iamrp.net-BRXK-1791515265-f182729739 1791515265 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 2022 1 1 Michael Turner Sophia Nguyen David Clark Emma Wilson 2022 10.68159/f182729739 https://iamrp.net/pub/journal/1/article/f182729739 Schmidt J, Marques MRG, Botti S, Marques MAL. Recent advances and applications of machine learning in solid-state materials science. npj Comput Mater. 2019;5:83. 10.1038/s41524-019-0221-0 Butler KT, Davies DW, Cartwright H, Isayev O, Walsh A. Machine learning for molecular and materials science. Nature. 2018;559(7715):547-55. 10.1038/s41586-018-0337-2 Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, et al. Attention is all you need. Adv Neural Inf Process Syst. 2017;30:5998-6008. 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. 10.1021/acs.chemmater.9b01294 Nicol AA, Owens SM, Le Coze SS, MacIntyre A, Eastwood C. Comparison of high-technology active learning and low-technology active learning classrooms. Act Learn High Educ. 2018;19(3):253-65. Gómez-Bombarelli R, Wei JN, Duvenaud D, Hernández-Lobato JM, Sánchez-Lengeling B, Sheberla D, et al. Automatic chemical design using a data-driven continuous representation of molecules. ACS Cent Sci. 2018;4(2):268-76. 10.1021/acscentsci.7b00572 Zunger A. Inverse design in search of materials with target functionalities. Nat Rev Chem. 2018;2(4). 10.1038/s41570-018-0121 Wang AY, Kauwe SK, Murdock RJ, Sparks TD, Long S, Lutz RE, et al. Compositionally restricted attention-based network for materials property predictions. npj Comput Mater. 2021;7:77. 10.1038/s41524-021-00545-1 Lookman T, Balachandran PV, Xue D, Yuan R, Kirchdoerfer T, Gould T, et al. Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design. npj Comput Mater. 2019;5:21. 10.1038/s41524-019-0153-8 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;11:5966. 10.1038/s41467-020-19597-w Mazoure B, Nadon R, Makarenkov V. Identification and correction of spatial bias are essential for obtaining quality data in high-throughput screening technologies. Sci Rep. 2017;7:11921. 10.1038/s41598-017-11940-4 Caraus I, Mazoure B, Nadon R, Makarenkov V. Detecting and removing multiplicative spatial bias in high-throughput screening technologies. Bioinformatics. 2017;33(20):3258-67. 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 Chem Chem Phys. 2020;22(33):18141-8. 10.1039/D0CP01474E Lin X, Jiang H, Wang L, Ren Y, Ma W, Zhan S. 3D-structure-attention graph neural network for crystals and materials. Mol Phys. 2022;120(11):e2051000. 10.1080/00268976.2022.2077258 Chen Q, Xiong Y, Chen X. Directed graph attention neural network utilizing 3D coordinates for molecular property prediction. Comput Mater Sci. 2021;200:110761. 10.1016/j.commatsci.2021.110761 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;3:93. 10.1038/s43246-022-00315-6 Coley CW. Defining and exploring chemical spaces. Trends Chem. 2021;3(2):133-45. 10.1016/j.trechm.2020.12.001 Khalak Y, Tresadern G, Hahn DF, de Groot BL, Gapsys V. Chemical space exploration with active learning and alchemical free energies. J Chem Theory Comput. 2022;18(10):6259-70. 10.1021/acs.jctc.2c00752 Balachandran PV, Kowalski B, Sehirlioglu A, Lookman T, Cao Y, Ponomareva I, et al. Experimental search for high-temperature ferroelectric perovskites guided by two-step machine learning. Nat Commun. 2018;9:1668. 10.1038/s41467-018-03821-9 Yuan R, Liu Z, Balachandran PV, Xue D, Zhou Y, Ding X, et al. Accelerated discovery of large electrostrains in batio3-based piezoelectrics using active learning. Adv Mater. 2018;30(7). 10.1002/adma.201702884 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. 10.1103/PhysRevLett.120.145301 Sanchez-Lengeling B, Aspuru-Guzik A. Inverse molecular design using machine learning: Generative models for matter engineering. Science. 2018;361(6400):360-5. 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):241722. 10.1063/1.5019779 Isayev O, Oses C, Toher C, Gossett E, Curtarolo S, Tropsha A, et al. Universal fragment descriptors for predicting properties of inorganic crystals. Nat Commun. 2017;8:15679. 10.1038/ncomms15679 Ramprasad R, Batra R, Pilania G, Mannodi-Kanakkithodi A, Kim C. Machine learning in materials informatics: Recent applications and prospects. npj Comput Mater. 2017;3:54. 10.1038/s41524-017-0056-5 Himanen L, Geurts A, Foster AS, Rinke P. Erratum: Data-driven materials science: Status, challenges, and perspectives. Adv Sci (Weinh). 2020;7(2):1903667. Erratum for: Adv Sci (Weinh). 2019;6(21):1900808. Tshitoyan V, Dagdelen J, Weston L, Dunn A, Rong Z, Kononova O, et al. Unsupervised word embeddings capture latent knowledge from materials science literature. Nature. 2019;571(7763):95-8. 10.1038/s41586-019-1335-8 Jha D, Ward L, Paul A, Liao WK, Choudhary A, Wolverton C, et al. ElemNet: Deep learning the chemistry of materials from only elemental composition. Sci Rep. 2018;8(1):17593. 10.1038/s41598-018-35934-y Pilania G, Gubernatis JE, Lookman T. Multi-fidelity machine learning models for accurate bandgap predictions of solids. Comput Mater Sci. 2017;129:156-63. 10.1016/j.commatsci.2016.12.028