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RESEARCH PAPER · npj Computational Materials · 2023

Divide and conquer: Machine learning accelerated design of lead-free solder alloys with high strength and high ductility

Qinghua Wei, Bin Cao, Hao Yuan, Youyang Chen, Kangdong You, Shuting Yu, Tixin Yang, Ziqiang Dong, Tong‐Yi Zhang

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Divide and conquer: Machine learning accelerated design of lead-free solder alloys with high strength and high ductilityOriginalPhysics-Informed Machine Learning for Solder Design and Reliability Prediction for Electronics7A framework for computer-aided high performance titanium alloy design based on machine learning9Active learning accelerates the discovery of high strength and high ductility lead-free solder alloys37Machine learning-enabled prediction of high-temperature oxidation resistance for Ni-based alloys29An interpretable predictive framework based on rules and machine learning: rule extraction, validation and adaptive integration with machine learning0Leveraging feature gradient for efficient acquisition function maximization in material composition design3Multi-objective optimization design of process parameters for rare earth hard magnetic materials based on machine learning0Discovering novel lead-free solder alloy by multi-objective Bayesian active learning with experimental uncertainty21Modelling wetting angle of solder on substrate using machine learning approach4Machine learning inverse design of high-strength mid-temperature Ag-based solders8Machine learning-driven design of rare metal doped niobium alloys with enhanced strength and ductility6Interpretable Active Learning Identifies Iron‐Doped Carbon Dots With High Photothermal Conversion Efficiency for Antitumor Synergistic Therapy5Unlocking the black box beyond Bayesian global optimization for materials design using reinforcement learning16Uncertainty-aware multi-objective optimization for high work output and low hysteresis in TiNiCuHfCo shape memory alloys3Novel machine learning driven design strategy for high strength Zn Alloys optimization with multiple constraints21AI-driven point cloud framework for predicting solder joint reliability using 3D FEA data7Active learning-enhanced yttrium oxyhydride thin films for photoinduced insulator-to-metal transitions1AI-aided bi-objective optimization of honeycomb metastructures for enhanced microwave absorption and mechanical resistance5Domain knowledge and interpretable machine learning for designing high performance high‐entropy alloys/graphene composite14AI‐Driven Advances in Sustainable Materials for Green Energy: From Innovation to Lifecycle Management31A new multi-objective optimization design framework for high-temperature alloy steel via machine learning4Bayesian Global Optimization0Design of shape memory alloys with enhanced thermal management properties via adaptively constrained multi-objective optimization8Strength improvement and ductility preservation of Sn-Ag-Cu solder alloy induced by Bi and Mn microalloying3Multi-fidelity learning in materials informatics: methodologies, applications, and outlook2Data-driven design: Interpretive optimization of strength-ductility synergy in as-cast Mg-Gd-Y-Zn-Zr alloys through correlation-guided approaches and data augmentation2A study on surface tension prediction of Sn-based lead-free solder via machine learning algorithms0Machine learning in solder joint engineering: a review of microstructure informatics, reliability prediction, and accelerated design0Synergy of Artificial Intelligence and Materials Science in the Creation of NanoArt Images Based on Metal and Alloy Structures0Machine learning-driven discovery of a Co/Ni-free FeCrMoMnCu binder for Ti(C, N) cermets0Advancing breakthrough design of superior strength-ductility synergy of lead-free solder alloys via interpretable machine learning and iterative optimization0Simultaneously enhancing strength and ductility of multicomponent alloys via a synergistic study of machine learning, DFT modelling and experimental validation1High-strength and ductile high-entropy alloy via expert-trajectory-guided processing0Cross-Alloy Transfer Learning With Transferability Assessment for Mechanical Property Prediction of Lead-Free Solders0Effects of Bi and In on the shear strength and thermal aging behavior of SAC387/Cu solder joints: A machine learning and experimental study1Recent advances in silver-matrix composite electrical contact materials: microstructural design, properties enhancement, and rare-earth modification0

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  2. A framework for computer-aided high performance titanium alloy design based on machine learning

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  3. Active learning accelerates the discovery of high strength and high ductility lead-free solder alloys

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  4. Machine learning-enabled prediction of high-temperature oxidation resistance for Ni-based alloys

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  8. Discovering novel lead-free solder alloy by multi-objective Bayesian active learning with experimental uncertainty

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  9. Modelling wetting angle of solder on substrate using machine learning approach

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  10. Machine learning inverse design of high-strength mid-temperature Ag-based solders

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  12. Interpretable Active Learning Identifies Iron‐Doped Carbon Dots With High Photothermal Conversion Efficiency for Antitumor Synergistic Therapy

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  13. Unlocking the black box beyond Bayesian global optimization for materials design using reinforcement learning

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  14. Uncertainty-aware multi-objective optimization for high work output and low hysteresis in TiNiCuHfCo shape memory alloys

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  15. Novel machine learning driven design strategy for high strength Zn Alloys optimization with multiple constraints

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  16. AI-driven point cloud framework for predicting solder joint reliability using 3D FEA data

    Scientific Reports · 2025-07-07

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  17. Active learning-enhanced yttrium oxyhydride thin films for photoinduced insulator-to-metal transitions

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  18. AI-aided bi-objective optimization of honeycomb metastructures for enhanced microwave absorption and mechanical resistance

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  19. Domain knowledge and interpretable machine learning for designing high performance high‐entropy alloys/graphene composite

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  20. AI‐Driven Advances in Sustainable Materials for Green Energy: From Innovation to Lifecycle Management

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  21. A new multi-objective optimization design framework for high-temperature alloy steel via machine learning

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  22. Bayesian Global Optimization

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  23. Design of shape memory alloys with enhanced thermal management properties via adaptively constrained multi-objective optimization

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  24. Strength improvement and ductility preservation of Sn-Ag-Cu solder alloy induced by Bi and Mn microalloying

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  27. A study on surface tension prediction of Sn-based lead-free solder via machine learning algorithms

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  28. Machine learning in solder joint engineering: a review of microstructure informatics, reliability prediction, and accelerated design

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  35. Effects of Bi and In on the shear strength and thermal aging behavior of SAC387/Cu solder joints: A machine learning and experimental study

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  36. Recent advances in silver-matrix composite electrical contact materials: microstructural design, properties enhancement, and rare-earth modification

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