Scaling Discovery Infrastructures: Computational Resource Allocation in Materials Engineering and Computational Data

iamrp.net-ZW3z-1791515266-b688694306 1791515266 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 2 Scaling Discovery Infrastructures: Computational Resource Allocation in Materials Engineering and Computational Data Carlos Vega Maria Hernandez 2024 10.68159/b688694306 https://iamrp.net/pub/journal/2/article/b688694306 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, Gubernatis JE, Lookman T. Multi-fidelity machine learning models for accurate bandgap predictions of solids. Comput Mater Sci. 2017;129:156-63. Xue H, Cheng G, Yin WJ. Computational design of energy‐related materials: From first‐principles calculations to machine learning. Wiley Interdiscip Rev Comput Mol Sci. 2024;14(5):e1732. Kaufmann K, Maryanovsky D, Mellor WM, Zhu C, Rosengarten AS, Vecchio KS. Discovery of high-entropy ceramics via closed-loop optimization using artificial intelligence. npj Comput Mater. 2020;6(1):42. 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):5090-103. 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):1903242. Kim C, Batra R, Chen L, Tran H, Ramprasad R. Polymer design using genetic algorithm and machine learning. Comput Mater Sci. 2021;186:110067. Tran H, Mannodi-Kanakkithodi A, Kim C, Sharma V, Pilania G, Ramprasad R. Machine-learning predictions of polymer properties with Polymer Genome. J Appl Phys. 2020;128(17):171104. Sun S, Hartono NTP, Ren ZD, Oviedo F, Buscemi AM, Layurova M, et al. Accelerated development of perovskite-inspired materials via high-throughput synthesis and machine-learning diagnosis. Joule. 2019;3(6):1437-51. 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. Merchant A, Batzner S, Schoenholz SS, Aykol M, Cheon G, Cubuk ED. Scaling deep learning for materials discovery. Nature. 2023;624:80-5. 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. 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):5966. Choudhary K, Kalish I, Beams R, Tavazza F. High-throughput assessment of vacancy formation and surface energies of materials using graph neural networks. Phys Rev Mater. 2021;5(1):013803. Fung V, Zhang J, Juarez E, Sumpter BG. Benchmarking graph neural networks for materials chemistry. npj Comput Mater. 2021;7(1):84. Fulginiti D, Abraham Y, Florentin L, Bernadou S. Machine learning-driven discovery of key descriptors for CO2 activation on bimetallic transition metal carbides. Digit Discov. 2022;1(3):320-8. Saidi WA, Castelli IE, Wisesa P, Jain A. Autonomous materials discovery driven by Gaussian process regression with inhomogeneous measurement noise and anisotropic kernels. Sci Rep. 2020;10(1):17059. Liu C, Zhang Y, Zhang T, Wu X, Gao L, Zhang Q. High throughput vehicle coordination strategies at road intersections. IEEE Trans Veh Technol. 2020;69(12):14341-54. Saal JE, Kirklin S, Aykol M, Meredig B, Wolverton C. Materials design and discovery with high-throughput density functional theory: The open quantum materials database (OQMD). JOM 2013;65(11):1501-9. Dan Y, Zhao Y, Li X, Li S, Hu M, Hu J. Generative adversarial networks (GAN) based efficient sampling of chemical composition space for inverse design of inorganic materials. npj Comput Mater. 2020;6(1):84. Korolev V, Mitrofanov A, Eliseev A, Tkachenko V. Machine-learning-assisted search for functional materials over extended chemical space. Mater Horiz. 2020;7(10):2710-8. Zunger A. Inverse design in search of materials with target functionalities. Nat Rev Chem.2018;2(4):0121. Butler KT, Davies DW, Cartwright H, Isayev O, Walsh A. Machine learning for molecular and materials science. Nature. 2018;559(7715):547-55. Saidi WA, Shadid W, Vesely EJ. Machine-Learning structural and electronic properties of high-temperature superconductors. npj Comput Mater. 2020;6(1):197. Umeda Y, Hayashi H, Moriwake H, Tanaka I. Prediction of dielectric constants using a combination of first principles calculations and machine learning. Jpn J Appl Phys. 2019;58(SL):SLLC01. Ping J, Fan Z, Sindoro M, Ying Y, Zhang H. Recent advances in sensing applications of two‐dimensional transition metal dichalcogenide nanosheets and their composites. Adv Funct Mater. 2017;27(19):1605817. Aykol M, Hegde VI, Hung L, Suram SK, Herring P, Wolverton C, et al. Network analysis of synthesizable materials data. Nat Commun. 2019;10(1):2014. Badini S, Regondi S, Pugliese R. Unleashing the power of artificial intelligence in materials design. Materials. 2023;16(17):5927. Oliynyk AO, Mar A. Discovery of intermetallic compounds from traditional to machine-learning approaches. Acc Chem Res. 2018;51(1):59-68.