Journal-2026

1 CHEN S, et al., Forward versus inverse design of Al-Si-Mg-Cu alloys targeting peak comprehensive mechanical properties: A comparative study integrating computational thermodynamics and active learning, Acta Materialia,306:121930

2 LI Z, et al., Progress and opportunities for AI in additively manufactured NiTi-based shape memory alloys, Advanced Powder Materials,5,100406

3 JIANG X, et al., Manipulating multi-dimensional crystal defects for ultra-high superelastic stress and excellent shape memory effect in additively manufactured NiTiFe shape memory alloys, Journal of Materials Science & Technology,271,177–188

4 LI Q, et al., Phase equilibria and crystal structures of ternary compounds in the Mg-Gd-Ag system over a composition range of 0-50 at.% Gd at 450 and 500°C, Journal of Magnesium and Alloys,14,101619

5 GAO T, et al., Integrating 3D CNN and phase-field simulation for TiAlN coating property prediction via 3D microstructure, Microstructures,6,2026030

6 YANG D, et al., Ultra-fast design and application of nonheat-treatable integrated die casting aluminum alloys, npj Computational Materials,12:140

7 DENG P, et al., Machine-learning designed Pt-modified aluminate coating with a dual-layer heterostructure oxide scale for exceptional hot-corrosion resistance, Corrosion Science,266,113870

8 WU F, et al., A universal Mn/Fe criterion for Fe-IMCs morphology control in Al-Si casting alloys across a wide range of Fe levels by computational thermodynamics and machine learning, Journal of Materials Research and Technology,42,10497-10509