iamrp.net-JKzq-1791483544-w946411282 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 2 Sanjay Kulkarni Meenal Joshi Rohan Patil 2022 10.68159/w946411282 https://iamrp.net/pub/journal/2/article/w946411282 Schmidt J, Marques MRG, Botti S, Marques MAL. Recent advances and applications of machine learning in solid-state materials science. npj Comput Mater. 2019;5(1):83. Fung V, Zhang J, Juarez E, Sumpter BG. Benchmarking graph neural networks for materials chemistry. npj Comput Mater. 2021;7(1):84. Musil F, Grisafi A, Bartók AP, Ortner C, Csányi G, Ceriotti M. Physics-Inspired structural representations for molecules and materials. Chem Rev. 2021;121(16):9759-815. 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. Hu J, Stefanov S, Song Y, Omee SS, Louis S-Y, Siriwardane EMD, et al. MaterialsAtlas.org: A materials informatics web app platform for materials discovery and survey of state-of-the-art. npj Comput Mater. 2022;8(1):65. Chen C, Zuo Y, Ye W, Li X, Ong SP. Learning properties of ordered and disordered materials from multi-fidelity data. Nat Comput Sci. 2021;1(1):46-53. Choudhary K, DeCost B. Atomistic line graph neural network for improved materials property predictions. npj Comput Mater. 2021;7(1):185. Reiser P, Neubert M, Eberhard A, Torresi L, Zhou C, Shao C, et al. Graph neural networks for materials science and chemistry. Commun Mater. 2022;3(1):93. Dai M, Demirel MF, Liang Y, Hu J-M. Graph neural networks for an accurate and interpretable prediction of the properties of polycrystalline materials. npj Comput Mater. 2021;7(1):103. Pettersson L, Verdozzi C, Marques MAL. Crystal graph attention networks for the prediction of stable materials. Sci Adv. 2021;7(49):eabi7948. Goodall REA, Lee AA. Predicting materials properties without crystal structure: deep representation learning from stoichiometry. Nat Commun. 2020;11(1):6280. Park CW, Wolverton C. Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture. npj Comput Mater. 2021;7(1):73. Xie T, Grossman JC. crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties. Phys Rev Lett. 2018;120(14):145301. Hatakeyama-Sato K, Oyaizu K. Integrating multiple materials science projects in a single neural network. Commun Mater. 2020;1(1):49. Chen C, Ong SP. A universal graph deep learning interatomic potential for the periodic table. Nat Comput Sci. 2020;2(11):718-28. Chen Z, Wei T, Ruzsinszky A, Stillinger FH, Carter EA, Debenedetti PG. Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials. Nat Mach Intell. 2019;1(3):158-68. Rossi K, Cumby J. Graph representation forecasting of patient's medical conditions: toward a digital twin. Sci Rep. 2021;11(1):18423. Zhong X, Gallagher B, Liu S, Kailkhura B, Hiszpanski A, Han TY-J. Explainable machine learning in materials science. npj Comput Mater. 2022;8(1):204. Zubatyuk R, Smith JS, Leszczynski J, Isayev O. Accurate and transferable multitask prediction of chemical properties with an atoms-in-molecules neural network. Sci Adv. 2019;5(8):eaav6490. Louis S-Y, Zhao Y, Nasiri A, Wang X, Song Y, Liu F, et al. Graph convolutional neural networks with global attention for improved materials property prediction. Phys Chem Chem Phys. 2020;22(32):18141-8. Xie T, Grossman JC. Hierarchical visualization of materials space with graph convolutional neural networks. J Chem Phys. 2018;149(17):174107. Yan K, Liu Q, Yan Z, Wang X, Wu B, Zhu D, et al. Organic reaction mechanism classification using machine learning. Nature. 2021;598(7881):451-6. Jorgensen PB, Jacobsen KW, Schmidt MN. Neural message passing with edge updates for predicting properties of molecules and materials. arXiv preprint arXiv:1806.03146. 2018. Karamad M, Magar R, Shi Y, Siahrostami S, Gates ID, Farhad S. Orbital graph convolutional neural network for material property prediction. Phys Rev Mater. 2020;4(9):093801. Schütt KT, Sauceda HE, Kindermans P-J, Tkatchenko A, Müller K-R. SchNet – A deep learning architecture for molecules and materials. J Chem Theory Comput. 2018;14(6):3151-61.