iamrp.net-x2t9-1788878306-g989274613 1788878306 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 2024 3 2 Nguyen Thanh Huy Le Thi Bich Pham Quang Minh 2024 10.68159/g989274613 https://iamrp.net/pub/journal/1/article/g989274613 Jain A, Ong SP, Hautier G, Chen W, Richards WD, Dacek S, et al. The materials project: A materials genome approach to accelerating materials innovation. APL Mater. 2021;9(3):030902. Draxl C, Scheffler M. The NOMAD laboratory: From data sharing to artificial intelligence. J Phys Mater. 2020;3(4):040302. Chen C, Ye W, Zuo Y, Zheng C, Ong SP. Graph networks as a universal machine learning framework for molecules and crystals. Chem Mater. 2019;31(9):3564–72. Merchant A, Batzner S, Schoenholz SS, Aykol M, Cheon G, Cubuk ED. Scaling deep learning for materials discovery. Nature. 2023;624:80–5. Gawlik A, Balachandran PV. Machine learning in materials science: recent progress and emerging applications. MRS Bull. 2021;46:1–8. Butler KT, Davies DW, Cartwright H, Isayev O, Walsh A. Machine learning for molecular and materials science. Nature. 2018;559:547–55. Frazier PI. A tutorial on Bayesian optimization. https://arxiv.org/pdf/1807.02811. 2018. Settles B. Active learning literature survey. Univ Wisconsin-Madison. 2010. Abdar M, Pourpanah F, Hussain S, Rezazadegan D, Liu L, Ghavamzadeh M, et al. A review of uncertainty quantification in deep learning: Techniques, applications and challenges. Inf Fusion. 2021;76:243–97. Huang B, von Lilienfeld OA. Ab initio machine learning in chemical compound space. Chem Rev. 2021;121(16):10001–36. Rudin C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat Mach Intell. 2019;1:206–15. Molnar C. Interpretable Machine Learning. Leanpub; 2022. Mosqueira-Rey E, Hernández-Pereira E, Alonso-Ríos D, Bobes-Bascarán J, Fernández-Leal Á. Human-in-the-loop machine learning: a state of the art. Artif Intell Rev. 2023;56:3005–54. Wu X, Xiao L, Sun Y, Zhang J, Ma T, He L. A survey of human-in-the-loop for machine learning. Future Gener Comput Syst. 2022;135:364–81. Isayev O, Oses C, Toher C, Gossett E, Curtarolo S, Tropsha A. Universal fragment descriptors for predicting properties of inorganic crystals. Nat Commun. 2020;11:2593. Karniadakis GE, Kevrekidis IG, Lu L, Perdikaris P, Wang S, Yang L. Physics-informed machine learning. Nat Rev Phys. 2021;3:422–40. Brunton SL, Kutz JN. Data-driven science and engineering: Machine learning, dynamical systems, and control. 2nd ed. Cambridge: Cambridge University Press; 2022. Doshi-Velez F, Kim B. Towards a rigorous science of interpretable machine learning. arXiv: Machine Learning; 2017. 10.48550/arXiv.1702.08608 Ghorbani A, Wexler J, Zou J, Kim B. Towards automatic concept-based explanations. Proceedings of the 33rd International Conference on Neural Information Processing Systems; 2019. pp. 9277-86. 10.48550/arXiv.1902.03129 Xu Q, Zhang H, et al. Data-driven materials discovery and design using machine learning. Patterns. 2021;2(11):100332. Lin S, Huang W, et al. Human-in-the-loop learning: A review of methods and applications. ACM Comput Surv. 2021;54(8):1–36. Amershi S, Weld D, Vorvoreanu M, Fourney A, Nushi B, Collisson P, et al. Guidelines for human-ai interaction. In: Proceedings of the 2019 chi conference on human factors in computing systems (chi '19). New York (NY): Association for Computing Machinery; 2019. Paper 3:1-13. 10.1145/3290605.3300233 Arya V, Bellamy RK, Chen PY, Dhurandhar A, Hind M, Hoffman SC, et al. One explanation does not fit all: A toolkit and taxonomy of AI explainability techniques. 2019. 10.48550/arXiv.1909.03012 Zhou Z, et al. Transfer learning in materials informatics: A review. Comput Mater Sci. 2020;186:110014. Jablonka KM, Ongari D, Moosavi SM, Smit B. Big-data science in porous materials: materials genomics and machine learning. Chem Rev. 2020;120(16):8066–129. Moosavi SM, Jablonka KM, Smit B. The role of machine learning in the understanding and design of materials. J Am Chem Soc. 2020;142(48):20273–87. Ward L, Wolverton C. Atomistic calculations and materials informatics: A review. Curr Opin Solid State Mater Sci. 2020;24(3):100805. Xie T, Grossman JC. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties. Phys Rev Lett. 2020;120:145301. Chen S, et al. A critical review of machine learning of defects in materials. Prog Mater Sci. 2022;127:100951. Goodall REA, Lee AA. Predicting materials properties without crystal structure: deep representation learning from stoichiometry. Nat Commun. 2020;11:6280. Tshitoyan V, Dagdelen J, Weston L, Dunn A, Rong Z, Kononova O, et al. Unsupervised word embeddings capture latent knowledge from materials science literature. Nature. 2019;571(7763):95-8. 10.1038/s41586-019-1335-8 Himanen L, Geurts A, Foster AS, Rinke P. Data-driven materials science: status, challenges, and perspectives. Adv Sci. 2020;7(21):1900808. Wang AY-T, Murdock R, Kauwe S, Oliynyk AO, Gurlo A, Brgoch J, et al. Machine learning for materials scientists: An introductory guide toward best practices. Chem Mater. 2020;32(12):4954–65. Yang Z, et al. A perspective on active learning in materials science. NPJ Comput Mater. 2021;7:1–11. Lookman T, Balachandran PV, Xue D, Yuan R. Active learning in materials science with emphasis on adaptive sampling using uncertainties. NPJ Comput Mater. 2022;8:1–13.