Application and Prospect of Machine Learning in the Field of Materials and Chemical Engineering
Journal: Architecture Engineering and Science DOI: 10.32629/aes.v5i1.1845
Abstract
With the rapid development of computer science, machine learning has gradually penetrated into various research fields and led to the development of related fields to different degrees. At recent stage, machine learning (ML) algorithms have been able to extensively search for variable relationships from a large amount of observed data, thus helping people to discover more laws that were once unnoticed. As an emerging approach, ML can efficiently utilize and process large-volume data generated from high-throughput experiments in the field of materials and chemicals. In recent years, with the continuous improvement of various algorithmic tools, more and more researchers have applied ML to materials and chemical research, which has greatly broadened the research ideas and methods. This paper briefly introduces the development of machine learning in recent years; summarizes the classification methods of common molecular descriptors and their types; introduces the classification, common algorithms and application scenarios of different types of ML; reviews the relevant research results in the field of materials prediction and inverse synthesis analysis of machine learning; puts forward related suggestions for the application of ML for research; and finally summarizes the prospects for the future development of machine learning applied to the field of materials and chemical engineering.
Keywords
machine learning, molecular descriptors, material prediction, inverse synthesis design
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[1]McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (2006). A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence, August 31, 1955. AI Magazine, 27(4), 12.
[2]Silver, D., Huang, A., Maddison, C. et al. Mastering the game of Go with deep neural networks and tree search. Nature 529, 484–489 (2016).
[3]Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260.
[4]Liu Yidi, Yang Qi, LI Yao et al. Application of Machine Learning in Organic Chemistry [J]. Chinese Journal of Organic Chemistry, 2019,40(11):3812-3827.
[5]Brockherde F, Vogt L, Li L, Tuckerman ME, Burke K, Müller KR. Bypassing the Kohn-Sham equations with machine learning. Nat Commun. 2017 Oct 11;8(1):872.
[6]Matthias Rupp, Alexandre Tkatchenko, Klaus-Robert Müller, and O. Anatole von Lilienfeld. Phys. Rev. Lett. 109,059802.
[7]Sun T L, Pei J F. Drug design and drug information in the big data era (in Chinese). Chin Sci Bull, 2015, 60: 689–693, doi: 10.1360/N972014-01114
[8]Roger Monreal-Corona, Anna Pla-Quintana, Albert Poater.Predictive catalysis: a valuable step towards machine learning,Trends in Chemistry,2023,ISSN 2589-5974.
[9]LI Wei, YANG Jin-cai, HUANG Niu. Deep learning in drug design and discovery[J]. Acta Pharmaceutica Sinica, 2019, 54 (05): 761-767.
[10]Segler MHS, Preuss M, Waller MP. Planning chemical syntheses with deep neural networks and symbolic AI. Nature. 2018 Mar 28;555(7698):604-610.
[11]Maarten R. Dobbelaere, Pieter P. Plehiers, Ruben Van de Vijver, Christian V. Stevens, Kevin M. Van Geem.Machine Learning in Chemical Engineering: Strengths, Weaknesses, Opportunities, and Threats.Engineering.Volume 7, Issue 9,2021,Pages 1201-1211,ISSN 2095-8099.
[12]Levenspiel O. Modeling in chemical engineering. Chem Eng Sci 2002;57(22– 23):4691–6.
[13]Sarker, I.H., Hoque, M.M., Uddin, M.K. et al. Mobile Data Science and Intelligent Apps: Concepts, AI-Based Modeling and Research Directions. Mobile Netw Appl 26, 285–303 (2021).
[14]Sarker, I.H., Kayes, A.S.M., Badsha, S. et al. Cybersecurity data science: an overview from machine learning perspective. J Big Data 7, 41 (2020).
[15]David Weininger. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. Journal of Chemical Information and Computer Sciences, 1988 28 (1), 31-36.
[16]Stefi, Nouleho, Barth, Dominique, David, Olivier, Quessette, Franck, Weisser, Marc-Antoine, Watel, Dimitri. Improving graphs of cycles approach to structural similarity of molecules. PLOS ONE, 2019/12/27(14).
[17]Chakravarti, Suman. Distributed Representation of Chemical Fragments. ACS Omega, 2018/03/31(3), 2825-2836.
[18]Warr WA. A Short Review of Chemical Reaction Database Systems, Computer-Aided Synthesis Design, Reaction Prediction and Synthetic Feasibility. Mol Inform. 2014 Jun;33(6-7):469-76.
[19]Ren Wei and Kong Dexin. On the correlation of molecule descriptors used in QSAR study [J]. Computers and Applied Chemistry, 2009, 26 (11): 1455-1458.
[20]Danishuddin, Asad U. Khan. Descriptors and their selection methods in QSAR analysis: paradigm for drug design. Drug Discovery Today. Volume 21, Issue 8, 2016, Pages 1291-1302, ISSN 1359-6446.
[21]Zi-Hao Li, Quan-Zhe Li, He-Yuan Bai, Shu-Yu Zhang.Synthetic strategies and mechanistic studies of axially chiral styrenes.Chem Catalysis.Volume 3, Issue 6,2023,100594,ISSN 2667-1093.
[22]Quan-Zhe Li, Zi-Hao Li, Jun-Chen Kang, Tong-Mei Ding, Shu-Yu Zhang.Ni-catalyzed, enantioselective three-component radical relayed reductive coupling of alkynes: Synthesis of axially chiral styrenes.Chem Catalysis.Volume 2, Issue 11,2022,Pages 3185-3195,ISSN 2667-1093.
[23]Sarker, I.H. Machine Learning: Algorithms, Real-World Applications and Research Directions. SN COMPUT. SCI. 2, 160 (2021).
[24]Sun Jie, Li Zihao, Zhang Shuyu. Machine learning applications in chemical synthesis and characterization of [J]. Journal of Shanghai Jiao Tong University, 2023, 57 (10): 1231-1244.
[25]Xiujuan Liu, Pengcheng Xu, Juanjuan Zhao, Wencong Lu, Minjie Li, Gang Wang.Material machine learning for alloys: Applications, challenges and perspectives.Journal of Alloys and Compounds.Volume 921,2022,165984,ISSN 0925-8388
[26]Md Hosne Mobarak, Mariam Akter Mimona, Md. Aminul Islam, Nayem Hossain, Fatema Tuz Zohura, Ibnul Imtiaz, Md Israfil Hossain Rimon.Scope of machine learning in materials research—A review.Applied Surface Science Advances.Volume 18,2023,100523,ISSN 2666-5239.
[27]Logan Ward, Stephanie C. O'Keeffe, Joseph Stevick, Glenton R. Jelbert, Muratahan Aykol, Chris Wolverton.A machine learning approach for engineering bulk metallic glass alloys.Acta Materialia.Volume 159,2018,Pages 102-111,ISSN 1359-6454.
[28]Yan Zhang, Cheng Wen, Changxin Wang, Stoichko Antonov, Dezhen Xue, Yang Bai, Yanjing Su.Phase prediction in high entropy alloys with a rational selection of materials descriptors and machine learning models.Acta Materialia.Volume 185,2020,Pages 528-539,ISSN 1359-6454.
[29]Lifei Ju, Minjie Li, Lumin Tian, Pengcheng Xu, Wencong Lu.Accelerated discovery of high-efficient N-annulated perylene organic sensitizers for solar cells via machine learning and quantum chemistry.Materials Today Communications.Volume 25.2020,101604,ISSN 2352-4928.
[30]Zhengyang Gao, Hanwen Zhang, Guangyang Mao, Jianuo Ren, Ziheng Chen, Chongchong Wu, Ian D. Gates, Weijie Yang, Xunlei Ding, Jianxi Yao.Screening for lead-free inorganic double perovskites with suitable band gaps and high stability using combined machine learning and DFT calculation.Applied Surface Science.Volume 568,2021,150916,ISSN 0169-4332.
[31]Xi Cheng, Yangyanbing Liao, Zhao Lei, Jie Li, Xiaolei Fan, Xin Xiao. Multi-scale design of MOF-based membrane separation for CO2/CH4 mixture via integration of molecular simulation, machine learning and process modeling and simulation. Journal of Membrane Science. Volume 672, 2023, 121430, ISSN 0376-7388.
[32]Feng-Yu Zhang, Sicheng Liu, Anwei Huang, Yi-Ning Li, Xiao-Yan Liu, Peng Zhang, A Theoretical Analysis of the Differential Chemical Reaction Results Caused by Chirality Induction, Molecules, 10.3390/molecules28176286, 28, 17, (6286), (2023).
[33]Florian Häse, Loïc M. Roch, Alán Aspuru-Guzik.Next-Generation Experimentation with Self-Driving Laboratories.Trends in Chemistry.Volume 1, Issue 3,2019,Pages 282-291,ISSN 2589-5974.
[34]Lin, Kangjie and Xu, Youjun and Pei, Jianfeng and Lai, Luhua.Automatic retrosynthetic route planning using template-free models.Chem. Sci.Volume 11,Issue 12,2020,Pages 3355-3364.
[35]Xiaorui Wang, Yuquan Li, Jiezhong Qiu, Guangyong Chen, Huanxiang Liu, Benben Liao, Chang-Yu Hsieh, Xiaojun Yao.RetroPrime: A Diverse, plausible and Transformer-based method for Single-Step retrosynthesis predictions.Chemical Engineering Journal.Volume 420, Part 1,2021,129845,ISSN 1385-8947.
[36]Chi Hao Liow, Hyeonmuk Kang, Seunggu Kim, Moony Na, Yongju Lee, Arthur Baucour, Kihoon Bang, Yoonsu Shim, Jacob Choe, Gyuseong Hwang, Seongwoo Cho, Gun Park, Jiwon Yeom, Joshua C. Agar, Jong Min Yuk, Jonghwa Shin, Hyuck Mo Lee, Hye Ryung Byon, EunAe Cho, Seungbum Hong. Machine learning assisted synthesis of lithium-ion batteries cathode materials. Nano Energy. Volume 98, 2022, 107214, ISSN 2211-2855.
[37]Hu Y, Buehler M J. Deep language models for interpretative and predictive materials science[J]. APL Machine Learning, 2023, 1(1).
[38]Wiktor Beker, Rafał Roszak, Agnieszka Wołos, Nicholas H. Angello, Vandana Rathore, Martin D. Burke, and Bartosz A. Grzybowski. Machine Learning May Sometimes Simply Capture Literature Popularity Trends: A Case Study of Heterocyclic Suzuki–Miyaura Coupling. Journal of the American Chemical Society 2022 144 (11), 4819-4827.
[39]Skoraczyński, G., Dittwald, P., Miasojedow, B. et al. Predicting the outcomes of organic reactions via machine learning: are current descriptors sufficient?. Sci Rep 7, 3582 (2017).
[40]Emami, F.S., Vahid, A., Wylie, E.K., Szymkuć, S., Dittwald, P., Molga, K. and Grzybowski, B.A. (2015), A Priori Estimation of Organic Reaction Yields. Angew. Chem. Int. Ed., 54: 10797-10801.
[41]Md Hosne Mobarak, Mariam Akter Mimona, Md. Aminul Islam, Nayem Hossain, Fatema Tuz Zohura, Ibnul Imtiaz, Md Israfil Hossain Rimon. Scope of machine learning in materials research — A review. Applied Surface Science Advances, Volume 18, 2023, 100523, ISSN 2666-5239.
[2]Silver, D., Huang, A., Maddison, C. et al. Mastering the game of Go with deep neural networks and tree search. Nature 529, 484–489 (2016).
[3]Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260.
[4]Liu Yidi, Yang Qi, LI Yao et al. Application of Machine Learning in Organic Chemistry [J]. Chinese Journal of Organic Chemistry, 2019,40(11):3812-3827.
[5]Brockherde F, Vogt L, Li L, Tuckerman ME, Burke K, Müller KR. Bypassing the Kohn-Sham equations with machine learning. Nat Commun. 2017 Oct 11;8(1):872.
[6]Matthias Rupp, Alexandre Tkatchenko, Klaus-Robert Müller, and O. Anatole von Lilienfeld. Phys. Rev. Lett. 109,059802.
[7]Sun T L, Pei J F. Drug design and drug information in the big data era (in Chinese). Chin Sci Bull, 2015, 60: 689–693, doi: 10.1360/N972014-01114
[8]Roger Monreal-Corona, Anna Pla-Quintana, Albert Poater.Predictive catalysis: a valuable step towards machine learning,Trends in Chemistry,2023,ISSN 2589-5974.
[9]LI Wei, YANG Jin-cai, HUANG Niu. Deep learning in drug design and discovery[J]. Acta Pharmaceutica Sinica, 2019, 54 (05): 761-767.
[10]Segler MHS, Preuss M, Waller MP. Planning chemical syntheses with deep neural networks and symbolic AI. Nature. 2018 Mar 28;555(7698):604-610.
[11]Maarten R. Dobbelaere, Pieter P. Plehiers, Ruben Van de Vijver, Christian V. Stevens, Kevin M. Van Geem.Machine Learning in Chemical Engineering: Strengths, Weaknesses, Opportunities, and Threats.Engineering.Volume 7, Issue 9,2021,Pages 1201-1211,ISSN 2095-8099.
[12]Levenspiel O. Modeling in chemical engineering. Chem Eng Sci 2002;57(22– 23):4691–6.
[13]Sarker, I.H., Hoque, M.M., Uddin, M.K. et al. Mobile Data Science and Intelligent Apps: Concepts, AI-Based Modeling and Research Directions. Mobile Netw Appl 26, 285–303 (2021).
[14]Sarker, I.H., Kayes, A.S.M., Badsha, S. et al. Cybersecurity data science: an overview from machine learning perspective. J Big Data 7, 41 (2020).
[15]David Weininger. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. Journal of Chemical Information and Computer Sciences, 1988 28 (1), 31-36.
[16]Stefi, Nouleho, Barth, Dominique, David, Olivier, Quessette, Franck, Weisser, Marc-Antoine, Watel, Dimitri. Improving graphs of cycles approach to structural similarity of molecules. PLOS ONE, 2019/12/27(14).
[17]Chakravarti, Suman. Distributed Representation of Chemical Fragments. ACS Omega, 2018/03/31(3), 2825-2836.
[18]Warr WA. A Short Review of Chemical Reaction Database Systems, Computer-Aided Synthesis Design, Reaction Prediction and Synthetic Feasibility. Mol Inform. 2014 Jun;33(6-7):469-76.
[19]Ren Wei and Kong Dexin. On the correlation of molecule descriptors used in QSAR study [J]. Computers and Applied Chemistry, 2009, 26 (11): 1455-1458.
[20]Danishuddin, Asad U. Khan. Descriptors and their selection methods in QSAR analysis: paradigm for drug design. Drug Discovery Today. Volume 21, Issue 8, 2016, Pages 1291-1302, ISSN 1359-6446.
[21]Zi-Hao Li, Quan-Zhe Li, He-Yuan Bai, Shu-Yu Zhang.Synthetic strategies and mechanistic studies of axially chiral styrenes.Chem Catalysis.Volume 3, Issue 6,2023,100594,ISSN 2667-1093.
[22]Quan-Zhe Li, Zi-Hao Li, Jun-Chen Kang, Tong-Mei Ding, Shu-Yu Zhang.Ni-catalyzed, enantioselective three-component radical relayed reductive coupling of alkynes: Synthesis of axially chiral styrenes.Chem Catalysis.Volume 2, Issue 11,2022,Pages 3185-3195,ISSN 2667-1093.
[23]Sarker, I.H. Machine Learning: Algorithms, Real-World Applications and Research Directions. SN COMPUT. SCI. 2, 160 (2021).
[24]Sun Jie, Li Zihao, Zhang Shuyu. Machine learning applications in chemical synthesis and characterization of [J]. Journal of Shanghai Jiao Tong University, 2023, 57 (10): 1231-1244.
[25]Xiujuan Liu, Pengcheng Xu, Juanjuan Zhao, Wencong Lu, Minjie Li, Gang Wang.Material machine learning for alloys: Applications, challenges and perspectives.Journal of Alloys and Compounds.Volume 921,2022,165984,ISSN 0925-8388
[26]Md Hosne Mobarak, Mariam Akter Mimona, Md. Aminul Islam, Nayem Hossain, Fatema Tuz Zohura, Ibnul Imtiaz, Md Israfil Hossain Rimon.Scope of machine learning in materials research—A review.Applied Surface Science Advances.Volume 18,2023,100523,ISSN 2666-5239.
[27]Logan Ward, Stephanie C. O'Keeffe, Joseph Stevick, Glenton R. Jelbert, Muratahan Aykol, Chris Wolverton.A machine learning approach for engineering bulk metallic glass alloys.Acta Materialia.Volume 159,2018,Pages 102-111,ISSN 1359-6454.
[28]Yan Zhang, Cheng Wen, Changxin Wang, Stoichko Antonov, Dezhen Xue, Yang Bai, Yanjing Su.Phase prediction in high entropy alloys with a rational selection of materials descriptors and machine learning models.Acta Materialia.Volume 185,2020,Pages 528-539,ISSN 1359-6454.
[29]Lifei Ju, Minjie Li, Lumin Tian, Pengcheng Xu, Wencong Lu.Accelerated discovery of high-efficient N-annulated perylene organic sensitizers for solar cells via machine learning and quantum chemistry.Materials Today Communications.Volume 25.2020,101604,ISSN 2352-4928.
[30]Zhengyang Gao, Hanwen Zhang, Guangyang Mao, Jianuo Ren, Ziheng Chen, Chongchong Wu, Ian D. Gates, Weijie Yang, Xunlei Ding, Jianxi Yao.Screening for lead-free inorganic double perovskites with suitable band gaps and high stability using combined machine learning and DFT calculation.Applied Surface Science.Volume 568,2021,150916,ISSN 0169-4332.
[31]Xi Cheng, Yangyanbing Liao, Zhao Lei, Jie Li, Xiaolei Fan, Xin Xiao. Multi-scale design of MOF-based membrane separation for CO2/CH4 mixture via integration of molecular simulation, machine learning and process modeling and simulation. Journal of Membrane Science. Volume 672, 2023, 121430, ISSN 0376-7388.
[32]Feng-Yu Zhang, Sicheng Liu, Anwei Huang, Yi-Ning Li, Xiao-Yan Liu, Peng Zhang, A Theoretical Analysis of the Differential Chemical Reaction Results Caused by Chirality Induction, Molecules, 10.3390/molecules28176286, 28, 17, (6286), (2023).
[33]Florian Häse, Loïc M. Roch, Alán Aspuru-Guzik.Next-Generation Experimentation with Self-Driving Laboratories.Trends in Chemistry.Volume 1, Issue 3,2019,Pages 282-291,ISSN 2589-5974.
[34]Lin, Kangjie and Xu, Youjun and Pei, Jianfeng and Lai, Luhua.Automatic retrosynthetic route planning using template-free models.Chem. Sci.Volume 11,Issue 12,2020,Pages 3355-3364.
[35]Xiaorui Wang, Yuquan Li, Jiezhong Qiu, Guangyong Chen, Huanxiang Liu, Benben Liao, Chang-Yu Hsieh, Xiaojun Yao.RetroPrime: A Diverse, plausible and Transformer-based method for Single-Step retrosynthesis predictions.Chemical Engineering Journal.Volume 420, Part 1,2021,129845,ISSN 1385-8947.
[36]Chi Hao Liow, Hyeonmuk Kang, Seunggu Kim, Moony Na, Yongju Lee, Arthur Baucour, Kihoon Bang, Yoonsu Shim, Jacob Choe, Gyuseong Hwang, Seongwoo Cho, Gun Park, Jiwon Yeom, Joshua C. Agar, Jong Min Yuk, Jonghwa Shin, Hyuck Mo Lee, Hye Ryung Byon, EunAe Cho, Seungbum Hong. Machine learning assisted synthesis of lithium-ion batteries cathode materials. Nano Energy. Volume 98, 2022, 107214, ISSN 2211-2855.
[37]Hu Y, Buehler M J. Deep language models for interpretative and predictive materials science[J]. APL Machine Learning, 2023, 1(1).
[38]Wiktor Beker, Rafał Roszak, Agnieszka Wołos, Nicholas H. Angello, Vandana Rathore, Martin D. Burke, and Bartosz A. Grzybowski. Machine Learning May Sometimes Simply Capture Literature Popularity Trends: A Case Study of Heterocyclic Suzuki–Miyaura Coupling. Journal of the American Chemical Society 2022 144 (11), 4819-4827.
[39]Skoraczyński, G., Dittwald, P., Miasojedow, B. et al. Predicting the outcomes of organic reactions via machine learning: are current descriptors sufficient?. Sci Rep 7, 3582 (2017).
[40]Emami, F.S., Vahid, A., Wylie, E.K., Szymkuć, S., Dittwald, P., Molga, K. and Grzybowski, B.A. (2015), A Priori Estimation of Organic Reaction Yields. Angew. Chem. Int. Ed., 54: 10797-10801.
[41]Md Hosne Mobarak, Mariam Akter Mimona, Md. Aminul Islam, Nayem Hossain, Fatema Tuz Zohura, Ibnul Imtiaz, Md Israfil Hossain Rimon. Scope of machine learning in materials research — A review. Applied Surface Science Advances, Volume 18, 2023, 100523, ISSN 2666-5239.
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