iamrp.net-aKon-1791515264-c536696006 1791515264 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 2023 2 2 Ahmed El-Kholy Nour Abdelrahman Karim Hassan Mona Saad 2023 10.68159/c536696006 https://iamrp.net/pub/journal/2/article/c536696006 Himanen L, Geurts A, Foster AS, Rinke P. Data-Driven materials science: Status, challenges, and perspectives. Adv Mater. 2017;29(43). 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). Lee D, Choi J, Lee S, Mitchell B, Lee JH. Data-Driven Design for Metamaterials and Multiscale Systems: A Review. Advanced Materials. 2023;35(48). 10.1002/adma.202305254 Choudhary K, DeCost B, Chen C, Jain A, Tavazza F, Cohn R, et al. Recent advances and applications of deep learning methods in materials science. npj Comput Mater. 2022;8(1). Butler KT, Davies DW, Cartwright H, Isayev O, Walsh A. Machine learning for molecular and materials science. Nature. 2018;559(7715). Ha CS, Yao D, Xu Z, Liu C, Liu H, Elkins D, et al. Rapid inverse design of metamaterials based on prescribed mechanical behavior through machine learning. Nat Commun. 2023;14(1). Xie T, Grossman JC. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties. Phys Rev Lett. 2018;120(14). Schmidt J, Shi J, Borlido P, Chen L, Botti S, Marques MAL. Predicting the thermodynamic stability of solids combining density functional theory and machine learning. Chem Mater. 2017;29(12). Pilania G, Gubernatis JE, Lookman T. Multi-fidelity machine learning models for accurate bandgap predictions of solids. Comput Mater Sci. 2017;129. Gupta V, Choudhary K, DeCost B, Tavazza F, Campbell C, Liao W-k, et al. Structure-aware graph neural network based deep transfer learning framework for enhanced predictive analytics on diverse materials datasets. npj Comput Mater. 2023;9(1). 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). 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. Schütt KT, Sauceda HE, Kindermans PJ, Tkatchenko A, Müller KR. SchNet – A deep learning architecture for molecules and materials. J Chem Phys. 2018;148(24). Park CW, Wolverton C. Developing an improved crystal graph convolutional neural network framework for accelerated materials discovery. Phys Rev Mater. 2020;4(6). Louis SY, 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 Chemistry Chem Phys. 2020;22(32). Hatakeyama-Sato K, Tezuka T, Ujihira T, Oyaizu K. Integrating multiple materials science projects in a single neural network. Commun Mater. 2020;1(1). Merchant A, Batzner S, Schoenholz SS, Aykol M, Cheon G, Cubuk ED. Scaling deep learning for materials discovery. Nature. 2023;624(7991). 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). MacLeod BP, Parlane FGL, Morrissey TD, Häse F, Roch LM, Dvorak KE, et al. Self-driving laboratory for accelerated discovery of thin-film materials. Sci Adv. 2020;6(20). Szymanski NJ, Rendy B, Fei Y, Merckx R, Tan S, Zeng Y, et al. An autonomous laboratory for the accelerated synthesis of novel materials. Nature. 2023;624(7990). Deringer VL, Bartók AP, Bernstein N, Wilkins DM, Ceriotti M, Csányi G. Gaussian process regression for materials and molecules. Chem Rev. 2021;121(16). Li K, DeCost B, Choudhary K, Greenwood M, Hattrick-Simpers J. A critical examination of robustness and generalizability of machine learning prediction of materials properties. npj Comput Mater. 2023;9(1). Kavalsky L, Hegde VI, Muckley E, Johnson MS, Meredig B, Viswanathan V. By how much can closed-loop frameworks accelerate computational materials discovery? Digit Discov. 2023;2(2). Chen C, Zuo Y, Ye W, Li X, Deng Z, Ong SP. A critical review of machine learning of energy materials. Adv Energy Mater. 2020;10(8). Karamad M, Magar R, Shi Y, Siahrostami S, Gates ID, Farhad S. Catalytic activity and stability over nanorod and octahedron-shaped CeO2 supported Pt catalysts for preferential oxidation of CO (PROX). ACS Appl Mater Interfaces. 2020;12(28). Zeni C, Rossi K, Glielmo A, Fekete Á, Gaston N, Baletto F, et al. Building machine learning force fields for nanoclusters. J Chem Phys. 2018;148(23). Glielmo A, Sollich P, De Vita A. Accurate interatomic force fields via machine learning with covariant kernels. Phys RevB. 2017;95(21).