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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