Graph Neural Networks for Materials Property Prediction: A Decadal Review of Advances and Limits

iamrp.net-Q7Kr-1791483544-e795301159 1791483544 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 Graph Neural Networks for Materials Property Prediction: A Decadal Review of Advances and Limits Daniel Fischer Laura Meier Thomas Braun 2022 10.68159/e795301159 https://iamrp.net/pub/journal/2/article/e795301159 Fung V, Zhang J, Juarez E, Sumpter BG. Benchmarking graph neural networks for materials chemistry. npj Comput Mater. 2021;7(84). Chen C, Ong SP. Atomistic line graph neural network for improved materials property predictions. npj Comput Mater. 2021;7(201). Sun X, Kresse G, Mishin Y. Graph neural networks for an accurate and interpretable prediction of the properties of polycrystalline materials. npj Comput Mater. 2021;7(103). 10.1038/s41524-021-00574-z Choudhary K, DeCost B. Atomistic line graph neural network for improved materials property predictions. npj Comput Mater. 2021;7(185). Iwata S, Chen W, Wang Y. Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture. npj Comput Mater. 2021;7(73). Lee J, Asahi R. Transfer learning for materials informatics using crystal graph convolutional neural network. Comput Mater Sci. 2021;190:110314. 10.1016/j.commatsci.2021.110314 Schwarzer M, Rogan B, Ruan Y, Song Z, Lee R, Stein E, et al. Learning to fail: Predicting fracture evolution in brittle material models using recurrent graph convolutional neural networks. Comput Mater Sci. 2019;162:322-32. Dai M, Demirel MF, Liu X, Liang Y, Guo J-G. Graph neural network for predicting the effective properties of polycrystalline materials: A comprehensive analysis. Comput Mater Sci. 2022;201:110878. Mandal R, Casert C, Sollich P. Robust prediction of force chains in jammed solids using graph neural networks. Nat Commun. 2022;13(3605). Rosen AS, Iyer SM, Ray D, Yao Z, Aspuru-Guzik A, Gagliardi L, et al. Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery. Matter. 2021;4(5):1578-97. Shao Y, Zhang L, Chen C, Dwaraknath S, Persson KA, Horton M. AI applications through the whole life cycle of material discovery. Matter. 2020;3(2):393-415. Cao Y, Lee S-J, Kluender EJ, Sun C-J, Hwang J, Betts JB, et al. Neural network-assisted development of high-entropy alloy catalysts: Decoupling ligand and coordination effects. Matter. 2020;3(3):793-803. Ganose AM, Jain A. Inverse design of solid-state materials via a continuous representation. Matter. 2019;1(6):1487-1500. Trofimov A, Davis AM, Soldatov DV, Dylov DV. The Convergence of machine learning and microfluidics in materials science and biomedicine. Matter. 2021;4(3):781-802. Buehler MJ. Using deep learning to predict fracture patterns in crystalline solids. Matter. 2020;3(1):197-214. Choudhary K, Yildirim T, Siderius DW, Kusne AG, McDannold A, Ortiz-Montalvo DL. Graph neural network predictions of metal organic framework CO2 adsorption properties. npj Comput Mater. 2022;8(88). 10.1016/j.commatsci.2022.111388 Gu GH, Jang J, Jung Y. Perovskite synthesizability using graph neural networks. npj Comput Mater. 2022;8(71). Gibson J, Hire A, Hennig RG. Data-augmentation for graph neural network learning of the relaxed energies of unrelaxed structures. npj Comput Mater. 2022;8(158). Almufarreh A, Arshad M. Promising emerging technologies for teaching and learning: Recent developments and future challenges. Sustainability. 2023;15(8):6917. 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(21):1-17. 10.1038/s41524-019-0153-8 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. Honarmandi P, Arróyave R. Uncertainty quantification and propagation in computational materials science and simulation-assisted materials design. Acta Mater. 2020;190:1-14. 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. Ouyang Y, Bai L, Tian H, Li X, Yuan F. Recent progress of thermal conductive ploymer composites: Al2O3 fillers, properties and applications. Composites Part A Appl Sci Manuf. 2022;152:106685.