سکوی ساخت و تولید هوشمند پلیمرها: مهندسی ژنوم مواد پلیمری
الموضوعات : روش های پیشرفته شناسایی پلیمرها
1 - گروه فرآیندهای پلیمریزاسیون، دانشکده مهندسی شیمی، دانشگاه تربیت مدرس، صندوق پستی ۱۴3-14115
الکلمات المفتاحية: پلیمر, ژنوم مواد, هوش مصنوعی, یادگیری ماشین,
ملخص المقالة :
مواد پلیمری با کارایی عالی، پایه و اساس توسعه فناوری سطح بالا و ساخت و تولید پیشرفته است. اخیراً، مهندسی ژنوم مواد پلیمری(Polymeric material genome engineering) (PMGE) به عنوان سکویی اساسی برای ساخت و تولید هوشمند مواد پلیمری مطرح شده است. PMGE یک رشته نوظهور است که اصول طرح ژنوم مواد را با علم پلیمر ترکیب می کند تا کشف و توسعه مواد پلیمری جدید را تسریع بخشد. مفهوم PMGE ایجاد یک پایگاه داده جامع از خواص پلیمر است که از هر دو روش محاسباتی و تجربی به دست آمده است. سپس می توان از این پایگاه داده برای آموزش مدل های یادگیری ماشینی استفاده کرد که می تواند خواص پلیمرهای جدید را پیش بینی کند. به طور کلی، PMGE نشان دهنده یک گام مهم به سمت تولید هوشمند مواد پلیمری با پتانسیل ایجاد انقلاب در این زمینه همراه با امکان توسعه سریعتر و کارآمدتر مواد جدید است. با این حال، توسعه PMGE هنوز در ابتدای راه است و بسیاری از مسائل، باقی مانده که باید مورد توجه قرار گیرد. در این بررسی، مفاهیم بنیادی PMGE و خلاصه ای از تحقیقات و دستاوردهای پیشرفت های اخیر ارائه می شود، سپس مهمترین چالشها به همراه چشمانداز آینده ترسیم میشود. بهطورخاص، این مطالعه بر رویکردهای پیشبینی خواص، از جمله رویکرد پروکسی و یادگیری ماشین متمرکز است و کاربردهای بالقوه PMGE یعنی کامپوزیت های پیشرفته، مواد پلیمری مورد استفاده در سامانه های ارتباطی و ساخت مدارهای یکپارچه الکتریکی را مورد بحث قرار خواهد داد.
1 Pablo J. J. de, Jones B., Kovacs C. L., Ozolins V., and Ramirez A. P., “The Materials Genome Initiative, the Interplay of Experiment, Theory and Computation,” Curr. Opin. Solid State Mater. Sci., =18, 99–117, 2014.
2 Pablo J. J. de, Jackson N. E., Webb M. A, Zhao J. C, “New Frontiers for the Materials Genome Initiative,” npj Comput. Mater., 5, 41-64, 2019.
3 Yuan W.-L., He L., Tao G.-H., and Shreeve J. M., “Materials-Genome Approach to Energetic Materials,” Accounts Mater. Res., 2, 692–696, 2021.
4 Xie J., Su Y., Zhang D., and Feng Q., “A Vision of Materials Genome Engineering in China,” Engineering, 10, 10–12, 2022.
5 Rizkin B. A.and Hartman R. L., “Supervised Machine Learning for Prediction of Zirconocene-Catalyzed α-Olefin Polymerization,” Chem. Eng. Sci., 210, 115224-115236, 2019.
6 Xu P., Chen H., Li M., and Lu W., “New Opportunity: Machine Learning for Polymer Materials Design and Discovery,” Adv. Theory Simulations, 5, 2100565-2100575, 2022.
7 Agrawal A.and Choudhary A., “Perspective: Materials Informatics and Big Data: Realization of the ‘Fourth Paradigm’ of Science in Materials Science,” APL Mater., 4, 053208-053218, 2016.
8 Gao L., Wang L., Lin J., and Du L., “An Intelligent Manufacturing Platform of Polymers: Polymeric Material Genome Engineering,” Engineering, 27, 31–36, 2023.
9 Kannan S., Subbaram K., and Faiyazuddin M., “Artificial Intelligence in Vaccine Development: Significance and Challenges Ahead,” in A Handbook of Artificial Intelligence in Drug Delivery, Elsevier, 467–486, 2023.
10 Mannodi-Kanakkithodi A., Chandrasekaran A., Kim C., Huan T. D., Pilania G., Botu V., Ramprasad R., “Scoping the Polymer Genome: A Roadmap for Rational Polymer Dielectrics Design and Beyond,” Mater. Today, 21, 785–796, 2018.
11 Zhao H., Li X., Zhang Y., Schadler L. S., Chen W., and Brinson L. C., “Perspective: NanoMine: A Material Genome Approach for Polymer Nanocomposites Analysis and Design,” APL Mater., 4, 053204-053214, 2016
12 Sharma V., Wang C., Lorenzini R. G., Ma R., Zhu Q., Sinkovits D. W., Pilania G., Oganov A. R., Kumar S.,. Sotzing G. A., Boggs S. A. and Ramprasad R., “Rational Design of All Organic Polymer Dielectrics,” Nat. Commun., 5, 4845-4853, 2014.
13 Zhu J., Chu M., Chen Z., Wang L., Lin J., and Du L., “Rational Design of Heat-Resistant Polymers with Low Curing Energies by a Materials Genome Approach,” Chem. Mater., 32, 4527–4535, 2020.
14 Gao G., Zhang S., Wang L., Lin J., Qi H., Zhu J., Du L., and Chu M., “Developing Highly Tough, Heat-Resistant Blend Thermosets Based on Silicon-Containing Arylacetylene: A Material Genome Approach,” ACS Appl. Mater. Interfaces, 12, 27587–27597, Jun. 2020.
15 Mannodi-Kanakkithodi A., Pilania G., Huan T. D., Lookman T., and Ramprasad R., “Machine Learning Strategy for Accelerated Design of Polymer Dielectrics,” Sci. Rep., 6, 20952-20962, 2016.
16 Zhang S., Du S., Wang L., Lin J., Du L., Xu X., Gao L., , “Design of Silicon-Containing Arylacetylene Resins Aided by Machine Learning Enhanced Materials Genome Approach,” Chem. Eng. J., 448, 137643-137652, 2022.
17 Song X., Lv L., Sun W., and Zhang J., “A Radial Basis Function-Based Multi-Fidelity Surrogate Model: Exploring Correlation Between High-Fidelity and Low-Fidelity Models,” Struct. Multidiscip. Optim., 60, 965–981, 2019.
18 Huang Y., Zhang T., Zhu Z., Gong W., and Xia X., “PM2.5 Concentration Estimation with 1-km Resolution at High Coverage over Urban Agglomerations in China Using the BPNN-KED Approach and Potential Application,” Atmos. Res., 258, 105628-105642, 2021.
19 Chen Y., Mao Y., Pan X., Jin W., and Qiu T., “Verification and Comparison of Three Prediction Models of Ischemic Stroke in Young Adults Based on the Back Propagation Neural Networks,” Medicine (Baltimore)., 100, e25081-e25086, 2021.
20 Du B., Lund P. D., Wang J., Kolhe M., and Hu E., “Comparative Study of Modelling the Thermal Efficiency of a Novel Straight Through Evacuated Tube Collector with MLR, SVR, BP and RBF Methods,” Sustain. Energy Technol. Assessments, 44, 101029-101039, 2021.
21 Han H.-G., Ma M.-L., and Qiao J.-F., “Accelerated Gradient Algorithm for RBF Neural Network,” Neurocomputing, 441, 237–247, 2021.
22 Fang Q., “Estimation of Navigation Mark Floating Based on Fractional-Order Gradient Descent with Momentum for RBF Neural Network,” Math. Probl. Eng., 2021, 1–10, 2021.
23 Wu H., Zhao Y.-P., and Tan H.-J., “Novel Radial Basis Function Network Based on Dynamic Time Warping and Kalman Filter for Real-Time Monitoring of Supersonic Inlet Flow Patterns,” J. Aerosp. Eng., 34, 04021041- 04021053, 2021.
24 Duriagina Z. A., Tkachenko R.O., Trostianchyn A.M. , Lemishka I.A. , Kovalchuk A.M., Kulyk V.V. , Kovbasyuk T.M.., “Determination of the Best Microstructure and Titanium Alloy Powders Properties Using Neural Network,” J. Achiev. Mater. Manuf. Eng., 1, 25–31, 2018.
25 Jueyendah S., Lezgy-Nazargah M., Eskandari-Naddaf H., and Emamian S. A., “Predicting the Mechanical Properties of Cement Mortar Using the Support Vector Machine Approach,” Constr. Build. Mater., 291, 123396-123408, 2021.
26 Zhu Q., Wang Y., and Luo Y., “Improvement of Multi‐Layer Soil Moisture Prediction Using Support Vector Machines and Ensemble Kalman Filter Coupled with Remote Sensing Soil Moisture Datasets over an Agriculture Dominant Basin in China,” Hydrol. Process., 35, e14154-e14176, 2021.
27 Sun Y. T., Bai H. Y., Li M. Z., and Wang W. H., “Machine Learning Approach for Prediction and Understanding of Glass-Forming Ability,” J. Phys. Chem. Lett., 8, 3434–3439, 2017.
28 Tkachenko R., Duriagina Z., Lemishka I., Izonin I., and Trostianchyn A., “Development of Machine Learning Method of Titanium Alloy Properties Identification in Additive Technologies,” Eastern-European J. Enterp. Technol., 3, 23–31, 2018.
29 Wolfensberger D., Gabella M., Boscacci M., Germann U., and Berne A., “RainForest: a Random Forest Algorithm for Quantitative Precipitation Estimation over Switzerland,” Atmos. Meas. Tech., 14, 3169–3193, 2021.
30 Speiser J. L., “A Random Forest Method with Feature Selection for Developing Medical Prediction Models with Clustered and Longitudinal Data,” J. Biomed. Inform., 117, 103763-103774, 2021.
31 Li M., Xu Y., Men J., Yan C., Tang H., Zhang T., Li H., “Hybrid Variable Selection Strategy Coupled with Random Forest (RF) for Quantitative Analysis of Methanol in Methanol-Gasoline via Raman Spectroscopy,” Spectrochim. Acta Part A Mol. Biomol. Spectrosc., 251, 119430-119438, 2021.
32 Fan Y., Bai J., Lei X., Lin W., Hu Q., Wu G., Guo J., Tan G.,“PPMCK: Privacy-Preserving Multi-Party Computing for K-means Clustering,” J. Parallel Distrib. Comput., 154, 54–63, 2021.
33 Rim B., Lee S., Lee A., Gil H.-W., and Hong M., “Semantic Cardiac Segmentation in Chest CT Images Using K-Means Clustering and the Mathematical Morphology Method,” Sensors, 21, 2675-2694, 2021.
34 Liu B., Zhang T., Li Y., Liu Z., and Zhang Z., “Kernel Probabilistic K-Means Clustering,” Sensors, 21, 1892-1908, 2021.
35 Xiong P., Liu H., Tian Y., Chen Z., Wang B., and Yang H., “Helicopter Maritime Search Area Planning Based on a Minimum Bounding Rectangle and K-means Clustering,” Chinese J. Aeronaut., 34, 554–562, 2021.
36 Liz H., Sánchez-Montañés M., Tagarro A., Domínguez-Rodríguez S., Dagan R., and Camacho D., “Ensembles of Convolutional Neural Network Models for Pediatric Pneumonia Diagnosis,” Futur. Gener. Comput. Syst.,122, 220–233, 2021.
37 Li M. and Ruan Z., “A Novel Decoding Method for Motor Imagery Tasks with 4D Data Representation and 3D Convolutional Neural Networks,” J. Neural Eng., 18, 046029-046050, 2021.
38 Jia S.and Hu P., “ChrNet: A Re-trainable Chromosome-Based 1D Convolutional Neural Network for Predicting Immune Cell Types,” Genomics, 113, 2023–2031, 2021.
39 Qiu Y., Wu Z., Wang J., Zhang C., and Zhang H., “Introduction of Materials Genome Technology and Its Applications in the Field of Biomedical Materials,” Materials (Basel)., 16, 1906-1925, 2023.
40 Boyd P. G., Lee Y., and Smit B., “Computational Development of the Nanoporous Materials Genome,” Nat. Rev. Mater., 2, 17037-17052, 2017.
41 Lin T.-S., Coley C. W., Mochigase H., Beech H. K., Wang W., Woods E., Craig S. L., Johnson J. A., Kalow J. A., Jensen K. F., Olsen B. D.,“BigSMILES: A Structurally-Based Line Notation for Describing Macromolecules,” ACS Cent. Sci., 5, 1523–1531, 2019.
42 Hu Y., Zhao W., Wang L., Lin J., and Du L., “Machine-Learning-Assisted Design of Highly Tough Thermosetting Polymers,” ACS Appl. Mater. Interfaces, 14, 55004–55016, 2022.
43 Shetty P.and Ramprasad R., “Machine-Guided Polymer Knowledge Extraction Using Natural Language Processing: The Example of Named Entity Normalization,” J. Chem. Inf. Model., 61, 5377–5385, 2021.
44 Wu S.-F.and Hsieh Y.-T., “The Assessment on the Lifetime Performance Index of Products with Gompertz Distribution Based on the Progressive Type I Interval Censored Sample,” J. Comput. Appl. Math., 351, 66–76, 2019.