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材料科学
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经济学与金融学
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固体物理学 结构化学 群论 群表示论 量子力学 量子化学 材料科学基础 计算材料科学
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计算材料科学论文


26. Deringer, VL; Caro, MA and Csanyi, G. Machine Learning Interatomic Potentials as Emerging Tools for Materials Science. 2019. pdf

25. Machine-learned potentials for next-generation matter simulations. Nature Materials volume 20, pages750–761 (2021), pdf

24. Recent progress of the Computational 2D Materials Database (C2DB) 2D Mater. 8 (2021) 044002. pdf

23. Promises and perils of computational materials databases. Nature Computational Science. 14 January 2021 pdf

22. Carvalho, R.P., Marchiori, C.F.N., Brandell, D., Araujo, C.M. Artificial intelligence driven in-silico discovery of novel organic lithium-ion battery cathodes. Energy Storage Mater 2022, 44, 313-25. pdf

21. Lv, C.D., Zhou, X., Zhong, L.X., Yan, C.S., Srinivasan, M., Seh, Z.W., et al. Machine Learning: An Advanced Platform for Materials Development and State Prediction in Lithium-Ion Batteries. Adv Mater, 2022. 17. pdf

20. Chen, X., Liu, X.Y., Shen, X., Zhang, Q. Applying Machine Learning to Rechargeable Batteries: From the Microscale to the Macroscale. Angewandte Chemie-International Edition 2021, 60, 24354-66. pdf

19. Yang, W.H., Siriwardane, E.M.D., Dong, R.Z., Li, Y.X., Hu, J.J. Crystal structure prediction of materials with high symmetry using differential evolution. J Phys-Condes Matter 2021, 33, 11.

18. Wang, Z.L., Wang, Q.X., Han, Y.Q., Ma, Y., Zhao, H., Nowak, A., et al. Deep learning for ultra-fast and high precision screening of energy materials. Energy Storage Mater 2021, 39, 45-53. pdf

17. Mishin, Y. Machine-learning interatomic potentials for materials science. Acta Mater 2021, 214, 16. pdf

16. Jeschke, S., Johansson, P. Supervised Machine Learning-Based Classification of Li-S Battery Electrolytes. Batteries Supercaps 2021, 4, 1156-62 pdf.

15. Wang, Z.L., Cai, J.F., Wang, Q.X., Wu, S.C., Li, J.J. Unsupervised discovery of thin-film photovoltaic materials from unlabeled data. npj Comput Mater 2021, 7, 11. pdf

14. Gao, C.C., Min, X., Fang, M.H., Tao, T.Y., Zheng, X.H., Liu, Y.G., et al. Innovative Materials Science via Machine Learning. Adv Funct Mater, 14. pdf

13. Han, Y.Q., Ali, I., Wang, Z.L., Cai, J.F., Wu, S.C., Tang, J.Q., et al. Machine learning accelerates quantum mechanics predictions of molecular crystals. Phys Rep-Rev Sec Phys Lett 2021, 934, 1-71.| pdf

12. Wang, Z.L., Lin, X.R., Han, Y.Q., Cai, J.F., Wu, S.C., Yu, X., et al. Harnessing artificial intelligence to holistic design and identification for solid electrolytes. Nano Energy 2021, 89, 12. pdf

11. Esser, B., Dolhem, F., Becuwe, M., Poizot, P., Vlad, A., Brandell, D. A perspective on organic electrode materials and technologies for next generation batteries. Journal Of Power Sources 2021, 482. pdf

10. Mao, J.L., Miao, J.Z., Lu, Y.Y., Tong, Z.M. Machine learning of materials design and state prediction for lithium ion batteries. Chin J Chem Eng 2021, 37, 1-11. pdf

9. Yang, Z.H., Wang, F., Hu, Z.J., Chu, J., Zhan, H., Ai, X.P., et al. Room-Temperature All-Solid-State Lithium-Organic Batteries Based on Sulfide Electrolytes and Organodisulfide Cathodes. Adv Energy Mater, 2021. 10. pdf

8. Gao, X.L., Liu, X.H., He, R., Wang, M.Y., Xie, W.L., Brandon, N.P., et al. Designed high-performance lithium-ion battery electrodes using a novel hybrid model-data driven approach. Energy Storage Mater 2021, 36, 435-58. pdf

7. Liu, Y., Guo, B.R., Zou, X.X., Li, Y.J., Shi, S.Q. Machine learning assisted materials design and discovery for rechargeable batteries. Energy Storage Mater 2020, 31, 434-50. pdf

6. Pinheiro, G.A., Mucelini, J., Soares, M.D., Prati, R.C., Da Silva, J.L.F., Quiles, M.G. Machine Learning Prediction of Nine Molecular Properties Based on the SMILES Representation of the QM9 Quantum-Chemistry Dataset. J Phys Chem A 2020, 124, 9854-66.

5. Podryabinkin, E.V., Tikhonov, E.V., Shapeev, A.V., Oganov, A.R. Accelerating crystal structure prediction by machine-learning interatomic potentials with active learning. Phys Rev B 2019, 99, 7.

4. Juan, Y.F., Dai, Y.B., Yang, Y., Zhang, J. Accelerating materials discovery using machine learning. J Mater Sci Technol 2021, 79, 178-90. pdf

3. Allam, O., Cho, B.W., Kim, K.C., Jang, S.S. Application of DFT-based machine learning for developing molecular electrode materials in Li-ion batteries. Rsc Advances 2018, 8, 39414-20. pdf

2. Ahmad, Z., Xie, T., Maheshwari, C., Grossman, J.C., Viswanathan, V. Machine Learning Enabled Computational Screening of Inorganic Solid Electrolytes for Suppression of Dendrite Formation in Lithium Metal Anodes. Acs Central Science 2018, 4, 996-1006. pdf

1. Ramprasad, R., Batra, R., Pilania, G., Mannodi-Kanakkithodi, A., Kim, C. Machine learning in materials informatics: recent applications and prospects. npj Comput Mater 2017, 3, 13. pdf


 

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