iamrp.net-Q9hz-1791515267-w239501393 1791515267 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 2022 1 1 Ahmed Mansour Omar Saeed 2022 10.68159/w239501393 https://iamrp.net/pub/journal/2/article/w239501393 Ramprasad R, Batra R, Pilania G, Mannodi-Kanakkithodi A, Kim C. Machine learning in materials informatics: Recent applications and prospects. npj Comput Mater. 2017;3(1):54. 10.1038/s41524-017-0056-5 Pilania G, Gubernatis JE, Lookman T. Multi-fidelity machine learning models for accurate bandgap predictions of solids. Comput Mater Sci. 2017;129:156-63. 10.1016/j.commatsci.2016.12.004 Wen M, Tadmor EB. Uncertainty quantification in molecular simulations with dropout neural network potentials. npj Comput Mater. 2020;6(1):124. 10.1038/s41524-020-00390-8 Vu-Bich H, Nguyen-Trung C, Pham D, Le T, Nguyen H, Ngo N, et al. Uncertainty quantification in molecular dynamics studies of the glass transition temperature. Acta Mater. 2017;128:344-52. 10.1016/j.actamat.2017.02.016 Otis R. Uncertainty reduction and quantification in computational thermodynamics. Comput Mater Sci. 2022;212:111590. 10.1016/j.commatsci.2022.111590 Hu Z, Mahadevan S. Uncertainty quantification in prediction of material properties during additive manufacturing. Acta Mater. 2019;176:37-48. 10.1016/j.actamat.2019.06.041 Bassman L, Hexner D, Owen L, Zok F, Hexemer A, Ushizima D, et al. Active learning for accelerated design of layered materials. npj Comput Mater. 2018;4(1):74. 10.1038/s41524-018-0129-7 Zhang L, Lin DY, Wang H, Car R, Weinan E. Active learning of uniformly accurate interatomic potentials for materials simulation. Phys Rev Mater. 2019;3(2):023804. Xue D, Balachandran PV, Hogden J, Theiler J, Xue D, Lookman T. Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design. npj Comput Mater. 2019;5(1):28. 10.1038/s41524-019-0153-8 Wang Z, Jiang C, Liu P, Yang W, Zhao Y, Horstemeyer MF, et al. Uncertainty quantification and reduction in metal additive manufacturing. npj Comput Mater. 2020;6(1):175. 10.1038/s41524-020-00444-x Tran A, Furlan JM, Pagalthivarthi KV, Viswanathan R, Wildey T, Wang Y. eQual: Uncertainty quantification for material property predictions with theory-guided machine learning. Adv Mater. 2022;34(21):2200983. 10.1002/adma.202200983 Wang Z, Jiang C, Liu P, Yang W, Zhao Y, Horstemeyer MF, et al. 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 Zhang L, Lin DY, Wang H, Car R, Weinan E. Active learning of uniformly accurate interatomic potentials for materials simulation. Phys Rev Mater. 2019;3(2):023804. 10.1103/PhysRevMaterials.3.023804 Zheng P, Roungvong W, Dong W, Lopez J, Hu Z. Uncertainty quantification in multiscale simulation of woven fiber composites. Comput Methods Appl Mech Eng. 2018;338:506-32. 10.1016/j.cma.2018.04.024 Honarmandi P, Arróyave R. Uncertainty quantification and propagation in computational materials science and simulation-assisted materials design. Acta Mater. 2020;190:1-14. 10.1016/j.actamat.2020.03.012 Lookman T, Balachandran PV, Xue D, Hogden J, Theiler J. Statistical inference and adaptive design for materials discovery. Curr Opin Solid State Mater Sci. 2017;21(3):121-28. 10.1016/j.cossms.2016.10.002 Ling J, Hutchinson M, Antono E, Paradiso S, Meredig B. High-dimensional materials and process optimization using data-driven experimental design with well-calibrated uncertainty estimates. Integr Mater Manuf Innov. 2017;6(3):207-17. 10.1007/s40192-017-0098-z Pernot P, Cailliez F. A critical review of statistical calibration modelling in property calculation and QSAR. Comput Mater Sci. 2020;173:109473. 10.1016/j.commatsci.2019.109473 Pernot P. Uncertainty quantification for molecular property predictions with graph neural architecture search. Digit Discov. 2022;1(3):266-77. 10.1039/D2DD00006A Zhou T, Gui C, Sun L, Hu Y, Lyu H, Wang Z, et al. Energy applications of ionic liquids: Recent developments and future prospects. Chem Rev. 2023;123(21):12170-253. Fernandez M, Boyd PG, Daff TD, Aghaji MZ, Woo TK. Rapid and accurate machine learning recognition of high performing active sites in metal–organic frameworks for H2 storage. J Mater Chem A. 2018;6(5):2432-42. 10.1039/C7TA09919A Bostanabad R, Kearney T, Tao S, Apley DW, Chen W. Leveraging the nurbs-based parametric geometry representations for uncertainty quantification of geometric models. Comput Aided Des. 2018;104:103-15. 10.1016/j.cad.2018.06.002 Ouyang Y, Bai L, Tian H, Li X, Yuan F. Recent progress of thermal conductive ploymer composites: Al2O3 fillers, properties and applications. Compos Part A Appl Sci Manuf. 2022;152:106685. Dornheim E, Vorberger J, Böhme M, Moldabekov UA, Diessel N, Schlanges M, et al. Density response of the warm dense electron gas: models, methods and experiments. Phys Rep. 2022;956:1-74. 10.1016/j.physrep.2022.01.001 Tran A, Wildey T, McCann S. sMF-BO-2CoGP: A sequential multi-fidelity constrained Bayesian optimization framework for design applications. J Comput Inf Sci Eng. 2020;20(3):031006. 10.1115/1.4045670 Foks NL, Starn JJ, Stengel DE, Yager RM. Uncertainty quantification of environmental performance measures and spatial ranking of watersheds using stochastic watershed modeling. J Hydrol. 2021;598:126259. 10.1016/j.jhydrol.2021.126259 Shang Z, Sun Y. Local sensitivity analysis of multiscale structural reliability using separation distance. Theor Appl Frac Mech. 2020;105:102417. 10.1016/j.tafm.2019.102417 Almufarreh A, Arshad M. Promising emerging technologies for teaching and learning: Recent developments and future challenges. Sustainability. 2023;15(8):6917.