iamrp.net-3nAv-1793474074-q852961834 1793474074 Institute for Advanced Materials Research Press info@iamrp.net Institute for Advanced Materials Research Press Journal of Artificial Intelligence for Materials Science J. Artif. Intell. Mater. Sci. 3149-8957 2023 2 2 Oliver Grant David Clark Sophia Nguyen 2023 10.68159/q852961834 https://iamrp.net/pub/journal/1/article/q852961834 Hollingsworth SA, Dror RO. Molecular dynamics simulation for all. Neuron. 2018;99(6):1129-43. Lei H, Xie P, Zhang L. Machine learning-assisted multi-scale modeling. J Math Phys. 2023;64(7). Bonaccorso G. Machine learning algorithms: Popular algorithms for data science and machine learning. Birmingham: Packt Publishing; 2018. Butler KT, Davies DW, Cartwright H, Isayev O, Walsh A. Machine learning for molecular and materials science. Nature. 2018;559(7715):547-55. Schmidt J, Marques MR, Botti S, Marques MA. Recent advances and applications of machine learning in solid-state materials science. npj Comput Mater. 2019;5(1):83. Zunger A. Inverse design in search of materials with target functionalities. Nat Rev Chem. 2018;2(4):0121. 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. Shireen Z, Weeratunge H, Menzel A, Phillips AW, Larson RG, Smith-Miles K, et al. A machine learning enabled hybrid optimization framework for efficient coarse-graining of a model polymer. npj Comput Mater. 2022;8(1):224. Giuntoli A, Hansoge NK, van Beek A, Meng Z, Chen W, Keten S. Systematic coarse-graining of epoxy resins with machine learning-informed energy renormalization. npj Comput Mater. 2021;7(1):168. Ricci E, Vergadou N. Integrating machine learning in the coarse-grained molecular simulation of polymers. J Phys Chem B. 2023;127(11):2302-22. Goodall RE, Parackal AS, Faber FA, Armiento R, Lee AA. Rapid discovery of stable materials by coordinate-free coarse graining. Sci Adv. 2022;8(30):eabn4117. Duan K, He Y, Li Y, Liu J, Zhang J, Hu Y, et al. Machine-learning assisted coarse-grained model for epoxies over wide ranges of temperatures and cross-linking degrees. Mater Des. 2019;183:108130. Chan H, Cherukara MJ, Narayanan B, Loeffler TD, Benmore C, Gray SK, et al. Machine learning coarse grained models for water. Nat Commun. 2019;10(1):379. Swinburne TD. Coarse-graining and forecasting atomic material simulations with descriptors. Phys Rev Lett. 2023;131(23):236101. 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. Nyshadham C, Rupp M, Bekker B, Shapeev AV, Mueller T, Rosenbrock CW, et al. Machine-learned multi-system surrogate models for materials prediction. npj Comput Mater. 2019;5(1):51. Lubbers N, Agarwal A, Chen Y, Son S, Mehana M, Kang Q, et al. Modeling and scale-bridging using machine learning: Nanoconfinement effects in porous media. Sci Rep. 2020;10(1):13312. Yang W, Peng L, Zhu Y, Hong L. When machine learning meets multiscale modeling in chemical reactions. J Chem Phys. 2020;153(9). Batra R, Sankaranarayanan S. Machine learning for multi-fidelity scale bridging and dynamical simulations of materials. J Phys Mater. 2020;3(3):031002. Mianroodi JR, Rezaei S, Siboni NH, Xu BX, Raabe D. Lossless multi-scale constitutive elastic relations with artificial intelligence. npj Comput Mater. 2022;8(1):67. Joglekar AS, Thomas AG. Machine learning of hidden variables in multiscale fluid simulation. Mach Learn Sci Technol. 2023;4(3):035049. Ingólfsson HI, Bhatia H, Aydin F, Oppelstrup T, López CA, Stanton LG, et al. Machine learning-driven multiscale modeling: Bridging the scales with a next-generation simulation infrastructure. J Chem Theory Comput. 2023;19(9):2658-75. Kovachki N, Liu B, Sun X, Zhou H, Bhattacharya K, Ortiz M, et al. Multiscale modeling of materials: Computing, data science, uncertainty and goal-oriented optimization. Mech Mater. 2022;165:104156. Alber M, Buganza Tepole A, Cannon WR, De S, Dura-Bernal S, Garikipati K, et al. Integrating machine learning and multiscale modeling—perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences. NPJ Digit Med. 2019;2(1):115. Peng GCY, Alber M, Tepole AB, Cannon WR, De S, Dura-Bernal S, et al. Multiscale modeling meets machine learning: What can we learn? Arch Comput Methods Eng. 2021;28(3):1017-37. 10.1007/s11831-020-09405-5 Yi W, Xiao P, Liu X, Zhao Z, Sun X, Wang J, et al. Recent advances in developing active targeting and multi-functional drug delivery systems via bioorthogonal chemistry. Signal Transduct Target Ther. 2022;7(1):386. de Haan P, Cohen TS, Welling M. Natural graph networks. In: NeurIPS. Red Hook, NY: Curran Associates; 2020. p. 3636-46. Peng J, Schwalbe-Koda D, Akkiraju K, Xie T, Giordano L, Yu Y, et al. Human-and machine-centred designs of molecules and materials for sustainability and decarbonization. Nat Rev Mater. 2022;7(12):991-1009. West AR. Solid state chemistry and its applications. Hoboken, NJ: John Wiley & Sons; 2022.