REFERENCES
1. Mazitov, A.; Bigi, F.; Kellner, M.; et al. PET-MAD as a lightweight universal interatomic potential for advanced materials modeling. Nat. Commun. 2025, 16, 10653.
2. Gao, R.; Yam, C.; Mao, J.; Chen, S.; Chen, G.; Hu, Z. A foundation machine learning potential with polarizable long-range interactions for materials modelling. Nat. Commun. 2025, 16, 10484.
3. Yang, C.; Wu, C.; Xie, W.; Xie, D.; Hu, P. General reactive element-based machine learning potentials for heterogeneous catalysis. Nat. Catal. 2025, 8, 891-904.
4. Zeni, C.; Pinsler, R.; Zügner, D.; et al. A generative model for inorganic materials design. Nature 2025, 639, 624-32.
5. Han, Y.; Ding, C.; Wang, J.; et al. Efficient crystal structure prediction based on the symmetry principle. Nat. Comput. Sci. 2025, 5, 255-67.
6. Zhang, Y.; Han, Y.; Chen, S.; et al. Large language models to accelerate organic chemistry synthesis. Nat. Mach. Intell. 2025, 7, 1010-22.
7. Zhang, Z.; Ren, Z.; Hsu, C. W.; et al. A multimodal robotic platform for multi-element electrocatalyst discovery. Nature 2025, 647, 390-6.
8. Jia, X.; Zhou, Z.; Liu, F.; et al. Closed-loop framework for discovering stable and low-cost bifunctional metal oxide catalysts for efficient electrocatalytic water splitting in acid. J. Am. Chem. Soc. 2025, 147, 22642-54.
9. Wang, C.; Wei, X.; van der Zwaag, S.; Wang, Q.; Xu, W. From creep-life prediction to ultra-creep-resistant steel design: an uncertainty-informed machine learning approach. Acta. Mater. 2025, 292, 121073.
10. Guo, G.; Saidi, T. L.; Terban, M. W.; Valsecchi, M.; Billinge, S. J. L.; Lipson, H. Ab initio structure solutions from nanocrystalline powder diffraction data via diffusion models. Nat. Mater. 2025, 24, 1726-34.
