Page 111 - Read Online
P. 111

Wu et al. J. Mater. Inf. 2025, 5, 15  https://dx.doi.org/10.20517/jmi.2024.67   Page 15 of 15

                    2023, 7, 125403.  DOI
               88.       Satorras, V. G.; Hoogeboom, E.; Welling, M. E(n) equivariant graph neural networks. arXiv 2021, arXiv:2102.09844. Available
                    online: https://doi.org/10.48550/arXiv.2102.09844 (accessed 15 Jan 2025)
               89.       Zhang, X.; Wang, L.; Helwig, J.; et al. Artificial intelligence for science in quantum, atomistic, and continuum systems. arXiv 2023,
                    arXiv:2307.08423. Available online: https://doi.org/10.48550/arXiv.2307.08423 (accessed 15 Jan 2025)
               90.       Zitnick, C. L.; Das, A.; Kolluru, A.; et al. Spherical channels for modeling atomic interactions. arXiv 2022, arXiv:2206.14331.
                    Available online: https://doi.org/10.48550/arXiv.2206.14331 (accessed 15 Jan 2025)
               91.       Passaro, S.; Zitnick, C. L. Reducing SO  convolutions to SO  for efficient equivariant GNNs. arXiv 2023, arXiv:2302.03655.
                                               3              2
                    Available online: https://doi.org/10.48550/arXiv.2302.03655 (accessed 15 Jan 2025)
               92.       Liao, Y. L.; Wood, B.; Das, A.; Smidt, T. EquiformerV2: improved equivariant transformer for scaling to higher-degree
                    representations. arXiv 2023, arXiv:2306.12059. Available online: https://doi.org/10.48550/arXiv.2306.12059 (accessed 15 Jan 2025)
               93.       Hastie, T. J. Generalized additive models. In Statistical models in S, 1st ed.; Routledge, 2017; pp 249-307.  DOI
               94.       Ouyang, R.; Curtarolo, S.; Ahmetcik, E.; Scheffler, M.; Ghiringhelli, L. M. SISSO: a compressed-sensing method for identifying the
                    best low-dimensional descriptor in an immensity of offered candidates. Phys. Rev. Mater. 2018, 2, 083802.  DOI
               95.       Lin, X.; Wang, Y.; Chang, X.; Zhen, S.; Zhao, Z.; Gong, J. High-throughput screening of electrocatalysts for nitrogen reduction
                    reactions accelerated by interpretable intrinsic descriptor. Angew. Chem. Int. Ed. 2023, 135, e202300122.  DOI
               96.       Ding, Z.; Pang, Y.; Ma, A.; et al. Single-atom catalysts based on two-dimensional metalloporphyrin monolayers for electrochemical
                    nitrate reduction to ammonia by first-principles calculations and interpretable machine learning. Int. J. Hydrogen. Energy. 2024, 80,
                    586-98.  DOI
               97.       Shu, W.; Li, J.; Liu, J. X.; et al. Structure sensitivity of metal catalysts revealed by interpretable machine learning and first-principles
                    calculations. J. Am. Chem. Soc. 2024, 146, 8737-45.  DOI
               98.       Su, Y.; Wang, X.; Ye, Y.; et al. Automation and machine learning augmented by large language models in a catalysis study. Chem.
                    Sci. 2024, 15, 12200-33.  DOI  PubMed  PMC
               99.       Liu, X.; Peng, H. Toward next-generation heterogeneous catalysts: empowering surface reactivity prediction with machine learning.
                    Engineering 2024, 39, 25-44.  DOI
               100.      Yang, Z.; Gao, W. Applications of machine learning in alloy catalysts: rational selection and future development of descriptors. Adv.
                    Sci. 2022, 9, e2106043.  DOI  PubMed  PMC
               101.      Ribeiro, M. T.; Singh, S.; Guestrin, C. “Why should I trust you?”: Explaining the predictions of any classifier. arXiv 2016,
                    arXiv:1602.04938. Available online: https://doi.org/10.48550/arXiv.1602.04938 (accessed 15 Jan 2025)
               102.      Van der Maaten L, Hinton G. Visualizing data using t-SNE. J. Mach. Learn. Res. 2008, 9, 2579-605. https://www.jmlr.org/papers/
                    volume9/vandermaaten08a/vandermaaten08a.pdf. (accessed 2025-01-15).
               103.      Lundberg, S.; Lee, S. I. A unified approach to interpreting model predictions. arXiv 2017, arXiv:1705.07874. Available online: https:/
                    /doi.org/10.48550/arXiv.1705.07874 (accessed 15 Jan 2025)
               104.      Omidvar, N.; Wang, S.; Huang, Y.; et al. Explainable AI for optimizing oxygen reduction on Pt monolayer core–shell catalysts.
                    Electrochem. Sci. Adv. 2024, 4, e202300028.  DOI
               105.      Li, Y.; Zhang, X.; Li, T.; Chen, Y.; Liu, Y.; Feng, L. Accelerating materials discovery for electrocatalytic water oxidation via center-
                    environment deep learning in spinel oxides. J. Mater. Chem. A. 2024, 12, 19362-77.  DOI
               106.      Roh, J.; Park, H.; Kwon, H.; et al. Interpretable machine learning framework for catalyst performance prediction and validation with
                    dry reforming of methane. Appl. Catal. B. Environ. 2024, 343, 123454.  DOI
               107.      Ding, R.; Chen, Y.; Chen, P.; et al. Machine learning-guided discovery of underlying decisive factors and new mechanisms for the
                    design of nonprecious metal electrocatalysts. ACS. Catal. 2021, 11, 9798-808.  DOI
               108.      Pillai, H. S.; Li, Y.; Wang, S. H.; et al. Interpretable design of Ir-free trimetallic electrocatalysts for ammonia oxidation with graph
                    neural networks. Nat. Commun. 2023, 14, 792.  DOI  PubMed  PMC
               109.      Zhong, M.; Tran, K.; Min, Y.; et al. Accelerated discovery of CO  electrocatalysts using active machine learning. Nature 2020, 581,
                                                               2
                    178-83.  DOI
               110.      Pablo-García, S.; Morandi, S.; Vargas-Hernández, R. A.; et al. Fast evaluation of the adsorption energy of organic molecules on
                    metals via graph neural networks. Nat. Comput. Sci. 2023, 3, 433-42.  DOI  PubMed  PMC
               111.      Schütt, K. T.; Unke, O. T.; Gastegger, M. Equivariant message passing for the prediction of tensorial properties and molecular
                    spectra. arXiv 2021, arXiv:2102.03150. Available online: https://doi.org/10.48550/arXiv.2102.03150 (accessed 15 Jan 2025)
               112.      Szymanski, N. J.; Rendy, B.; Fei, Y.; et al. An autonomous laboratory for the accelerated synthesis of novel materials. Nature 2023,
                    624, 86-91.  DOI  PubMed  PMC
   106   107   108   109   110   111   112   113   114   115   116