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\u6765\u81ea\u7f8e\u56fd\u534e\u76db\u987f\u5927\u5b66\u7684Jim\u00a0Pfaendtner\u6559\u6388\u548c\u4ed6\u7684\u56e2\u961f\u4f7f\u7528ILThermo\u6570\u636e\u5e93\u548cPyChem\uff0cSciKit-Learn\u4e0eRDKit\u5de5\u5177\u5305\u5bf9\u79bb\u5b50\u6eb6\u6db2\u8fdb\u884c\u4e86\u5efa\u6a21\u548c\u6a21\u62df\uff0c\u5e76\u53d1\u8868\u4e86\u9898\u4e3a\u201cStatistical models are able to predict ionic liquid viscosity across a wide range of chemical functionalities and experimental conditions\u201d\u7684\u7814\u7a76\u6210\u679c\u3002\u8be5\u7814\u7a76\u4e2d\uff0c\u4ed6\u4eec\u4f7f\u7528\u4e86\u6765\u81ea\u7f8e\u56fd\u56fd\u5bb6\u6807\u51c6\u4e0e\u6280\u672f\u7814\u7a76\u9662\u7684\u7ea6700\u4e2a\u6570\u636e\u70b9\u5e76\u5229\u7528RDKit\u63d0\u53d6\u4e86\u8fd11200\u4e2a\u751f\u5316\u7279\u5f81\u8bad\u7ec3\u7c7b\u4eba\u7c7b\u795e\u7ecf\u7f51\u7edc\uff08ANN\uff09\uff0c\u901a\u8fc7\u5728\u4e00\u7cfb\u5217\u4e0d\u540c\u7684\u6e29\u5ea6\uff08273.15-373.15K\uff09\u548c\u538b\u5f3a\uff0860-160 kPa\uff09\u72b6\u51b5\u4e0b\u5bf9\u9ecf\u5ea6\u8fdb\u884c\u6d4b\u8bd5\u4ee5\u5f97\u5230\u901a\u7528\u6027\u7684\u6a21\u578b\u3002\u4ece\u8fd11200\u4e2a\u7279\u5f81\u7a7a\u95f4\u5f00\u59cb\uff0c\u5c0f\u7ec4\u6210\u5458\u4eec\u4f7f\u7528\u53c2\u6570\u5316\u6700\u5c0f\u7edd\u5bf9\u6536\u7f29\u9009\u62e9\u7b97\u5b50\uff08LASSO\uff09\u548cBootstrap\u5efa\u7acb\u4e86\u7f6e\u4fe1\u533a\u95f4\uff0c\u5e76\u6700\u7ec8\u8f93\u51fa\u4e86\u5305\u542b11\u4e2a\u7279\u5f81\u7684\u795e\u7ecf\u7f51\u7edc\u6a21\u578b\uff08\u5177\u4f5311\u4e2a\u7279\u5f81\u53ef\u53c2\u8003\u56fe1\uff0c\u5177\u4f53\u7ec6\u8282\u53ef\u53c2\u8003\u539f\u6587\uff0c\u672c\u6587\u4e0d\u8be6\u8ff0\uff09\u3002\u5b9e\u9a8c\u6210\u679c\u8868\u793a\u5305\u542b\u8be511\u4e2a\u7279\u5f81\u7684\u795e\u7ecf\u7f51\u7edc\u6a21\u578b\u5728\u8f83\u5bbd\u8303\u56f4\u7684\u6e29\u5ea6\uff0c\u538b\u5f3a\u548c\u7c98\u7a20\u5ea6\u6761\u4ef6\u4e0b\u53ef\u4ee5\u51c6\u786e\u7684\u8fd0\u884c\u3002\u8fd9\u4e00\u6210\u679c\u5927\u5927\u63d0\u9ad8\u4e86\u79bb\u5b50\u6db2\u4f53\u7684\u68c0\u7d22\u6548\u7387\u3002\u5728\u672a\u6765\uff0c\u4f7f\u7528\u8005\u53ef\u4ee5\u4f7f\u7528\u8bad\u7ec3\u5b8c\u6210\u7684\u795e\u7ecf\u6a21\u578b\u6765\u641c\u7d22\u5177\u6709\u6240\u9700\u8981\u5c5e\u6027\u7684\u79bb\u5b50\u6db2\u4f53\u3002<\/p>\n

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\u56fe2\u00a0\u673a\u5668\u5b66\u4e60\u6570\u636e\u5efa\u6a21\u8bbe\u8ba1\u00a0[2]<\/sup><\/p>\n

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\u56fe4\u00a0\u5bf9\u8f93\u5165\u53c2\u6570\uff08\u5408\u91d1\u5316\u5b66\u6210\u5206\u548c\u52a0\u5de5\u53c2\u6570\uff09\u548c\u76ee\u6807\u53c2\u6570\uff08\u4f38\u957f\u6bd4\u4f8b\uff09\u5efa\u6a21\u8fc7\u7a0b\u56fe\u89e3 [6]<\/sup><\/p>\n

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\u53c2\u8003\u6587\u732e<\/strong><\/p>\n

[1] Beckner, W.; Mao, C.; Pfaendtner, J. Statistical Models Are Able To Predict Ionic Liquid Viscosity Across A Wide Range Of Chemical Functionalities And Experimental Conditions. Molecular Systems Design & Engineering 2018, 3, 253-263.<\/p>\n

[2] Li, F.; Han, J.; Cao, T.; Lam, W.; Fan, B.; Tang, W.; Chen, S.; Fok, K.; Li, L. Design Of Self-Assembly Dipeptide Hydrogels And Machine Learning Via Their Chemical Features. Proceedings of the National Academy of Sciences 2019, 116, 11259-11264.<\/p>\n

[3] . C. Yan, D. J. Pochan, Rheological properties of peptide-based hydrogels for biomedical and other applications. Chem. Soc. Rev. 39, 3528\u20133540 (2010).<\/p>\n

[4] P. W. Frederix et al., Exploring the sequence space for (tri-)peptide self-assembly to design and discover new hydrogels. Nat. Chem. 7, 30\u201337 (2015).<\/p>\n

[5] Hattrick-Simpers, J.; Choudhary, K.; Corgnale, C. A Simple Constrained Machine Learning Model For Predicting High-Pressure-Hydrogen-Compressor Materials.\u00a0Molecular Systems Design & Engineering\u00a02018,\u00a03, 509-517.<\/p>\n

[6] Thankachan, T.; Prakash, K.; David Pleass, C.; Rammasamy, D.; Prabakaran, B.; Jothi, S. Artificial Neural Network To Predict The Degraded Mechanical Properties Of Metallic Materials Due To The Presence Of Hydrogen. International Journal of Hydrogen Energy 2017, 42, 28612-28621.<\/p>\n

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