alloys exhibit complementary advantages: Ni 12 Co 62 Cr 26 retains ~50% higher hardness than NiCoCr at 600 °C, whereas Ni 36 Co 14 Cr 50 reduces oxidation mass gain by 85% at 1000 °C compared with conventional superalloys. Our study demonstrates that optimum performance can reside far from equiatomic proportions and can be uncovered with substantially fewer experiments when exploration is guided by application-specific objectives. The modular workflow provides a general route to rapidly discover new printable, alloys for targeted demanding service conditions.
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Active learning for the accelerated discovery of complex concentrated NiCoCr alloys in additive manufacturing — 科研速览 Science Skim