Editorial: special topic on computation-assisted materials screening and design
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s40843-024-2897-6.pdf
Reference22 articles.
1. Yang X, Zhou K, He X, et al. Methods and applications of machine learning in computational design of optoelectronic semiconductors. Sci China Mater, 2024, 67: 1042–1081
2. Ran N, Yin L, Qiu W, et al. Recent advances in machine learning interatomic potentials for cross-scale computational simulation of materials. Sci China Mater, 2024, 67: 1082–1100
3. Gao T, Ji Y, Liu C, et al. Molecular descriptor-enhanced graph neural network for energetic molecular property prediction. Sci China Mater, 2024, 67: 1243–1252
4. Zhang C, Yang B, Peng Z, et al. Machine learning-based prediction of mechanical properties of N-doped γ-graphdiyne. Sci China Mater, 2024, 67: 1129–1139
5. Yang F, Cheng G, Yin WJ. Comparative study of crystal structure prediction approaches based on a graph network and an optimization algorithm. Sci China Mater, 2024, 67: 1273–1281
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1. Computation screening for high-performance photocatalysts in normal spinels;International Journal of Hydrogen Energy;2024-09
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