[ 1 ] Gao H, Shreeve JM. Azole-based energetic salts. Chem Rev 2011;111 (11):7377–436. link1

[ 2 ] Núñez-Quintero D, Hernández-Rivera SP. Spectroscopic modeling of nitro group in explosives. In: Szu HH, editor. Proceedings Volume 6247, Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks IV; 2006 Apr 17–21; Orlando, FL, USA. link1

[ 3 ] Dippold AA, Klapötke TM. A study of dinitro-bis-1,2,4-triazole-1,10 -diol and derivatives: design of high-performance insensitive energetic materials by the introduction of N-oxides. J Am Chem Soc 2013;135(26):9931–8. link1

[ 4 ] Baxter AF, Martin I, Christe KO, Haiges R. Formamidinium nitroformate: an insensitive RDX alternative. J Am Chem Soc 2018;140(44):15089–98. link1

[ 5 ] Zhao G, He C, Kumar D, Hooper JP, Imler GH, Parrish DA, et al. 1,3,5-Triiodo2,4,6-trinitrobenzene (TITNB) from benzene: balancing performance and high thermal stability of functional energetic materials. Chem Eng J 2019;378:122119. link1

[ 6 ] Li S, Wang Y, Qi C, Zhao X, Zhang J, Zhang S, et al. 3D energetic metal–organic frameworks: synthesis and properties of high energy materials. Angew Chem Int Ed Engl 2013;52(52):14031–5. link1

[ 7 ] Kamlet MJ, Jacobs SJ. Chemistry of detonations. I. A simple method for calculating detonation properties of C-H–N–O explosives. J Chem Phys 1968;48(1):23–35. link1

[ 8 ] Zhang C, Shu Y, Huang Y, Zhao X, Dong H. Investigation of correlation between impact sensitivities and nitro group charges in nitro compounds. J Phys Chem B 2005;109(18):8978–82. link1

[ 9 ] Wang Y, Liu Y, Song S, Yang Z, Qi X, Wang K, et al. Accelerating the discovery of insensitive high-energy-density materials by a materials genome approach. Nat Commun 2018;9(1):2444. link1

[10] Gu GH, Noh J, Kim I, Jung Y. Machine learning for renewable energy materials. J Mater Chem A 2019;7(29):17096–117. link1

[11] Agrawal A, Choudhary A. Perspective: materials informatics and big data: realization of the ‘fourth paradigm’ of science in materials science. APL Mater 2016;4(5):053208. link1

[12] Butler KT, Davies DW, Cartwright H, Isayev O, Walsh A. Machine learning for molecular and materials science. Nature 2018;559(7715):547–55. link1

[13] Muratov EN, Bajorath J, Sheridan RP, Tetko IV, Filimonov D, Poroikov V, et al. QSAR without borders. Chem Soc Rev 2020;49(11):3525–64. Correction in: Chem Soc Rev 2020;49(11):3716. link1

[14] 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. link1

[15] Takahashi K, Takahashi L. Creating machine learning-driven material recipes based on crystal structure. J Phys Chem Lett 2019;10(2):283–8. link1

[16] Barnett JW, Bilchak CR, Wang Y, Benicewicz BC, Murdock LA, Bereau T, et al. Designing exceptional gas-separation polymer membranes using machine learning. Sci Adv 2020;6(20):eaaz4301. link1

[17] Zhou T, Song Z, Sundmacher K. Big data creates new opportunities for materials research: a review on methods and applications of machine learning for materials design. Engineering 2019;5(6):1017–26. link1

[18] Gómez-Bombarelli R, Aguilera-Iparraguirre J, Hirzel TD, Duvenaud D, Maclaurin D, Blood-Forsythe MA, et al. Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach. Nat Mater 2016;15(10):1120–7. link1

[19] 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. link1

[20] Chen G, Shen Z, Iyer A, Ghumman UF, Tang S, Bi J, et al. Machine-learningassisted de novo design of organic molecules and polymers: opportunities and challenges. Polymers (Basel) 2020;12(1):163. link1

[21] Elton DC, Boukouvalas Z, Butrico MS, Fuge MD, Chung PW. Applying machine learning techniques to predict the properties of energetic materials. Sci Rep 2018;8(1):9059. link1

[22] Kang P, Liu Z, Abou-Rachid H, Guo H. Machine-learning assisted screening of energetic materials. J Phys Chem A 2020;124(26):5341–51. link1

[23] Arús-Pous J, Johansson SV, Prykhodko O,BjerrumEJ, Tyrchan C,Reymond J-L, et al. Randomized SMILES strings improve the quality ofmolecular generativemodels. J Cheminform 2019;11(1). https://doi.org/10.1186/s13321-019-0393-0. link1

[24] Bjerrum EJ. SMILES enumeration as data augmentation for neural network modeling of molecules. 2017. arXiv:1703.07076.

[25] Solov’eva NP, Makarov VA, Granik VG. Highly polarized enamines. Chem Heterocycl Compd 1997;33(1):78–85. link1

[26] Tang Y, Ma J, Imler GH, Parrish DA, Shreeve JM. Versatile functionalization of 3,5-diamino-4-nitropyrazole for promising insensitive energetic compounds. Dalton Trans 2019;48(38):14490–6. link1

[27] Hall LH, Kier LB. Electrotopological state indices for atom types: a novel combination of electronic, topological, and valence state information. J Chem Inf Comput Sci 1995;35(6):1039–45. link1

[28] Hall LH, Story CT. Boiling point and critical temperature of a heterogeneous data set: QSAR with atom type electrotopological state indices using artificial neural net-works. J Chem Inf Comput Sci 1996;36(5):1004–14. link1

[29] Landrum G. RDKit: open-source cheminformatics. 2006.

[30] Abdi H, Williams LJ. Principal component analysis. WIREs Comp Stat 2010;2 (4):433–59. link1

[31] Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: machine learning in Python. J Mach Learn Res 2011;12:2825–30. link1

[32] Zhang C, Wang X, Huang H. p-Stacked interactions in explosive crystals: buffers against external mechanical stimuli. J Am Chem Soc 2008;130 (26):8359–65. link1

[33] Zhang J, Mitchell LA, Parrish DA, Shreeve JM. Enforced layer-by-layer stacking of energetic salts towards high-performance insensitive energetic materials. J Am Chem Soc 2015;137(33):10532–5. link1

[34] Song S, Wang Y, Wang K, Chen F, Zhang Q. Decoding the crystal engineering of graphite-like energetic materials: from theoretical prediction to experimental verification. J Mater Chem A 2020;8(12):5975–85. link1

[35] Ziletti A, Kumar D, Scheffler M, Ghiringhelli LM. Insightful classification of crystal structures using deep learning. Nat Commun 2018;9(1):2775. link1

[36] Ryan K, Lengyel J, Shatruk M. Crystal structure prediction via deep learning. J Am Chem Soc 2018;140(32):10158–68. link1

[37] Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Commun ACM 2017;60(6):84–90. link1

[38] Gers FA, Schraudolph NN, Schmidhuber J. Learning precise timing with LSTM recurrent networks. J Mach Learn Res 2002;3:115–43. link1

[39] Weininger D. a chemical language and information system. 1. Introduction to methodology and encoding rules. J Chem Inf Comput Sci 1988;28(1): 31–6. link1

[40] Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, et al. Pytorch: an imperative style, high-performance deep learning library. In: Wallach H, Larochelle H, Beygelzimer A, d’Alché-Buc F, Fox E, Garnett R, editors. Advances in neural information processing systems (NeurIPS 2019); 2019 Dec 8–14; Vancouver, BC, Canada. p. 8026–37. link1

[41] Moret M, Friedrich L, Grisoni F, Merk D, Schneider G. Generative molecular design in low data regimes. Nat Mach Intell 2020;2(3):171–80. link1

[42] Gani R, Brignole EA. Molecular design of solvents for liquid extraction based on UNIFAC. Fluid Phase Equilib 1983;13:331–40. link1

[43] Sumita M, Yang X, Ishihara S, Tamura R, Tsuda K. Hunting for organic molecules with artificial intelligence: molecules optimized for desired excitation energies. ACS Cent Sci 2018;4(9):1126–33. link1

[44] Gao H, Zhang Q, Shreeve JM. Fused heterocycle-based energetic materials (2012–2019). J Mater Chem A 2020;8(8):4193–216. link1

[45] Chen S, Liu Y, Feng Y, Yang X, Zhang Q. 5,6-Fused bicyclic tetrazolo-pyridazine energetic materials. Chem Commun 2020;56(10):1493–6. link1

[46] Tsyshevsky R, Smirnov AS, Kuklja MM. Comprehensive end-to-end design of novel high energy density materials: III. Fused heterocyclic energetic compounds. J Phys Chem C 2019;123(14):8688–98. link1

[47] Schulze MC, Scott BL, Chavez DE. A high density pyrazolo-triazine explosive (PTX). J Mater Chem A 2015;3(35):17963–5. link1

[48] Yao W, Xue Y, Qian L, Yang H, Cheng G. Combination of 1,2,3-triazole and 1,2,4-triazole frameworks for new high-energy and low-sensitivity compounds. Energ Mater Front 2021;2(2):131–8. link1

[49] Cao Y, Lai W, Yu T, Ma Y, Liu Y, Wang B. Graphite-like packing modes facilitating high thermal stability: a comparative study in the polymorphs of planar energetic molecules. Cryst Growth Des 2021;21(6):3175–8. link1

[50] Frisch MJ, Trucks GW, Schlegel HB, Scuseria GE, Robb MA, Cheeseman JR, et al. Gaussian 09, Revision D.01. Wallingford: Gaussian, Inc.; 2013. link1

[51] Lu T, Chen F. Multiwfn: a multifunctional wavefunction analyzer. J Comput Chem 2012;33(5):580–92. link1

[52] Mathieu D. Sensitivity of energetic materials: theoretical relationships to detonation performance and molecular structure. Ind Eng Chem Res 2017;56 (29):8191–201. link1

[53] Song S, Chen F, Wang Y, Wang K, Yan M, Zhang Q. Accelerating the discovery of energetic melt-castable materials by a high-throughput virtual screening and experimental approach. J Matter Chem A 2021;9(38):21723–31. link1