The Treatment of Absence and Null Results in Materials Machine Learning Literature: A Review Study

iamrp.net-lbTT-1790527237-c772411799 1790527237 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 2022 1 2 The Treatment of Absence and Null Results in Materials Machine Learning Literature: A Review Study Nguyen Thanh Huy Pham Quang Minh Le Thi Bich 2022 10.68159/c772411799 https://iamrp.net/pub/journal/1/article/c772411799 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. Stach E, DeCost B, Kusne AG, Hattrick-Simpers J, Brown KA, Reyes KG, et al. Autonomous experimentation systems for materials development: A community perspective. Matter. 2021;4(9):2702-26. Häse F, Roch LM, Aspuru-Guzik A. Next-generation experimentation with self-driving laboratories. Trends Chem. 2019;1(3):282-91. Montoya JH, Aykol M, Anapolsky A, Gopal CB, Herring PK, Hummelshøj JS, et al. Toward autonomous materials research: Recent progress and future challenges. Appl Phys Rev. 2022;9(1). Gunning D, Stefik M, Choi J, Miller T, Stumpf S, Yang GZ. XAI-Explainable artificial intelligence. Sci Robot. 2019;4(37):eaay7120. Miller T. Explanation in artificial intelligence: Insights from the social sciences. Artif Intell. 2019;267:1-38. Butler KT, Davies DW, Cartwright H, Isayev O, Walsh A. Machine learning for molecular and materials science. Nature. 2018;559(7715):547-55. Stein HS, Gregoire JM. Progress and prospects for accelerating materials science with automated and autonomous workflows. Chem Sci. 2019;10(42):9640-9. Zhong X, Gallagher B, Liu S, Kailkhura B, Hiszpanski A, Han TY. Explainable machine learning in materials science. npj Comput Mater. 2022;8(1):204. Ament S, Amsler M, Sutherland DR, Chang MC, Guevarra D, Connolly AB, et al. Autonomous materials synthesis via hierarchical active learning of nonequilibrium phase diagrams. Sci Adv. 2021;7(51):eabg4930. Oviedo F, Ferres JL, Buonassisi T, Butler KT. Interpretable and explainable machine learning for materials science and chemistry. Acc Mater Res. 2022;3(6):597-607. Adadi A, Berrada M. Peeking inside the black-box: A survey on explainable artificial intelligence (XAI). IEEE Access. 2018;6:52138-60. Guidotti R, Monreale A, Ruggieri S, Turini F, Giannotti F, Pedreschi D. A survey of methods for explaining black box models. ACM Comput Surv. 2018;51(5):1-42. Roscher R, Bohn B, Duarte MF, Garcke J. Explainable machine learning for scientific insights and discoveries. IEEE Access. 2020;8:42200-16. Arrieta AB, Díaz-Rodríguez N, Del Ser J, Bennetot A, Tabik S, Barbado A, et al. Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Inf Fusion. 2020;58:82-115. Kusne AG, Yu H, Wu C, Zhang H, Hattrick-Simpers J, DeCost B, et al. On-the-fly closed-loop materials discovery via Bayesian active learning. Nat Commun. 2020;11(1):5966. Gunning D, Aha D. DARPA’s explainable artificial intelligence (XAI) program. AI Mag. 2019;40(2):44-58. Minh D, Wang HX, Li YF, Nguyen TN. Explainable artificial intelligence: A comprehensive review. Artif Intell Rev. 2022;55(5):3503-68. Raschka S, Mirjalili V. Python machine learning: Machine learning and deep learning with Python, scikit-learn, and TensorFlow 2. Birmingham: Packt Publishing Ltd; 2019. Dix A. Human-computer interaction, foundations and new paradigms. J Vis Lang Comput. 2017;42:122-34. 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. ACM; 2019. p. 1-13. Shi S, Zhang X, Fan W. Explaining the predictions of any image classifier via decision trees. arXiv. 2019:1911.01058. Bender EM, Gebru T, McMillan-Major A, Shmitchell S. On the dangers of stochastic parrots: Can language models be too big? In: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. ACM; 2021. p. 610-23. Schmidt A. Interactive human centered artificial intelligence: A definition and research challenges. In: Proceedings of the 2020 International Conference on Advanced Visual Interfaces. ACM; 2020. p. 1-4. Mittelstadt B, Russell C, Wachter S. Explaining explanations in AI. In: Proceedings of the Conference on Fairness, Accountability, and Transparency. ACM; 2019. p. 279-88. Rudin C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat Mach Intell. 2019;1(5):206-15. Chai C, Li G. Human-in-the-loop techniques in machine learning. IEEE Data Eng Bull. 2020;43(3):37-52.