Page 93 - Read Online
P. 93

Wang et al. J. Mater. Inf. 2026, 6, 1                                              Page 9 of 9





               27.  Vasylenko, A.; Antypov, D.; Gusev, V. V.; Gaultois, M. W.; Dyer, M. S.; Rosseinsky, M. J. Element selection for functional materials
                  discovery by integrated machine learning of elemental contributions to properties. npj. Comput. Mater. 2023, 9, 1072. DOI
               28.  Jha, D.; Ward, L.; Paul, A.; et al. ElemNet: deep learning the chemistry of materials from only elemental composition. Sci. Rep. 2018, 8,
                  17593. DOI PubMed PMC
               29.  Hargreaves, C. J.; Gaultois, M. W.; Daniels, L. M.; et al. A database of experimentally measured lithium solid electrolyte conductivities
                  evaluated with machine learning. npj. Comput. Mater. 2023, 9, 951. DOI
               30.  Kang, Y.; Kim, J. ChatMOF: an artificial intelligence system for predicting and generating metal-organic frameworks using large
                  language models. Nat. Commun. 2024, 15, 4705. DOI PubMed PMC
               31.  Zheng, Y.; Koh, H. Y.; Ju, J.; et al. Large language models for scientific discovery in molecular property prediction. Nat. Mach. Intell.
                  2025, 7, 437-47. DOI
               32.  Butler, K. T.; Davies, D. W.; Cartwright, H.; Isayev, O.; Walsh, A. Machine learning for molecular and materials science. Nature 2018,
                  559, 547-55. DOI PubMed
               33.  Dass KT, Hossain MK, Marasamy L. Highly efficient emerging Ag 2BaTiSe 4 solar cells using a new class of alkaline earth metal-based
                  chalcogenide buffers alternative to CdS. Sci. Rep. 2024, 14, 1473. DOI PubMed PMC
               34.  Talirz, L.; Kumbhar, S.; Passaro, E.; et al. Materials Cloud, a platform for open computational science. Sci. Data. 2020, 7, 299. DOI
                  PubMed PMC
               35.  Curtarolo, S.; Setyawan, W.; Hart, G. L.; et al. AFLOW: an automatic framework for high-throughput materials discovery. Comput.
                  Mater. Sci. 2012, 58, 218-26. DOI
               36.  Sbailò, L.; Fekete, Á.; Ghiringhelli, L. M.; Scheffler, M. The NOMAD Artificial-Intelligence Toolkit: turning materials-science data into
                  knowledge and understanding. npj. Comput. Mater. 2022, 8, 935. DOI
               37.  Szymanski, N. J.; Smith, A.; Daoutidis, P.; Bartel, C. J. Topological descriptors for the electron density of inorganic solids. ACS.
                  Materials. Lett. 2025, 7, 2158-64. DOI
               38.  Hylton-Farrington, C. M.; Remsing, R. C. Dynamic local symmetry fluctuations of electron density in halide perovskites. Chem. Mater.
                  2024, 36, 9442-59. DOI
               39.  Feng, C.; Zhang, Y.; Jiang, B. Efficient sampling for machine learning electron density and its response in real space. J. Chem. Theory.
                  Comput. 2025, 21, 691-702. DOI PubMed
               40.  Xie, T.; Grossman, J. C. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties.
                  Phys. Rev. Lett. 2018, 120, 145301. DOI PubMed



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