AI-Driven Defect Engineering for Advanced Thermoelectric Materials
Published in None, 2025
AI-Driven Defect Engineering
Recommended citation: None https://advanced.onlinelibrary.wiley.com/doi/full/10.1002/adma.202505642
Published in None, 2025
AI-Driven Defect Engineering
Recommended citation: None https://advanced.onlinelibrary.wiley.com/doi/full/10.1002/adma.202505642
Published in Acta Materialia, 2024
The foundation of the CVM-CALPHAD project
Recommended citation: Chu-Liang Fu et al., 2024 Acta Materialia 277 (2024): 120138. https://www.sciencedirect.com/science/article/pii/S1359645424004890
Published in None, 2024
Polaron Crossover
Recommended citation: None https://arxiv.org/abs/2409.08458
Published in None, 2024
Designing Probe
Recommended citation: None https://www.sciencedirect.com/science/article/abs/pii/S2542529325001336
Published in None, 2021
This 2-page paper discussed the connection between current data-driven machine learning scheme and CALPHAD.
Recommended citation: None https://chuliangfu.github.io/files/how_would_ai_shape_calculation.pdf
Published in Physical Review Materials, 2020
This paper is about the cluster expansion approach for Mg-Sn alloys.
Recommended citation: Wang, K., Cheng, D., Fu, C.L. and Zhou, B.C., 2020. First-principles investigation of the phase stability and early stages of precipitation in Mg-Sn alloys. Physical Review Materials, 4(1), p.013606. https://journals.aps.org/prmaterials/abstract/10.1103/PhysRevMaterials.4.013606
Published in Journal of Physics: Condensed Matter, 2017
This paper is about the electronic transport with the interacting dislon, which is the quantization of dislocations.
Recommended citation: Chu-Liang Fu and Mingda Li 2017 J. Phys.: Condens. Matter 29 325702 https://iopscience.iop.org/article/10.1088/1361-648X/aa7955/meta