Discovery Acceleration vs Epistemic Depth: Speed–Understanding Trade-Offs in Computational Design

iamrp.net-CnI1-1791483544-v699708567 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 Discovery Acceleration vs Epistemic Depth: Speed–Understanding Trade-Offs in Computational Design Bruno Martins Lucas Pereira Renata Azevedo 2022 10.68159/v699708567 https://iamrp.net/pub/journal/2/article/v699708567 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. Schmidt J, Marques MRG, Botti S, Marques MAL. Recent advances and applications of machine learning in solid-state materials science. npj Computat Mater. 2019;5(1):83. Kim S, Yoon H, Lee J, Kim S, Lee S, Cho S, et al. Deep learning accelerates the detection of live bacteria using thin-film transistor arrays. Adv Intell Syst. 2019;1(6):1900048. Chen C, Ye W, Zuo Y, Zheng C. Graph networks as a universal machine learning framework for molecules and crystals. Chem Mater. 2019;31(9):3564-72. Tran K, Ulissi ZW. Active learning across intermetallics to guide discovery of electrocatalysts for CO2 reduction and H2 evolution. Nat Catal. 2018;1(9):696-703. Ward L, O'Keeffe SC, Stevick J, Wolverton C, Chard K, Gainaru C, et al. A long-lived automated materials discovery infrastructure. npj Comput Mater. 2021;7(1):175. Lookman T, Balachandran PV, Xue D, Yuan R. Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design. npj Comput Mater. 2019;5(1):21. Ward L, Wolverton C. Atomistic calculations and materials informatics: A review. Curr Opin Solid State Mater Sci. 2017;21(3):167-76. Pilania G, Gubernatis JE, Lookman T. Multi-fidelity machine learning models for accurate bandgap predictions of solids. Comput Mater Sci. 2017;129:156-63. Pilania G, Mannodi-Kanakkithodi A, Uberuaga BP, Ramprasad R, Gubernatis JE, Lookman T. Machine learning bandgaps of double perovskites. Sci Rep. 2016;6(1):19375. Montoya JH, Winther KT, Flores RA, Bligaard T, Hummelshøj JS, Aykol M. Autonomous intelligent agents for accelerated materials discovery. Chem Sci. 2020;11(32):8517-32. Ling J, Hutchinson M, Antono E, Paradiso S, Meredig B. High-Dimensional materials and process optimization using data-driven experimental design with uncertainty. Integr Mater Manuf Innov. 2017;6(3):207-17. Meredig B, Antono E, Church C, Hutchinson M, Ling J, Paradiso S, et al. Can machine learning identify the next high-temperature superconductor? Examining extrapolation performance in materials discovery. Mol Syst Des Eng. 2018;3(6):819-25. Ward L, Dunn A, Faghaninia A, Zimmermann NER, Bajaj S, Wang Q, et al. Matminer: An open source toolkit for materials data mining. Comput Mater Sci. 2018;152:60-9. Ahmad Z, Xie T, Maheshwari C, Grossman JC, Viswanathan V. Machine learning enabled computational screening of inorganic solid electrolytes for suppression of dendrite formation in lithium metal anodes. ACS Cent Sci. 2018;4(8):996-1006. Kim E, Huang K, Saunders A, McCallum A, Ceder G, Olivetti E. Materials synthesis insights from scientific literature via text extraction and machine learning. Chem Mater. 2017;29(21):9436-44. Xue D, Balachandran PV, Hogden J, Theiler J, Xue D, Lookman T. Accelerated search for materials with targeted properties by adaptive design. Nat Commun. 2016;7(1):11241. Sun S, Hartono NTP, Ren ZD, Oviedo F, Buscemi A, Layurova M, et al. Accelerated optimization of TiO2-mediated UV photocatalysis through automated flow synthesis and machine learning. Joule. 2020;4(1):130-46. Oliynyk AO, Antono E, Sparks TD, Ghadbeigi L, Gaultois MW, Meredig B, et al. High-Throughput machine-learning-driven synthesis of full-heusler compounds. Chem Mater. 2016;28(20):7324-31. Ward L, Agrawal A, Choudhary A, Wolverton C. A general-purpose machine learning framework for predicting properties of inorganic materials. npj Comput Mater. 2016;2(1):16028. Ling J, Jones R, Templeton J. Machine learning strategies for systems with invariance properties. J Comput Phys. 2016;318:22-35. Meredig B. Five high-impact research areas in machine learning for materials science. Chem Mater. 2019;31(23):9579-81. Meredig B, Agrawal A, Kirklin S, Saal JE, Doak JW, Thompson A, et al. Combinatorial screening for new materials in unconstrained composition space with machine learning. Phys Rev B. 2014;89(9):094104. Meredig B, Antono E, Church C, Hutchinson M, Ling J, Paradiso S, et al. High-Throughput computational screening of 12,000 experimentally stable compounds. Mol Syst Des Eng. 2020;5(2):485-94. Balachandran PV, Xue D, Theiler J, Hogden J, Lookman T. Adaptive strategies for materials design using uncertainties. Sci Rep. 2016;6(1):19660. Oliynyk AO, Adutwum LA, Rudyk BW, Pisavadia H, Lawler S, Mar A. Exploring the cationic states of I–III–IV2 semiconductors: An example of computational materials discovery for photovoltaic applications. Cryst Growth Des. 2018;18(6):3661-9. Saal JE, Oliynyk AO, Meredig B. Machine learning in materials discovery: Confirmed predictions and lessons learned. Annu Rev Mater Res. 2020;50(1):49-69. Kim E, Huang K, Jegelka S, Olivetti E. Virtual screening of inorganic materials synthesis parameters with deep learning. npj Comput Mater. 2017;3(1):53. Aykol M, Hegde VI, Suram L, Hung L, Malik K, Zhao L, et al. Network analysis of synthesizable materials discovery. Nat Commun. 2019;10(1):2018.