|
[1]
|
Wang, G., Liu, H., Song, K., Zhou, Y., Cheng, C., Guo, H., et al. (2022) Aging Process and Strengthening Mechanism of Cu-Cr-Ni Alloy with Superior Stress Relaxation Resistance. Journal of Materials Research and Technology, 19, 3579-3591. [Google Scholar] [CrossRef]
|
|
[2]
|
Zhou, S., Lei, Q., Yin, J., Liu, W., Yan, X., Hu, H., et al. (2025) Effect of Si Addition on the Microstructure and Mechanical Properties of a Cu-Cr-Ag Alloy with High Strength and Electrical Conductivity. Materials Science and Engineering: A, 934, Article 148310. [Google Scholar] [CrossRef]
|
|
[3]
|
Liu, S., Bo, X., Liu, Z. and Geng, Y. (2024) Investigation of Vacuum Arc Ignition Characteristics and Erosion Morphology Microstructure Characteristics of CuCr55 Electrode Contact Materials. AIP Advances, 14, Article 085203. [Google Scholar] [CrossRef]
|
|
[4]
|
Zhou, K., Zhao, Y., Dong, H., Mao, Q., Jin, S., Feng, M., et al. (2024) Fractal Structure and Nano-Precipitates Break Comprehensive Performance Limits of Cucrzr Alloys. Nano Today, 56, Article 102234. [Google Scholar] [CrossRef]
|
|
[5]
|
Pan, S., Yu, J., Han, J., Zhang, Y., Peng, Q., Yang, M., et al. (2023) Customized Development of Promising Cu-Cr-Ni-Co-Si Alloys Enabled by Integrated Machine Learning and Characterization. Acta Materialia, 243, Article 118484. [Google Scholar] [CrossRef]
|
|
[6]
|
Ma, M., Xiao, Z., Meng, X., Li, Z., Gong, S., Dai, J., et al. (2022) Effects of Trace Calcium and Strontium on Microstructure and Properties of Cu-Cr Alloys. Journal of Materials Science & Technology, 112, 11-23. [Google Scholar] [CrossRef]
|
|
[7]
|
Li, J., Ding, H., Li, B., and Wang, L. (2021) Microstructure Evolution and Properties of a Cu-Cr-Zr Alloy with High Strength and High Conductivity. Materials Science and Engineering: A, 819, Article 141464. [Google Scholar] [CrossRef]
|
|
[8]
|
Cai, H., Li, Y., Liu, S., Zeng, T., Huang, Y., Gong, S., et al. (2024) Ameliorating the Microstructure and Properties of a Cu Cr Alloy by Introducing a High-Temperature Short-Time Treatment into the Thermo-Mechanical Process. Materials Characterization, 217, Article 114463. [Google Scholar] [CrossRef]
|
|
[9]
|
Xiong, H., Ma, Y., Zhang, H. and Chen, L. (2022) Design of Cu-Cr Alloys with High Strength and High Ductility Based on First-Principles Calculations. Metals, 12, Article 1406. [Google Scholar] [CrossRef]
|
|
[10]
|
Zhang, J., Zhang, Y., Wang, A., Liang, T., Mao, Z., Su, B., et al. (2023) Insight into the Influence of Alloying Elements on the Elastic Properties and Strengthening of Copper: A High-Throughput First-Principles Calculations. Metals, 13, Article 875. [Google Scholar] [CrossRef]
|
|
[11]
|
Behler, J. and Parrinello, M. (2007) Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces. Physical Review Letters, 98, Article 146401. [Google Scholar] [CrossRef]
|
|
[12]
|
Bartók, A.P., Payne, M.C., Kondor, R. and Csányi, G. (2010) Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons. Physical Review Letters, 104, Article 136403. [Google Scholar] [CrossRef]
|
|
[13]
|
Thompson, A.P., Swiler, L.P., Trott, C.R., Foiles, S.M. and Tucker, G.J. (2015) Spectral Neighbor Analysis Method for Automated Generation of Quantum-Accurate Interatomic Potentials. Journal of Computational Physics, 285, 316-330. [Google Scholar] [CrossRef]
|
|
[14]
|
Novikov, I.S., Gubaev, K., Podryabinkin, E.V. and Shapeev, A.V. (2021) The Mlip Package: Moment Tensor Potentials with Mpi and Active Learning. Machine Learning: Science and Technology, 2, Article 025002. [Google Scholar] [CrossRef]
|
|
[15]
|
Wang, H., Zhang, L., Han, J., et al. (2018) Deepmd-Kit: A Deep Learning Package for Many-Body Potential Energy Representation and Molecular Dynamics. Computer Physics Communications, 228, 178-184. [Google Scholar] [CrossRef]
|
|
[16]
|
Drautz, R. (2019) Atomic Cluster Expansion for Accurate and Transferable Interatomic Potentials. Physical Review B, 99, Article 014104. [Google Scholar] [CrossRef]
|
|
[17]
|
Fan, Z., Zeng, Z., Zhang, C., Wang, Y., Song, K., Dong, H., et al. (2021) Neuroevolution Machine Learning Potentials: Combining High Accuracy and Low Cost in Atomistic Simulations and Application to Heat Transport. Physical Review B, 104, Article 104309. [Google Scholar] [CrossRef]
|
|
[18]
|
Daw, M.S. and Baskes, M.I. (1984) Embedded-Atom Method: Derivation and Application to Impurities, Surfaces, and Other Defects in Metals. Physical Review B, 29, 6443-6453. [Google Scholar] [CrossRef]
|
|
[19]
|
Song, K., Zhao, R., Liu, J., Wang, Y., Lindgren, E., Wang, Y., et al. (2024) General-Purpose Machine-Learned Potential for 16 Elemental Metals and Their Alloys. Nature Communications, 15, Article No. 10208. [Google Scholar] [CrossRef] [PubMed]
|
|
[20]
|
Liang, T., Xu, K., Lindgren, E., Chen, Z., Zhao, R., Liu, J., et al. (2025) NEP89: Universal Neuroevolution Potential for Inorganic and Organic Materials across 89 Elements. [Google Scholar] [CrossRef]
|
|
[21]
|
Xu, K., Bu, H., Pan, S., Lindgren, E., Wu, Y., Wang, Y., et al. (2025) Gpumd 4.0: A High‐Performance Molecular Dynamics Package for Versatile Materials Simulations with Machine‐Learned Potentials. Materials Genome Engineering Advances, 3, e70028. [Google Scholar] [CrossRef]
|
|
[22]
|
Stukowski, A. (2010) Visualization and Analysis of Atomistic Simulation Data with Ovito—The Open Visualization Tool. Modelling and Simulation in Materials Science and Engineering, 18, Article 015012. [Google Scholar] [CrossRef]
|
|
[23]
|
Kresse, G. and Furthmüller, J. (1996) Efficient Iterative Schemes for Ab Initio Total-Energy Calculations Using a Plane-Wave Basis Set. Physical Review B, 54, 11169-11186. [Google Scholar] [CrossRef]
|
|
[24]
|
Blöchl, P.E. (1994) Projector Augmented-Wave Method. Physical Review B, 50, 17953-17979. [Google Scholar] [CrossRef]
|
|
[25]
|
Togo, A. (2023) First-Principles Phonon Calculations with Phonopy and Phono3py. Journal of the Physical Society of Japan, 92, Article 012001. [Google Scholar] [CrossRef]
|
|
[26]
|
Wang, V., Xu, N., Liu, J.C., Tang, G. and Geng, W.T. (2021) Vaspkit: A User-Friendly Interface Facilitating High-Throughput Computing and Analysis Using Vasp Code. Computer Physics Communications, 267, Article 108033. [Google Scholar] [CrossRef]
|
|
[27]
|
Lindgren, E., Rahm, M., Fransson, E., Eriksson, F., Österbacka, N., Fan, Z., et al. (2024) Calorine: A Python Package for Constructing Andsampling Neuroevolution Potential Models. Journal of Open Source Software, 9, Article 6264. [Google Scholar] [CrossRef]
|
|
[28]
|
Van De Walle, A., Tiwary, P., De Jong, M., Olmsted, D.L., Asta, M., Dick, A., et al. (2013) Efficient Stochastic Generation of Special Quasirandom Structures. Calphad, 42, 13-18. [Google Scholar] [CrossRef]
|