iamrp.net-Ulzg-1788905500-o548436950 1788905500 Institute for Advanced Materials Research Press info@iamrp.net Institute for Advanced Materials Research Press Journal of Artificial Intelligence for Materials Science J. Artif. Intell. Mater. Sci. 3149-8957 2023 2 1 Sanjay Kulkarni Meenal Joshi Rohan Patil 2023 10.68159/o548436950 https://iamrp.net/pub/journal/1/article/o548436950 Bartel CJ, Trewartha A, Wang Q, Dunn A, Jain A, Ceder G. A critical examination of compound stability predictions from machine-learned formulas. npj Comput Mater. 2020;6:97. 10.1038/s41524-020-00362-y Goodall REA, Lee AA. Predicting materials properties without crystal structure: deep representation learning from stoichiometry. Nat Commun. 2020;11:6280. 10.1038/s41467-020-19964-7 Cerniauskas G, Sadia H, Alam P. Machine intelligence in metamaterials design: a review. Oxf Open Mater Sci. 2024;4(1):itae001. 10.1093/oxfmat/itae001 Meredig B, Antono E, McCulloch S, Rajan K, Ushijima T, Garratt E. Uncertainty prediction for machine learning models of material properties. ACS Omega. 2021;6(13):8523-35. 10.1021/acsomega.1c03752 Ovchinnikov OS, Gholami S, Rasooli B. Challenges of implementing uncertainty quantification in materials discovery. Front Mater. 2022;9:823700. 10.3389/fmats.2022.823700 Von Lilienfeld OA, Müller KR, Tkatchenko A. Exploring chemical space with machine learning. Nat Rev Chem. 2020;4:347-58. 10.1038/s41570-020-0189-9 Fung V, Ganesh S, Zhang T, Sumpter BG, Taylor V, Urban A, et al. Machine learning the quantum-chemical properties of metal-organic frameworks for accelerated materials discovery. Matter. 2021;4(7):2357-73. 10.1016/j.matt.2021.05.012 Merchant A, Batzner S, Schoenholz SS, Aykol M, Cheon G, Cubuk ED. Scaling deep learning for materials discovery. Nature. 2023;624:80-5. 10.1038/s41586-023-06735-9 Musil F, Grisafi A, Bartók AP, Ortner C, Csányi G, Ceriotti M. Physics-inspired structural representations for molecule and materials. Chem Rev. 2021;121(16):9759-815. 10.1021/acs.chemrev.1c00021 Oviedo F, Ferres JL, Buonassisi T, Butler KT. Interpreting machine learning models for material design. Mater Futures. 2022;1(2):025301. 10.1088/2752-5724/ac5b96 Dunn A, Wang Q, Ganose A, Dopp D, Jain A. Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm. npj Comput Mater. 2020;6:138. 10.1038/s41524-020-00406-3 Chen C, Ong SP. A universal graph deep learning interatomic potential for the periodic table. Nat Comput Sci. 2022;2:718-28. 10.1038/s43588-022-00364-3 Chen C, Ong SP. Atomsets as a hierarchical transfer learning framework for crystal property prediction. npj Comput Mater. 2021;7:83. 10.1038/s41524-021-00558-w Chen C, Ye W, Zuo Y, Zheng C, Ong SP. Graph networks as a universal machine learning framework for molecules and crystals. Chem Mater. 2020;32(12):4954-65. 10.1021/acs.chemmater.0c01907 Korolev V, Nevolin I, Protsenko P. A universal similarity based approach for predictive uncertainty quantification in materials science. Sci Rep. 2022;12(1):14931. 10.1038/s41598-022-19205-5 Wen M, Tadmor EB. Uncertainty quantification in molecular simulations with dropout neural network potentials. npj Comput Mater. 2020;6:124. 10.1038/s41524-020-00390-8 Li L, Chang J, Vakanski A, Wang Y, Yao T, Xian M. Uncertainty quantification in multivariable regression for material property prediction with bayesian neural networks. Sci Rep. 2024;14(1):10543. 10.1038/s41598-024-61189-x Zuo Y, Chen C, Li X, Deng Z, Chen Y, Behler J, et al. Performance and cost assessment of machine learning interatomic potentials. J Phys Chem A. 2020;124(4):731-45. 10.1021/acs.jpca.9b08723 Zhang H, Chen WW, Iyer A, Apley DW, Chen W. Uncertainty-aware mixed-variable machine learning for materials design. Sci Rep. 2022;12(1):19760. 10.1038/s41598-022-23431-2 Perez D, Subramanyam APA, Maliyov I, Swinburne TD. Uncertainty quantification for misspecified machine learned interatomic potentials. npj Comput Mater. 2025;11:263. 10.1038/s41524-025-01758-4 Schwalbe-Koda D, Hamel S, Sadigh B, Zhou F, Lordi V. Model-free estimation of completeness, uncertainties, and outliers in atomistic machine learning using information theory. Nat Commun. 2025;16(1):4014. 10.1038/s41467-025-59232-0 Tom G, Schmid SP, Baird SG, Cao Y, Darvish K, Hao H, et al. Self-driving laboratories for chemistry and materials science. Chem Rev. 2024;124(16):9633-732. 10.1021/acs.chemrev.4c00055 Alampara N, Schilling-Wilhelmi M, Ríos-García M, Mandal I, Khetarpal P, Grover HS, et al. Probing the limitations of multimodal language models for chemistry and materials research. Nat Comput Sci. 2025;5(10):952-61. 10.1038/s43588-025-00836-3 Frey NC, Soklaski R, Axelrod S, Samsi S, Gomez-Bombarelli R, Coley CW, et al. Neural scaling of deep chemical models. Nat Mach Intell. 2023;5:1297-305. 10.1038/s42256-023-00740-3 Tan AR, Urata S, Goldman S, Gómez-Bombarelli R. Single-model uncertainty quantification in neural network potentials does not consistently outperform model ensembles. npj Comput Mater. 2023;9:225. 10.1038/s41524-023-01180-8 Bilbrey JA, Firoz JS, Lee MS, Choudhury S. Uncertainty quantification for neural network potential foundation models. npj Comput Mater. 2025;11:109. 10.1038/s41524-025-01572-y Acharya K, Song H. A comprehensive review of neuro-symbolic AI for robustness, uncertainty quantification, and intervenability. Arab J Sci Eng. 2026;51:35-67. 10.1007/s13369-025-10887-3 Ahangar MN, Farhat ZA, Sivanathan A. AI Trustworthiness in manufacturing: challenges, toolkits, and the path to industry 5.0. Sensors. 2025;25(14):4357. 10.3390/s25174357 Cheimarios N. Scientific software development in the AI era: reproducibility, MLOps, and applications in soft matter physics. Front Phys. 2025;13:1711356. 10.3389/fphy.2025.1711356 Avilov FS, Eremin RA, Budennyy SA, Humonen IS. On the robustness of machine learning models in predicting thermodynamic properties: a case of searching for new quasicrystal approximants. Acta Mater. 2024;265:119620. Nooraiepour M. Bayesian parametric matrix models: principled uncertainty quantification in spectral learning. arXiv preprint arXiv:2509.12406. 2025. 10.48550/arXiv.2509.12406 Jacobs R, Morgan D, Attarian S, Meng J, Shen C, Wu Z, et al. A practical guide for machine learning interatomic potentials – status and future. Curr Opin Solid State Mater Sci. 2025;35:101214. 10.1016/j.cossms.2025.101214