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Recent advances and challenges in experiment-oriented polymer informatics

Abstract

This review summarizes recent advances in experimental polymer chemistry supported by data science. The area of polymer informatics is rapidly growing based on cheminformatics, materials informatics, and data science platforms. Data-driven analyses, predictions, and suggestions for experimental polymer research are becoming more practical, and machine learning models can now predict various macromolecular properties with reasonable accuracy. At the same time, the limitations of current polymer informatics are being revealed. Developing appropriate treatments for higher-order structures and experimental procedures is critical to adequately process the hierarchical relationships of polymer systems. Recent attempts to treat this advanced information and future challenges in polymer informatics are discussed.

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References

  1. 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. https://doi.org/10.1038/s41524-017-0056-5.

    Article  Google Scholar 

  2. Lopez-Bezanilla A, Littlewood PB. Growing field of materials informatics: databases and artificial intelligence. MRS Commun. 2020;10:1–10. https://doi.org/10.1557/mrc.2020.2.

    Article  CAS  Google Scholar 

  3. Jackson NE, Webb MA, de Pablo JJ. Recent advances in machine learning towards multiscale soft materials design. Curr Opin Chem Eng. 2019;23:106–14. https://doi.org/10.1016/j.coche.2019.03.005.

    Article  Google Scholar 

  4. Audus DJ, de Pablo JJ. Polymer informatics: opportunities and challenges. ACS Macro Lett. 2017;6:1078–82. https://doi.org/10.1021/acsmacrolett.7b00228.

    Article  CAS  Google Scholar 

  5. de Pablo JJ, Jackson NE, Webb MA, Chen L-Q, Moore JE, Morgan D, et al. New frontiers for the materials genome initiative. Npj Comput Mater. 2019;5:41 https://doi.org/10.1038/s41524-019-0173-4.

    Article  Google Scholar 

  6. Kim C, Chandrasekaran A, Huan TD, Das D, Ramprasad R. Polymer genome: a data-powered polymer informatics platform for property predictions. J Phys Chem C. 2018;122:17575–85. https://doi.org/10.1021/acs.jpcc.8b02913.

    Article  CAS  Google Scholar 

  7. Mannodi-Kanakkithodi A, Chandrasekaran A, Kim C, Huan TD, Pilania G, Botu V, et al. Scoping the polymer genome: a roadmap for rational polymer dielectrics design and beyond. Mater Today. 2018;21:785–96. https://doi.org/10.1016/j.mattod.2017.11.021.

    Article  CAS  Google Scholar 

  8. Amamoto Y. Data-driven approaches for structure-property relationships in polymer science for prediction and understanding. Polym J. 2022;54:957–67. https://doi.org/10.1038/s41428-022-00648-6.

    Article  CAS  Google Scholar 

  9. Sha W, Li Y, Tang S, Tian J, Zhao Y, Guo Y, et al. Machine learning in polymer informatics. InfoMat. 2021;3:353–61. https://doi.org/10.1002/inf2.12167.

    Article  CAS  Google Scholar 

  10. Chen L, Pilania G, Batra R, Huan TD, Kim C, Kuenneth C, et al. Polymer informatics: current status and critical next steps. Mater Sci Eng R Rep. 2021;144:100595. https://doi.org/10.1016/j.mser.2020.100595.

    Article  Google Scholar 

  11. Schustik SA, Cravero F, Ponzoni I, Díaz MF. Polymer informatics: expert-in-the-loop in QSPR modeling of refractive index. Comput Mater Sci. 2021;194:110460. https://doi.org/10.1016/j.commatsci.2021.110460.

    Article  CAS  Google Scholar 

  12. Oaki Y, Igarashi Y. Materials informatics for 2D materials combined with sparse modeling and chemical perspective: toward small-data-driven Chemistry and Materials Science. Bull Chem Soc Jpn. 2021;94:2410–22. https://doi.org/10.1246/bcsj.20210253.

    Article  CAS  Google Scholar 

  13. Jordan MI, Mitchell TM. Machine learning: trends, perspectives, and prospects. Science. 2015;349:255–60. https://doi.org/10.1126/science.aaa8415.

    Article  CAS  Google Scholar 

  14. Varadi M, Anyango S, Deshpande M, Nair S, Natassia C, Yordanova G, et al. AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Res. 2022;50:D439–44. https://doi.org/10.1093/nar/gkab1061.

    Article  CAS  Google Scholar 

  15. Callaway E. ‘It will change everything’: DeepMind’s AI makes gigantic leap in solving protein structures. Nature. 2020;588:203–4. https://doi.org/10.1038/d41586-020-03348-4.

    Article  CAS  Google Scholar 

  16. Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, et al. Highly accurate protein structure prediction with AlphaFold. Nature. 2021;596:583–9. https://doi.org/10.1038/s41586-021-03819-2.

    Article  CAS  Google Scholar 

  17. Kanda GN, Tsuzuki T, Terada M, Sakai N, Motozawa N, Masuda T, et al. Robotic search for optimal cell culture in regenerative medicine. Elife. 2022;11:e77007. https://doi.org/10.7554/eLife.77007.

    Article  CAS  Google Scholar 

  18. Burger B, Maffettone PM, Gusev VV, Aitchison CM, Bai Y, Wang X, et al. A mobile robotic chemist. Nature. 2020;583:237–41. https://doi.org/10.1038/s41586-020-2442-2.

    Article  CAS  Google Scholar 

  19. Haven JJ, Baeten E, Claes J, Vandenbergh J, Junkers T. High-throughput polymer screening in microreactors: boosting the Passerini three component reaction. Polym Chem. 2017;8:2972–8. https://doi.org/10.1039/c7py00360a.

    Article  CAS  Google Scholar 

  20. Baudis S, Behl M. High-throughput and combinatorial approaches for the development of multifunctional polymers. Macromol Rapid Commun. 2022;43:e2100400. https://doi.org/10.1002/marc.202100400.

    Article  Google Scholar 

  21. Granda JM, Donina L, Dragone V, Long DL, Cronin L. Controlling an organic synthesis robot with machine learning to search for new reactivity. Nature. 2018;559:377–81. https://doi.org/10.1038/s41586-018-0307-8.

    Article  CAS  Google Scholar 

  22. Dave A, Mitchell J, Kandasamy K, Wang H, Burke S, Paria B, et al. Autonomous discovery of battery electrolytes with robotic experimentation and machine learning. Cell Rep Phys Sci. 2020;1:100264. https://doi.org/10.1016/j.xcrp.2020.100264.

    Article  CAS  Google Scholar 

  23. Shimizu R, Kobayashi S, Watanabe Y, Ando Y, Hitosugi T. Autonomous materials synthesis by machine learning and robotics. APL Mater. 2020;8:111110. https://doi.org/10.1063/5.0020370.

    Article  CAS  Google Scholar 

  24. Hatakeyama-Sato K, Tezuka T, Umeki M, Oyaizu K. AI-assisted exploration of superionic Glass-Type Li(+) conductors with aromatic structures. J Am Chem Soc. 2020;142:3301–5. https://doi.org/10.1021/jacs.9b11442.

    Article  CAS  Google Scholar 

  25. Hatakeyama-Sato K, Umeki M, Adachi H, Kuwata N, Hasegawa G, Oyaizu K. Exploration of organic superionic glassy conductors by process and materials informatics with lossless graph database. Npj Comput Mater. 2022;8:170. https://doi.org/10.1038/s41524-022-00853-0.

    Article  Google Scholar 

  26. Hatakeyama-Sato K, Tezuka T, Nishikitani Y, Nishide H, Oyaizu K. Synthesis of Lithium-ion conducting polymers designed by machine learning-based prediction and screening. Chem Lett. 2019;48:130–2. https://doi.org/10.1246/cl.180847.

    Article  CAS  Google Scholar 

  27. Nagasawa S, Al-Naamani E, Saeki A. Computer-aided screening of conjugated polymers for organic solar cell: classification by random forest. J Phys Chem Lett. 2018;9:2639–46. https://doi.org/10.1021/acs.jpclett.8b00635.

    Article  CAS  Google Scholar 

  28. Miyake Y, Saeki A. Machine learning-assisted development of organic solar cell materials: issues, analyses, and outlooks. J Phys Chem Lett. 2021;12:12391–401. https://doi.org/10.1021/acs.jpclett.1c03526.

    Article  CAS  Google Scholar 

  29. Komura T, Sakano K, Igarashi Y, Numazawa H, Imai H, Oaki Y. A capacity-prediction model for exploration of organic anodes: discovery of 5-formylsalicylic acid as a high-performance anode active material. ACS Appl Energy Mater. 2022;5:8990–8. https://doi.org/10.1021/acsaem.2c01472.

    Article  CAS  Google Scholar 

  30. Numazawa H, Igarashi Y, Sato K, Imai H, Oaki Y. Experiment-oriented materials informatics for efficient exploration of design strategy and new compounds for high-performance organic anode. Adv Theory Simul. 2019;2:1900130. ARTN 190013010.1002/adts.201900130.

    Article  CAS  Google Scholar 

  31. Franco AA, Rucci A, Brandell D, Frayret C, Gaberscek M, Jankowski P, et al. Boosting rechargeable batteries R&D by multiscale modeling: myth or reality? Chem Rev. 2019;119:4569–627. https://doi.org/10.1021/acs.chemrev.8b00239.

    Article  CAS  Google Scholar 

  32. Pruksawan S, Lambard G, Samitsu S, Sodeyama K, Naito M. Prediction and optimization of epoxy adhesive strength from a small dataset through active learning. Sci Technol Adv Mater. 2019;20:1010–21. https://doi.org/10.1080/14686996.2019.1673670.

    Article  CAS  Google Scholar 

  33. Yamada H, Liu C, Wu S, Koyama Y, Ju S, Shiomi J, et al. Predicting materials properties with little data using shotgun transfer learning. ACS Cent Sci 2019;5:1717–30. https://doi.org/10.1021/acscentsci.9b00804.

    Article  CAS  Google Scholar 

  34. Wu S, Kondo Y, Kakimoto M-A, Yang B, Yamada H, Kuwajima I, et al. Machine-learning-assisted discovery of polymers with high thermal conductivity using a molecular design algorithm. Npj Comput Mater. 2019;5:66. https://doi.org/10.1038/s41524-019-0203-2.

    Article  Google Scholar 

  35. Taniwaki H, Kaneko H. Molecular design of monomers by considering the dielectric constant and stability of the polymer. Polym Eng Sci. 2022;62:2750–6. https://doi.org/10.1002/pen.26058.

    Article  CAS  Google Scholar 

  36. Hastie T, Tibshirani R, Friedman J, The Elements of Statistical Learning, Springer New York, NY, 2009 https://doi.org/10.1007/978-0-387-84858-7.

  37. David L, Thakkar A, Mercado R, Engkvist O. Molecular representations in AI-driven drug discovery: a review and practical guide. J Cheminform 2020;12:56. https://doi.org/10.1186/s13321-020-00460-5.

    Article  CAS  Google Scholar 

  38. Chen Y, Kirchmair J. Cheminformatics in natural product-based drug Discovery. Mol Inf. 2020;39:e2000171. https://doi.org/10.1002/minf.202000171.

    Article  Google Scholar 

  39. Muratov EN, Bajorath J, Sheridan RP, Tetko IV, Filimonov D, Poroikov V, et al. Tropsha QSAR without borders. Chem Soc Rev. 2020;49:3525–64. https://doi.org/10.1039/d0cs00098a.

    Article  CAS  Google Scholar 

  40. Papers With Code: The latest in Machine Learning. https://paperswithcode.com/datasets.

  41. Lin TS, Coley CW, Mochigase H, Beech HK, Wang W, Wang Z, et al. BigSMILES: a structurally-based line notation for describing macromolecules. ACS Cent Sci. 2019;5:1523–31. https://doi.org/10.1021/acscentsci.9b00476.

    Article  CAS  Google Scholar 

  42. Sharma A, Kumar R, Ranjta S, Varadwaj PK. SMILES to smell: decoding the structure-odor relationship of Chemical Compounds Using the Deep Neural Network Approach. J. Chem. Inf. Model. 2021. https://doi.org/10.1021/acs.jcim.0c01288.

  43. Drefahl A. CurlySMILES: a chemical language to customize and annotate encodings of molecular and nanodevice structures. J Cheminform. 2011;3:1 https://doi.org/10.1186/1758-2946-3-1.

    Article  CAS  Google Scholar 

  44. Kuenneth C, Rajan AC, Tran H, Chen L, Kim C, Ramprasad R. Polymer informatics with multi-task learning. Patterns (N. Y). 2021;2:100238 https://doi.org/10.1016/j.patter.2021.100238.

    Article  CAS  Google Scholar 

  45. Hatakeyama-Sato K, Oyaizu K. Integrating multiple materials science projects in a single neural network. Commun Mater 2020;1:49 https://doi.org/10.1038/s43246-020-00052-8. article number

    Article  Google Scholar 

  46. Shields BJ, Stevens J, Li J, Parasram M, Damani F, Alvarado JIM, et al. Bayesian reaction optimization as a tool for chemical synthesis. Nature. 2021;590:89–96. https://doi.org/10.1038/s41586-021-03213-y.

    Article  CAS  Google Scholar 

  47. Bilal M, Oyedele LO, Qadir J, Munir K, Ajayi SO, Akinade OO, et al. Big Data in the construction industry: a review of present status, opportunities, and future trends. Adv Eng Inform. 2016;30:500–21. https://doi.org/10.1016/j.aei.2016.07.001.

    Article  Google Scholar 

  48. Dong S, Wang P, Abbas K. A survey on deep learning and its applications. Comput Sci Rev. 2021;40:100379. https://doi.org/10.1016/j.cosrev.2021.100379.

    Article  Google Scholar 

  49. Janiesch C, Zschech P, Heinrich K. Machine learning and deep learning. Electron Mark. 2021;31:685–95. https://doi.org/10.1007/s12525-021-00475-2.

    Article  Google Scholar 

  50. Sepehri B. A review on created QSPR models for predicting ionic liquids properties and their reliability from chemometric point of view. J Mol Liq. 2020;297:112013. https://doi.org/10.1016/j.molliq.2019.112013.

    Article  CAS  Google Scholar 

  51. Curtarolo S, Hart GL, Nardelli MB, Mingo N, Sanvito S, Levy O. The high-throughput highway to computational materials design. Nat Mater. 2013;12:191–201. https://doi.org/10.1038/nmat3568.

    Article  CAS  Google Scholar 

  52. Matsubara M, Suzumura A, Ohba N, Asahi R. Identifying superionic conductors by materials informatics and high-throughput synthesis. Commun Mater. 2020;1:5 https://doi.org/10.1038/s43246-019-0004-7.

    Article  Google Scholar 

  53. Wang Y, Richards WD, Ong SP, Miara LJ, Kim JC, Mo Y, et al. Design principles for solid-state lithium superionic conductors. Nat Mater. 2015;14:1026–31. https://doi.org/10.1038/nmat4369.

    Article  CAS  Google Scholar 

  54. Otsuka S, Kuwajima I, Hosoya J, Xu Y, Yamazaki M. PoLyInfo: Polymer database for polymeric materials design. 2011 International Conference on Emerging Intelligent Data and Web Technologies 2011:22–29. https://doi.org/10.1109/eidwt.2011.13.

  55. Mark J, Polymer data handbook, Oxford University Press, New York 1998.

  56. Mark J, Physical Properties of Polymers Handbook, Springer, New York 2006.

  57. Luo Y, Bag S, Zaremba O, Cierpka A, Andreo J, Wuttke S, et al. MOF synthesis prediction enabled by automatic data mining and machine learning. Angew. Chem Int Ed. 2022;61:e202200242. https://doi.org/10.1002/anie.202200242.

    Article  CAS  Google Scholar 

  58. Wakiya T, Kamakura Y, Shibahara H, Ogasawara K, Saeki A, Nishikubo R, et al. Machine-learning-assisted selective synthesis of a semiconductive silver thiolate coordination polymer with segregated paths for holes and electrons. Angew Chem Int Ed. 2021;60:23217–24. https://doi.org/10.1002/anie.202110629.

    Article  CAS  Google Scholar 

  59. Nickel M, Murphy K, Tresp V, Gabrilovich E. A review of relational machine learning for knowledge graphs. Proc IEEE Inst Electr Electron Eng. 2016;104:11–33. https://doi.org/10.1109/jproc.2015.2483592.

    Article  Google Scholar 

  60. Qiao L, Zhang L, Chen S, Shen D. Data-driven graph construction and graph learning: a review. Neurocomputing. 2018;312:336–51. https://doi.org/10.1016/j.neucom.2018.05.084.

    Article  Google Scholar 

  61. Gaudelet T, Day B, Jamasb AR, Soman J, Regep C, Liu G, et al. Utilizing graph machine learning within drug discovery and development. Brief Bioinform. 2021;22:1. https://doi.org/10.1093/bib/bbab159.

    Article  CAS  Google Scholar 

  62. Wang X, Zhu W. in Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2021, 4082–3.

  63. Mrdjenovich D, Horton MK, Montoya JH, Legaspi CM, Dwaraknath S, Tshitoyan V, et al. propnet: a knowledge graph for Materials Science. Matter. 2020;2:464–80. https://doi.org/10.1016/j.matt.2019.11.013.

    Article  Google Scholar 

  64. Wieder O, Kohlbacher S, Kuenemann M, Garon A, Ducrot P, Seidel T, et al. A compact review of molecular property prediction with graph neural networks. Drug Disco Today Technol. 2020;37:1–12. https://doi.org/10.1016/j.ddtec.2020.11.009.

    Article  Google Scholar 

  65. Jiang D, Wu Z, Hsieh CY, Chen G, Liao B, Wang Z, et al. Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models. J Cheminform. 2021;13:12. https://doi.org/10.1186/s13321-020-00479-8.

    Article  CAS  Google Scholar 

  66. https://github.com/KanHatakeyama/flowmater3.1

  67. Walsh E, Cho I. Using Evernote as an electronic lab notebook in a translational science laboratory. J Lab Autom. 2013;18:229–34. https://doi.org/10.1177/2211068212471834.

    Article  Google Scholar 

  68. Tremouilhac P, Nguyen A, Huang YC, Kotov S, Lutjohann DS, Hubsch F, et al. Chemotion ELN: an Open Source electronic lab notebook for chemists in academia. J Cheminform. 2017;9:54. https://doi.org/10.1186/s13321-017-0240-0.

    Article  Google Scholar 

  69. Ghiandoni GM, Bodkin MJ, Chen B, Hristozov D, Wallace JEA, Webster J, et al. Development and application of a data-driven reaction classification model: comparison of an electronic lab notebook and medicinal Chemistry Literature. J Chem Inf Model. 2019;59:4167–87. https://doi.org/10.1021/acs.jcim.9b00537.

    Article  CAS  Google Scholar 

  70. Bhowmik R, Sihn S, Pachter R, Vernon JP. Prediction of the specific heat of polymers from experimental data and machine learning methods. Polymer. 2021;220:123558. https://doi.org/10.1016/j.polymer.2021.123558.

    Article  CAS  Google Scholar 

  71. Yang J, Tao L, He J, McCutcheon JR, Li Y. Machine learning enables interpretable discovery of innovative polymers for gas separation membranes. Sci Adv. 2022;8:eabn9545. https://doi.org/10.1126/sciadv.abn9545.

    Article  CAS  Google Scholar 

  72. Liang Z, Li Z, Zhou S, Sun Y, Yuan J, Zhang C. Machine-learning exploration of polymer compatibility. Cell Rep. Phys Sci. 2022;3:100931. https://doi.org/10.1016/j.xcrp.2022.100931.

    Article  CAS  Google Scholar 

  73. Park J, Shim Y, Lee F, Rammohan A, Goyal S, Shim M, et al. Prediction and interpretation of polymer properties using the graph convolutional network. ACS Polym Au. 2022;2:213–22. https://doi.org/10.1021/acspolymersau.1c00050.

    Article  CAS  Google Scholar 

  74. Li D. The MNIST database of handwritten digit images for machine learning research [Best of the Web]. IEEE Signal Process Mag. 2012;29:141–2. https://doi.org/10.1109/msp.2012.2211477.

    Article  Google Scholar 

  75. Zardecki C, Dutta S, Goodsell DS, Lowe R, Voigt M, Burley SK. PDB-101: Educational resources supporting molecular explorations through biology and medicine. Protein Sci. 2022;31:129–40. https://doi.org/10.1002/pro.4200.

    Article  CAS  Google Scholar 

  76. Wu Z, Ramsundar B, Feinberg EN, Gomes J, Geniesse C, Pappu AS, et al. MoleculeNet: a benchmark for molecular machine learning. Chem Sci. 2018;9:513–30. https://doi.org/10.1039/c7sc02664a.

    Article  CAS  Google Scholar 

  77. Levin I. NIST Inorganic Crystal Structure Database (ICSD), National Institute of Standards and Technology. https://doi.org/10.18434/M32147.

  78. Vrandečić D, Krötzsch Wikidata M. Commun. ACM. 2014;57:78–85. https://doi.org/10.1145/2629489.

    Article  Google Scholar 

  79. Katsura Y, Kumagai M, Kodani T, Kaneshige M, Ando Y, Gunji S, et al. Data-driven analysis of electron relaxation times in PbTe-type thermoelectric materials. Sci Technol Adv Mater. 2019;20:511–20. https://doi.org/10.1080/14686996.2019.1603885.

    Article  CAS  Google Scholar 

  80. Duchowicz PR, Fioressi SE, Bacelo DE, Saavedra LM, Toropova AP, Toropov AA. QSPR studies on refractive indices of structurally heterogeneous polymers. Chemom Intell Lab Syst. 2015;140:86–91. https://doi.org/10.1016/j.chemolab.2014.11.008.

    Article  CAS  Google Scholar 

  81. Gedeck P, Rohde B, Bartels C. QSAR-how good is it in practice? Comparison of descriptor sets on an unbiased cross section of corporate data sets. J Chem Inf Model. 2006;46:1924–36. https://doi.org/10.1021/ci050413p.

    Article  CAS  Google Scholar 

  82. Dam HC, Nguyen VC, Pham TL, Nguyen AT, Terakura K, Miyake T, et al. Important descriptors and descriptor groups of curie temperatures of rare-earth transition-metal binary alloys. J Phys Soc Jpn. 2018;87:113801. https://doi.org/10.7566/jpsj.87.113801.

    Article  Google Scholar 

  83. Rogers D, Hahn M. Extended-connectivity fingerprints. J Chem Inf Model. 2010;50:742–54. https://doi.org/10.1021/ci100050t.

    Article  CAS  Google Scholar 

  84. Capecchi A, Probst D, Reymond JL. One molecular fingerprint to rule them all: drugs, biomolecules, and the metabolome. J Cheminform. 2020;12:43. https://doi.org/10.1186/s13321-020-00445-4.

    Article  CAS  Google Scholar 

  85. Lambard G, Gracheva E. SMILES-X: autonomous molecular compounds characterization for small datasets without descriptors. Mach Llearn: Sci Technol. 2020;1:025004. https://doi.org/10.1088/2632-2153/ab57f3.

    Article  Google Scholar 

  86. Zhou J, Cui G, Zhang Z, Yang C, Liu Z, Wang L, et al. Graph neural networks: a review of methods and applications. 2018;arXiv:1812.08434. arXiv:1812.08434.

  87. Duvenaudy D, Maclauriny D, Aguilera-Iparraguirre J, Gomez-Bombarelli R, Hirzel T, Aspuru-Guzik A, et al. Convolutional networks on graphs for learning molecular fingerprints. 2015;arXiv:1509.09292. https://doi.org/10.48550/arXiv.1509.09292.

  88. Zhong S, Hu J, Yu X, Zhang H. Molecular image-convolutional neural network (CNN) assisted QSAR models for predicting contaminant reactivity toward OH radicals: Transfer learning, data augmentation and model interpretation. Chem Eng J. 2021;408:127998. https://doi.org/10.1016/j.cej.2020.127998.

    Article  CAS  Google Scholar 

  89. Gomez-Bombarelli R, Wei JN, Duvenaud D, Hernandez-Lobato JM, Sanchez-Lengeling B, Sheberla D, et al. Automatic Chemical design using a data-driven continuous representation of molecules. ACS Cent Sci. 2018;4:268–76. https://doi.org/10.1021/acscentsci.7b00572.

    Article  CAS  Google Scholar 

  90. Shimizu Y, Kurokawa T, Arai H, Washizu H. Higher-order structure of polymer melt described by persistent homology. Sci Rep. 2021;11:2274. https://doi.org/10.1038/s41598-021-80975-5.

    Article  CAS  Google Scholar 

  91. Buchet M, Hiraoka Y, Obayashi I. Persistent Homology and Materials Informatics. In: Tanaka I (eds) Nanoinformatics, Springer, Singapore 2018. https://doi.org/10.1007/978-981-10-7617-6_5.

  92. Maric M, Marano J, Cody RB, Bridge C. DART-MS: a new analytical technique for forensic paint analysis. Anal Chem. 2018;90:6877–84. https://doi.org/10.1021/acs.analchem.8b01067.

    Article  CAS  Google Scholar 

  93. Cody RB, Fouquet TNJ, Takei C. Thermal desorption and pyrolysis direct analysis in real time mass spectrometry for qualitative characterization of polymers and polymer additives. Rapid Commun Mass Spectrom. 2020;34:e8687. https://doi.org/10.1002/rcm.8687.

    Article  CAS  Google Scholar 

  94. Stafford CM, Guo S, Harrison C, Chiang MYM. Combinatorial and high-throughput measurements of the modulus of thin polymer films. Rev Sci Instrum. 2005;76:062207. https://doi.org/10.1063/1.1906085.

    Article  Google Scholar 

  95. Frenklach M. Transforming data into knowledge—process Informatics for combustion chemistry. Proc Combust Inst. 2007;31:125–40. https://doi.org/10.1016/j.proci.2006.08.121.

    Article  Google Scholar 

  96. Yamakage S, Kaneko H. Design of adaptive soft sensor based on Bayesian optimization. Case Stud Chem Env Eng. 2022;6:100237. https://doi.org/10.1016/j.cscee.2022.100237.

    Article  CAS  Google Scholar 

  97. Nagy ZK, Braatz RD. Advances and new directions in crystallization control. Annu Rev Chem Biomol Eng. 2012;3:55–75. https://doi.org/10.1146/annurev-chembioeng-062011-081043.

    Article  CAS  Google Scholar 

  98. Bruckstein AM, Donoho DL, Elad M. From sparse solutions of systems of equations to sparse modeling of signals and images. SIAM Rev. 2009;51:34–81. https://doi.org/10.1137/060657704.

    Article  Google Scholar 

  99. Udrescu SM, Tegmark M. AI Feynman: a physics-inspired method for symbolic regression. Sci Adv. 2020;6:eaay2631. https://doi.org/10.1126/sciadv.aay2631.

    Article  Google Scholar 

  100. Iwasaki Y, Ishida M. Data-driven formulation of natural laws by recursive-LASSO-based symbolic regression. arXiv. 2021. https://doi.org/10.48550/arXiv.2102.09210.

  101. Gillies S. Shapely: manipulation and analysis of geometric objects. 2007. https://github.com/Toblerity/Shapely.

  102. Molnar C. Interpretable machine learning: a guide for making black box models explainable.

  103. Hatakeyama-Sato K, Igarashi Y, Kashikawa T, Kimura K, Oyaizu K. Quantum circuit learning to predict experimental chemical properties. ChemRxiv. 2022. https://doi.org/10.26434/chemrxiv-2022-cz7wr-v2.

  104. Mitarai K, Negoro M, Kitagawa M, Fujii K. Quantum circuit learning. Phys Rev A. 2018;98:032309. ARTN 03230910.1103/PhysRevA.98.032309.

    Article  CAS  Google Scholar 

  105. Suzuki T, Katouda M. Predicting toxicity by quantum machine learning. J Phys Commun. 2020;4:125012. https://doi.org/10.1088/2399-6528/abd3d8.

    Article  Google Scholar 

  106. Kishino M, Matsumoto K, Kobayashi Y, Taguchi R, Akamatsu N, Shishido A. Fatigue life prediction of bending polymer films using random forest. Int J Fatigue. 2023;166:107230. https://doi.org/10.1016/j.ijfatigue.2022.107230.

    Article  CAS  Google Scholar 

  107. Sahu H, Li H, Chen L, Rajan AC, Kim C, Stingelin N, et al. An informatics approach for designing conducting polymers. ACS Appl Mater Interfaces. 2021;13:53314–22. https://doi.org/10.1021/acsami.1c04017.

    Article  CAS  Google Scholar 

  108. Shahriari B, Swersky K, Wang Z, Adams RP, de Freitas N. Taking the human out of the loop: a review of Bayesian optimization. Proc IEEE Inst Electr Electron Eng. 2016;104:148–75. https://doi.org/10.1109/jproc.2015.2494218.

    Article  Google Scholar 

  109. Shimizu N, Kaneko H. Direct inverse analysis based on Gaussian mixture regression for multiple objective variables in material design. Mater Des. 2020;196:109168. https://doi.org/10.1016/j.matdes.2020.109168.

    Article  Google Scholar 

  110. Haruna SI, Zhu H, Umar IK, Shao J, Adamu M, Ibrahim YE. Gaussian process regression model for the prediction of the compressive strength of polyurethane-based polymer concrete for runway repair: a comparative approach. IOP Conf Ser: Earth Environ Sci. 2022;1026:012007. https://doi.org/10.1088/1755-1315/1026/1/012007.

    Article  Google Scholar 

  111. Kuzminykh D, Polykovskiy D, Kadurin A, Zhebrak A, Baskov I, Nikolenko S, et al. 3D molecular representations based on the wave transform for convolutional neural networks. Mol Pharm. 2018;15:4378–85. https://doi.org/10.1021/acs.molpharmaceut.7b01134.

    Article  CAS  Google Scholar 

  112. Hatakeyama-Sato K, Adachi H, Umeki M, Kashikawa T, Kimura K, Oyaizu K. Automated design of Li(+) -conducting polymer by quantum-inspired annealing. Macromol Rapid Commun. 2022:e2200385. https://doi.org/10.1002/marc.202200385.

  113. Hatakeyama-Sato K, Oyaizu K. Generative models for extrapolation prediction in materials informatics. ACS Omega. 2021;6:14566–74. https://doi.org/10.1021/acsomega.1c01716.

    Article  CAS  Google Scholar 

  114. Gao M, Zhang J, Yu J, Li J, Wen J, Xiong Q. Recommender systems based on generative adversarial networks: a problem-driven perspective. Inf Sci. 2021;546:1166–85. https://doi.org/10.1016/j.ins.2020.09.013.

    Article  Google Scholar 

  115. Hatakeyama-Sato K: ion_predictor. https://github.com/KanHatakeyama/ion_predictor.

  116. Bunn CW. The melting points of chain polymers. J Polym Sci. 1955;16:323–43. https://doi.org/10.1002/pol.1955.120168222.

    Article  CAS  Google Scholar 

  117. Ogden S, Klintberg L, Thornell G, Hjort K, Bodén R. Review on miniaturized paraffin phase change actuators, valves, and pumps. Microfluid Nanofluidics. 2013;17:53–71. https://doi.org/10.1007/s10404-013-1289-3.

    Article  Google Scholar 

  118. Watanabe S. Knowing and Guessing: A Quantitative Study of Inference and Information, Wiley, New York 1969 https://archive.org/details/knowingguessingq0000wata.

  119. Sutton C, Boley M, Ghiringhelli LM, Rupp M, Vreeken J, Scheffler M. Identifying domains of applicability of machine learning models for materials science. Nat Commun. 2020;11:4428. https://doi.org/10.1038/s41467-020-17112-9.

    Article  CAS  Google Scholar 

  120. Kaneko H. Data visualization, regression, applicability domains and inverse analysis based on generative topographic mapping. Mol Inform. 2019;38:e1800088. https://doi.org/10.1002/minf.201800088.

    Article  Google Scholar 

  121. Shen K-H, Fan M, Hall LM. Molecular dynamics simulations of ion-containing polymers using generic coarse-grained models. Macromolecules. 2021;54:2031–52. https://doi.org/10.1021/acs.macromol.0c02557.

    Article  CAS  Google Scholar 

  122. Xie T, France-Lanord A, Wang Y, Lopez J, Stolberg MA, Hill M, et al. Accelerating amorphous polymer electrolyte screening by learning to reduce errors in molecular dynamics simulated properties. Nat Commun. 2022;13:3415. https://doi.org/10.1038/s41467-022-30994-1.

    Article  CAS  Google Scholar 

  123. Doerr S, Harvey MJ, Noe F, De Fabritiis G. HTMD: high-throughput molecular dynamics for molecular discovery. J Chem Theory Comput. 2016;12:1845–52. https://doi.org/10.1021/acs.jctc.6b00049.

    Article  CAS  Google Scholar 

  124. Takamoto S, Shinagawa C, Motoki D, Nakago K, Li W, Kurata I, et al. Towards universal neural network potential for material discovery applicable to arbitrary combination of 45 elements. Nat Commun. 2022;13:2991. https://doi.org/10.1038/s41467-022-30687-9.

    Article  CAS  Google Scholar 

  125. Polymer Property Predictor and Database. https://pppdb.uchicago.edu/.

  126. Polymer database (CROW). https://www.polymerdatabase.com/.

  127. Polymers: A Property Database https://poly.chemnetbase.com/faces/polymers/PolymerSearch.xhtml.

  128. Alesadi A, Cao Z, Li Z, Zhang S, Zhao H, Gu X, et al. Machine learning prediction of glass transition temperature of conjugated polymers from chemical structure. Cell Rep Phys Sci. 2022;3:100911. https://doi.org/10.1016/j.xcrp.2022.100911.

    Article  CAS  Google Scholar 

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Funding

This work was partially supported by Grants-in-Aid for Scientific Research (Grant Nos. 22H04623 and 21H02017) from MEXT, Japan, and the JST FOREST Program (Grant No. JPMJFR213V).

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Hatakeyama-Sato, K. Recent advances and challenges in experiment-oriented polymer informatics. Polym J 55, 117–131 (2023). https://doi.org/10.1038/s41428-022-00734-9

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