iamrp.net-poLM-1791483545-k856849605 1791483545 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 1 Fatima Zahra Amrani Youssef Benali 2024 10.68159/k856849605 https://iamrp.net/pub/journal/2/article/k856849605 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. Pilania G. Machine learning in materials science: From explainable predictions to autonomous design. Comput Mater Sci. 2021;193:110360. Jablonka KM, Ongari D, Moosavi SM, Smit B. Big-data science in porous materials: Materials genomics and machine learning. Chem Rev. 2020;120(16):8066-129. Liu Y, Zhao T, Ju W, Shi S. Materials discovery and design using machine learning. J Mater. 2017;3(3):159-77. Chen L, Tran H, Batra R, Kim C, Ramprasad R. Machine learning models for the prediction of energy, forces, and stresses for platinum. npj Comput Mater. 2019;5(1):111. Dunn A, Wang Q, Ganose A, Dopp D, Jain A. Benchmarking materials property prediction methods: The Matbench test set and Automatminer reference algorithm. npj Comput Mater. 2020;6(1):138. Lv C, Chen C, Chen T, Li Q, Liu H, Zhao L, et al. Machine learning: An advanced platform for materials development and state prediction in lithium-ion batteries. Adv Mater. 2022;34(25):2101474. Deringer VL, Bartók AP, Bernstein N, Wilkins DM, Ceriotti M, Csányi G. Gaussian process regression for materials and molecules. Chem Rev. 2021;121(16):10073-141. Noh J, Gu GH, Kim S, Jung Y. Machine-enabled inverse design of inorganic solid materials: Promises and challenges. Chem Sci. 2020;11(19):4871-81. Yan Q, Yu S, Gong W, Zhao Z, Zhang Y, Gong S, et al. Accelerating materials discovery using integrated deep machine learning approaches. J Mater Chem A. 2023;11(10):5084-94. Merchant A, Batzner S, Schoenholz SS, Aykol M, Cheon G, Cubuk ED. Scaling deep learning for materials discovery. Nature. 2023;624(7990):80-5. Vincent-Cuaz C, Flamary R, Corneli M, Vayer T, Courty N. Semi-relaxed Gromov-Wasserstein divergence with applications on graphs. arXiv preprint arXiv:2110.02753. 2021 Oct 6. Fung V, Hu J, Ganesh P, Sumpter BG. Machine learned features from density of states for accurate adsorption energy prediction. Nat Commun. 2021;12(1):88. Choudhary K, DeCost B. Atomistic line graph neural network for improved materials property predictions. npj Comput Mater. 2021;7(1):173. 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. 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. Park CW, Wolverton C. Developing an improved crystal graph convolutional neural network framework for accelerated materials discovery. Phys Rev Mater. 2020;4(6):063801. 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. Lu S, Zhou Q, Ouyang Y, Guo Y, Li Q, Wang J. Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning. Nat Commun. 2018;9(1):3405. Goodall REA, Lee AA. Predicting materials properties without crystal structure: Deep representation learning from stoichiometry. Nat Commun. 2020;11(1):6280. Schmidt J, Shi J, Borlido P, Chen L, Botti S, Marques MAL. Predicting the thermodynamic stability of solids with machine learning. Chem Mater. 2017;29(12):5090-103. Jelenic S, Bindics J, Czermak P, Pillai BR, Ruer M, Hoege C, et al. Composition can buffer protein dynamics within liquid-like condensates. bioRxiv. 2022:2022-05. DeCost B, Hattrick-Simpers JR, Trautt Z, Kusne AG, Campo E, Green ML. Scientific AI in materials science: Apath to a sustainable and scalable paradigm. Mach Learn Sci Technol. 2020;1(3):033001. Lin HY, Lu YJ, Chou HH, Ou KL, Huang BH, Lan WC, et al. Biomimetic ceramic composite: characterization, cell response, and in vivo biocompatibility. Materials. 2021;14(23):7374. Wei J, Chu X, Sun XY, Xu K, Deng HX, Chen J, et al. Machine learning in materials science. InfoMat. 2019;1(3):338-58. Li Y, Li C, Zhou K, Zhang C, Yang J, Zhang X, et al. Methods, progresses, and opportunities of materials informatics. InfoMat. 2023;5(3):e12425. Chan MH, Chan CH, Liu M, Li J, Li X, Hu H, et al. Application of machine learning for advanced material prediction and design. EcoMat. 2022;4(4):e12194. Shi S, Zuo Y, Li X, Li H, Guo X, Zhu X, et al. A review on the applications of graph neural networks in materials science at the atomic scale. Mater Genome Eng Adv. 2024;2(1):e50. Shen ZH, Liu HX, Shen Y, Hu JM, Chen LQ, Nan CW. Machine learning in energy storage materials. Interdiscip Mater. 2022;1(2):175-95.