iamrp.net-ejmA-1791491020-d802121443 1791491020 Institute for Advanced Materials Research Press info@iamrp.net Institute for Advanced Materials Research Press Journal of Computational and Data-Driven Materials Engineering J. Comput. Data-Driven Mater. Eng. 3149-9368 2024 3 2 Ivan Petrov Olga Ivanova Dmitry Smirnov 2024 10.68159/d802121443 https://iamrp.net/pub/journal/2/article/d802121443 Liu X, Zhang J, Pei Z. Machine learning for high-entropy alloys: Progress, challenges and opportunities. Prog Mater Sci. 2023;131:101018. 10.1016/j.pmatsci.2022.101018 Jung SG, Jung G, Cole JM. Predictive modeling of high-entropy alloys and amorphous metallic alloys using machine learning. J Chem Inf Model. 2024;64(19):7313-36. 10.1021/acs.jcim.4c00873 Kamnis S, Delibasis K. High entropy alloy property predictions using a transformer-based language model. Sci Rep. 2025;15(1):11861. 10.1038/s41598-025-95170-z Lakshminarayanan B, Pritzel A, Blundell C. Simple and scalable predictive uncertainty estimation using deep ensembles. In: Advances in Neural Information Processing Systems 30. Red Hook (NY): Curran Associates; 2017. p. 6402-13. Novick A, Nguyen Q, Garnett R, Toberer E, Stevanović V. Simulating high-entropy alloys at finite temperatures: An uncertainty-based approach. Phys Rev Mater. 2023;7(6):063801. 10.1103/PhysRevMaterials.7.063801 Allen M, Arroyave R, Malak R. Deep ensembles for modeling uncertain phase constraints in compositionally graded alloy design. In: Proceedings of the ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Volume 3A: 48th Design Automation Conference. New York: American Society of Mechanical Engineers; 2022. p. V03AT03A028. 10.1115/DETC2022-89091 Rao Z, Tung PY, Xie R, Wei Y, Zhang H, Ferrari A, et al. Machine learning-enabled high-entropy alloy discovery. Science. 2022;378(6615):78-85. 10.1126/science.abo4940 Ren F, Ward L, Williams T, Laws KJ, Wolverton C, Hattrick-Simpers J, et al. Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments. Sci Adv. 2018;4(4):eaaq1566. 10.1126/sciadv.aaq1566 Deshmukh G, Wichrowski NJ, Evangelou N, Ghanekar PG, Deshpande S, Kevrekidis IG, et al. Active learning of ternary alloy structures and energies. npj Comput Mater. 2024;10(1):116. 10.1038/s41524-024-01256-z Kendall A, Gal Y. What uncertainties do we need in Bayesian deep learning for computer vision? In: Advances in Neural Information Processing Systems 30. Red Hook (NY): Curran Associates; 2017. p. 5574-84. Tyralis H, Papacharalampous G. A review of predictive uncertainty estimation with machine learning. Artif Intell Rev. 2024;57(4):94. 10.1007/s10462-023-10698-8 Hoffmann L, Fortmeier I, Elster C. Uncertainty quantification by ensemble learning for computational optical form measurements. Mach Learn Sci Technol. 2021;2(3):035030. 10.1088/2632-2153/ac0495 Singh S, Katiyar NK, Goel S, Joshi SN. Phase prediction and experimental realisation of a new high entropy alloy using machine learning. Sci Rep. 2023;13(1):4811. 10.1038/s41598-023-31461-7 Qi J, Hoyos DI, Poon SJ. Machine learning-based classification, interpretation, and prediction of high-entropy-alloy intermetallic phases. High Entropy Alloys Mater. 2023;1(2):312-26. 10.1007/s44210-023-00017-9 Yang C, Ren C, Jia Y, Wang G, Li M, Lu W. A machine learning-based alloy design system to facilitate the rational design of high entropy alloys with enhanced hardness. Acta Mater. 2022;222:117431. 10.1016/j.actamat.2021.117431 Palmer G, Du S, Politowicz A, Emory JP, Yang X, Gautam A, et al. Calibration after bootstrap for accurate uncertainty quantification in regression models. npj Comput Mater. 2022;8(1):115. 10.1038/s41524-022-00794-8 Scalia G, Grambow CA, Pernici B, Li YP, Green WH. Evaluating scalable uncertainty estimation methods for deep learning-based molecular property prediction. J Chem Inf Model. 2020;60(6):2697-717. 10.1021/acs.jcim.9b00975 Caldeira J, Nord B. Deeply uncertain: Comparing methods of uncertainty quantification in deep learning algorithms. Mach Learn Sci Technol. 2021;2(1):015002. 10.1088/2632-2153/aba6f3 Chang YJ, Jui CY, Lee WJ, Yeh AC. Prediction of the composition and hardness of high-entropy alloys by machine learning: Chang, Jui, Lee, and Yeh. JOM. 2019;71(10):3433-42. 10.1007/s11837-019-03704-4 Liu J, Paisley J, Kioumourtzoglou MA, Coull B. Accurate uncertainty estimation and decomposition in ensemble learning. In: Advances in Neural Information Processing Systems 32. Red Hook (NY): Curran Associates; 2019. p. 8950-61. Yang S, Yee K. Towards reliable uncertainty quantification via deep ensemble in multi-output regression task. Eng Appl Artif Intell. 2024;132:107871. 10.1016/j.engappai.2024.107871 Carrete J, Montes-Campos H, Wanzenböck R, Heid E, Madsen GKH. Deep ensembles vs committees for uncertainty estimation in neural-network force fields: Comparison and application to active learning. J Chem Phys. 2023;158(20):204801. 10.1063/5.0146905 Zhou Y, Yang B. Uncertainty quantification of predicting stable structures for high-entropy alloys using Bayesian neural networks. J Energy Chem. 2023;81:118-24. 10.1016/j.jechem.2023.02.028 Wang X, Xiong W. Uncertainty quantification and composition optimization for alloy additive manufacturing through a CALPHAD-based ICME framework. npj Comput Mater. 2020;6(1):188. 10.1038/s41524-020-00454-9 Tran A, Tranchida J, Wildey T, Thompson AP. Multi-fidelity machine-learning with uncertainty quantification and Bayesian optimization for materials design: Application to ternary random alloys. J Chem Phys. 2020;153(7):074705. 10.1063/5.0015672 Pathrudkar S, Thiagarajan P, Agarwal S, Banerjee AS, Ghosh S. Electronic structure prediction of multi-million atom systems through uncertainty quantification enabled transfer learning. npj Comput Mater. 2024;10(1):175. 10.1038/s41524-024-01305-7 Duignan TT. The potential of neural network potentials. ACS Phys Chem Au. 2024;4(3):232-41. 10.1021/acsphyschemau.4c00004