Aplicación de inteligencia artificial en la resolución de estructuras cristalinas a partir de difracción de rayos X de polvo

La difracción de rayos X (DRX) es una técnica ampliamente empleada para el análisis de compuestos no reportados. Particularmente, la DRX de monocristal se emplea rutinariamente para resolver estructuras cristalinas, a diferencia de su contraparte la DRX de polvo. Este hecho se debe a que es difícil...

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Autores:
Rincón Carvajal, Sergio Iván
Tipo de recurso:
Trabajo de grado de pregrado
Fecha de publicación:
2024
Institución:
Universidad de los Andes
Repositorio:
Séneca: repositorio Uniandes
Idioma:
spa
OAI Identifier:
oai:repositorio.uniandes.edu.co:1992/74919
Acceso en línea:
https://hdl.handle.net/1992/74919
Palabra clave:
Difracción de rayos X
Inteligencia artificial
Modelos de difusión
Modelos de clasificación de imágenes
Química
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License
Attribution-NonCommercial 4.0 International
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repository_id_str
dc.title.spa.fl_str_mv Aplicación de inteligencia artificial en la resolución de estructuras cristalinas a partir de difracción de rayos X de polvo
title Aplicación de inteligencia artificial en la resolución de estructuras cristalinas a partir de difracción de rayos X de polvo
spellingShingle Aplicación de inteligencia artificial en la resolución de estructuras cristalinas a partir de difracción de rayos X de polvo
Difracción de rayos X
Inteligencia artificial
Modelos de difusión
Modelos de clasificación de imágenes
Química
title_short Aplicación de inteligencia artificial en la resolución de estructuras cristalinas a partir de difracción de rayos X de polvo
title_full Aplicación de inteligencia artificial en la resolución de estructuras cristalinas a partir de difracción de rayos X de polvo
title_fullStr Aplicación de inteligencia artificial en la resolución de estructuras cristalinas a partir de difracción de rayos X de polvo
title_full_unstemmed Aplicación de inteligencia artificial en la resolución de estructuras cristalinas a partir de difracción de rayos X de polvo
title_sort Aplicación de inteligencia artificial en la resolución de estructuras cristalinas a partir de difracción de rayos X de polvo
dc.creator.fl_str_mv Rincón Carvajal, Sergio Iván
dc.contributor.advisor.none.fl_str_mv Macías López, Mario Alberto
dc.contributor.author.none.fl_str_mv Rincón Carvajal, Sergio Iván
dc.contributor.jury.none.fl_str_mv Miscione, Gian Pietro
Reiber, Andreas
dc.subject.keyword.spa.fl_str_mv Difracción de rayos X
Inteligencia artificial
Modelos de difusión
Modelos de clasificación de imágenes
topic Difracción de rayos X
Inteligencia artificial
Modelos de difusión
Modelos de clasificación de imágenes
Química
dc.subject.themes.none.fl_str_mv Química
description La difracción de rayos X (DRX) es una técnica ampliamente empleada para el análisis de compuestos no reportados. Particularmente, la DRX de monocristal se emplea rutinariamente para resolver estructuras cristalinas, a diferencia de su contraparte la DRX de polvo. Este hecho se debe a que es difícil determinar una estructura cristalina sin ambigüedad utilizando DRX de polvo. En este estudio se presenta una primera aproximación a la resolución de estructuras cristalinas a partir de DRX de polvo mediante modelos de inteligencia artificial.
publishDate 2024
dc.date.accessioned.none.fl_str_mv 2024-08-02T18:42:28Z
dc.date.issued.none.fl_str_mv 2024-07
dc.date.accepted.none.fl_str_mv 2024-08-02
dc.date.available.none.fl_str_mv 2025-07-31
dc.type.none.fl_str_mv Trabajo de grado - Pregrado
dc.type.driver.none.fl_str_mv info:eu-repo/semantics/bachelorThesis
dc.type.version.none.fl_str_mv info:eu-repo/semantics/acceptedVersion
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format http://purl.org/coar/resource_type/c_7a1f
status_str acceptedVersion
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/1992/74919
dc.identifier.instname.none.fl_str_mv instname:Universidad de los Andes
dc.identifier.reponame.none.fl_str_mv reponame:Repositorio Institucional Séneca
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url https://hdl.handle.net/1992/74919
identifier_str_mv instname:Universidad de los Andes
reponame:Repositorio Institucional Séneca
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dc.language.iso.none.fl_str_mv spa
language spa
dc.relation.references.none.fl_str_mv Park, W. B.; Chung, J.; Jung, J.; Sohn, K.; Singh, S. P.; Pyo, M.; Shin, N.; Sohn, K. S. Classification of Crystal Structure Using a Convolutional Neural Network. IUCrJ 2017. https://doi.org/10.1107/S205225251700714X.
Harris, K. D. M.; Tremayne, M.; Kariuki, B. M. Contemporary Advances in the Use of Powder X-Ray Diffraction for Structure Determination. Angewandte Chemie - International Edition. 2001. https://doi.org/10.1002/1521-3773(20010504)40:9<1626::aid-anie16260>3.0.co;2-7.
Harris, K. D. M.; Williams, P. A. Structure Determination of Organic Molecular Solids from Powder X-Ray Diffraction Data: Current Opportunities and State of the Art. In Advances in Organic Crystal Chemistry: Comprehensive Reviews 2015; 2015. https://doi.org/10.1007/978-4-431-55555-1_8.
Lee, B. Do; Lee, J.-W.; Park, W. B.; Park, J.; Cho, M.-Y.; Pal Singh, S.; Pyo, M.; Sohn, K.-S. Powder X‐Ray Diffraction Pattern Is All You Need for Machine‐Learning‐Based Symmetry Identification and Property Prediction. Adv. Intell. Syst. 2022. https://doi.org/10.1002/aisy.202200042.
David, W. I. F.; Shankland, K. Structure Determination from Powder Diffraction Data. Acta Crystallographica Section A: Foundations of Crystallography. 2008. https://doi.org/10.1107/S0108767307064252.
Krauss, I. R.; Merlino, A.; Vergara, A.; Sica, F. An Overview of Biological Macromolecule Crystallization. International Journal of Molecular Sciences. 2013. https://doi.org/10.3390/ijms140611643.
Jumper, J.; Evans, R.; Pritzel, A.; Green, T.; Figurnov, M.; Ronneberger, O.; Tunyasuvunakool, K.; Bates, R.; Žídek, A.; Potapenko, A.; Bridgland, A.; Meyer, C.; Kohl, S. A. A.; Ballard, A. J.; Cowie, A.; Romera-Paredes, B.; Nikolov, S.; Jain, R.; Adler, J.; Back, T.; Petersen, S.; Reiman, D.; Clancy, E.; Zielinski, M.; Steinegger, M.; Pacholska, M.; Berghammer, T.; Bodenstein, S.; Silver, D.; Vinyals, O.; Senior, A. W.; Kavukcuoglu, K.; Kohli, P.; Hassabis, D. Highly Accurate Protein Structure Prediction with AlphaFold. Nature 2021. https://doi.org/10.1038/s41586-021-03819-2.
Greasley, J.; Hosein, P. Exploring Supervised Machine Learning for Multi-Phase Identification and Quantification from Powder X-Ray Diffraction Spectra. J. Mater. Sci. 2023. https://doi.org/10.1007/s10853-023-08343-4.
Schuetzke, J.; Benedix, A.; Mikut, R.; Reischl, M. Enhancing Deep-Learning Training for Phase Identification in Powder X-Ray Diffractograms. IUCrJ 2021. https://doi.org/10.1107/S2052252521002402.
Chakraborty, A.; Sharma, R. A Deep Crystal Structure Identification System for X-Ray Diffraction Patterns. Vis. Comput. 2022. https://doi.org/10.1007/s00371-021-02165-8.
Suzuki, Y.; Hino, H.; Hawai, T.; Saito, K.; Kotsugi, M.; Ono, K. Symmetry Prediction and Knowledge Discovery from X-Ray Diffraction Patterns Using an Interpretable Machine Learning Approach. Sci. Rep. 2020. https://doi.org/10.1038/s41598-020-77474-4.
Jin, C.; Netrapalli, P.; Ge, R.; Kakade, S. M.; Jordan, M. I. On Nonconvex Optimization for Machine Learning. J. ACM 2021. https://doi.org/10.1145/3418526.
Dauphin, Y. N.; De Vries, H.; Bengio, Y. Equilibrated Adaptive Learning Rates for Non-Convex Optimization. In Advances in Neural Information Processing Systems; 2015.
Martí-Rujas, J. Structural Elucidation of Microcrystalline MOFs from Powder X-Ray Diffraction. Dalt. Trans. 2020. https://doi.org/10.1039/d0dt02802a.
Pan, Z.; Cheung, E. Y.; Harris, K. D. M.; Constable, E. C.; Housecroft, C. E. A Case Study in Direct-Space Structure Determination from Powder x-Ray Diffraction Data: Finding the Hydrate Structure of an Organic Molecule with Significant Conformational Flexibility. Cryst. Growth Des. 2005. https://doi.org/10.1021/cg050212d.
Howard, J.; Gugger, S. Deep Learning for Coders with Fastai and PyTorch. O’Reilly Media 2020.
Saha, S.; Vignarajan, J.; Frost, S. A Fast and Fully Automated System for Glaucoma Detection Using Color Fundus Photographs. Sci. Reports 2023 131 2023, 13 (1), 1–11. https://doi.org/10.1038/s41598-023-44473-0.
Mathivanan, S. K.; Sonaimuthu, S.; Murugesan, S.; Rajadurai, H.; Shivahare, B. D.; Shah, M. A. Employing Deep Learning and Transfer Learning for Accurate Brain Tumor Detection. Sci. Reports 2024 141 2024, 14 (1), 1–15. https://doi.org/10.1038/s41598-024-57970-7.
Esteva, A.; Kuprel, B.; Novoa, R. A.; Ko, J.; Swetter, S. M.; Blau, H. M.; Thrun, S. Dermatologist-Level Classification of Skin Cancer with Deep Neural Networks. Nat. 2017 5427639 2017, 542 (7639), 115–118. https://doi.org/10.1038/nature21056.
Ho, J.; Jain, A.; Abbeel, P. Denoising Diffusion Probabilistic Models. In Advances in Neural Information Processing Systems; 2020; Vol. 2020-December.
Chao, C. H.; Sun, W. F.; Cheng, B. W.; Lo, Y. C.; Chang, C. C.; Liu, Y. L.; Chang, Y. L.; Chen, C. P.; Lee, C. Y. Denoising Likelihood Score Matching for Conditional Score-based Data Generation. In ICLR 2022 - 10th International Conference on Learning Representations; 2022.
Graulis, S.; Chateigner, D.; Downs, R. T.; Yokochi, A. F. T.; Quirós, M.; Lutterotti, L.; Manakova, E.; Butkus, J.; Moeck, P.; Le Bail, A. Crystallography Open Database - An Open-Access Collection of Crystal Structures. J. Appl. Crystallogr. 2009, 42 (4). https://doi.org/10.1107/S0021889809016690.
Axelrod, S.; Gómez-Bombarelli, R. GEOM, Energy-Annotated Molecular Conformations for Property Prediction and Molecular Generation. Sci. Data 2022. https://doi.org/10.1038/s41597-022-01288-4
Krizhevsky, A.; Sutskever, I.; Hinton, G. E. ImageNet Classification with Deep Convolutional Neural Networks. Commun. ACM 2017. https://doi.org/10.1145/3065386.
He, K.; Zhang, X.; Ren, S.; Sun, J. Deep Residual Learning for Image Recognition. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition; 2016. https://doi.org/10.1109/CVPR.2016.90.
Huang, G.; Liu, Z.; Van Der Maaten, L.; Weinberger, K. Q. Densely Connected Convolutional Networks. In Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017; 2017. https://doi.org/10.1109/CVPR.2017.243.
Dosovitskiy, A.; Beyer, L.; Kolesnikov, A.; Weissenborn, D.; Zhai, X.; Unterthiner, T.; Dehghani, M.; Minderer, M.; Heigold, G.; Gelly, S.; Uszkoreit, J.; Houlsby, N. An image is worth 16X16 words: Transformers for image recognition at scale. In ICLR 2021 - 9th International Conference on Learning Representations; 2021.
Simonyan, K.; Zisserman, A. Very Deep Convolutional Networks for Large-Scale Image Recognition. In 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings; 2015.
Liu, Z.; Lin, Y.; Cao, Y.; Hu, H.; Wei, Y.; Zhang, Z.; Lin, S.; Guo, B. Swin Transformer: Hierarchical Vision Transformer Using Shifted Windows. In Proceedings of the IEEE International Conference on Computer Vision; 2021. https://doi.org/10.1109/ICCV48922.2021.00986.
Liu, Z.; Hu, H.; Lin, Y.; Yao, Z.; Xie, Z.; Wei, Y.; Ning, J.; Cao, Y.; Zhang, Z.; Dong, L.; Wei, F.; Guo, B. Swin Transformer V2: Scaling Up Capacity and Resolution. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition; 2022. https://doi.org/10.1109/CVPR52688.2022.01170.
Vaswani, A.; Brain, G.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, Ł.; Polosukhin, I. Attention Is All You Need. 2023.
Hoogeboom, E.; Satorras, V. G.; Vignac, C.; Welling, M. Equivariant Diffusion for Molecule Generation in 3D. In Proceedings of Machine Learning Research; 2022; Vol. 162.
Rombach, R.; Blattmann, A.; Lorenz, D.; Esser, P.; Ommer, B. High-Resolution Image Synthesis with Latent Diffusion Models. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition; 2022; Vol. 2022-June. https://doi.org/10.1109/CVPR52688.2022.01042.
Peebles, W.; Xie, S. Scalable Diffusion Models with Transformers. In Proceedings of the IEEE International Conference on Computer Vision; 2023. https://doi.org/10.1109/ICCV51070.2023.00387.
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spelling Macías López, Mario Albertovirtual::19838-1Rincón Carvajal, Sergio IvánMiscione, Gian PietroReiber, Andreas2024-08-02T18:42:28Z2025-07-312024-072024-08-02https://hdl.handle.net/1992/74919instname:Universidad de los Andesreponame:Repositorio Institucional Sénecarepourl:https://repositorio.uniandes.edu.co/La difracción de rayos X (DRX) es una técnica ampliamente empleada para el análisis de compuestos no reportados. Particularmente, la DRX de monocristal se emplea rutinariamente para resolver estructuras cristalinas, a diferencia de su contraparte la DRX de polvo. Este hecho se debe a que es difícil determinar una estructura cristalina sin ambigüedad utilizando DRX de polvo. En este estudio se presenta una primera aproximación a la resolución de estructuras cristalinas a partir de DRX de polvo mediante modelos de inteligencia artificial.Pregrado38 páginasapplication/pdfspaUniversidad de los AndesQuímicaFacultad de CienciasDepartamento de QuímicaAttribution-NonCommercial 4.0 Internationalhttp://creativecommons.org/licenses/by-nc/4.0/http://purl.org/coar/access_right/c_f1cf http://purl.org/coar/access_right/c_f1cfAplicación de inteligencia artificial en la resolución de estructuras cristalinas a partir de difracción de rayos X de polvoTrabajo de grado - Pregradoinfo:eu-repo/semantics/bachelorThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_7a1fTexthttp://purl.org/redcol/resource_type/TPDifracción de rayos XInteligencia artificialModelos de difusiónModelos de clasificación de imágenesQuímicaPark, W. B.; Chung, J.; Jung, J.; Sohn, K.; Singh, S. P.; Pyo, M.; Shin, N.; Sohn, K. S. Classification of Crystal Structure Using a Convolutional Neural Network. IUCrJ 2017. https://doi.org/10.1107/S205225251700714X.Harris, K. D. M.; Tremayne, M.; Kariuki, B. M. Contemporary Advances in the Use of Powder X-Ray Diffraction for Structure Determination. Angewandte Chemie - International Edition. 2001. https://doi.org/10.1002/1521-3773(20010504)40:9<1626::aid-anie16260>3.0.co;2-7.Harris, K. D. M.; Williams, P. A. Structure Determination of Organic Molecular Solids from Powder X-Ray Diffraction Data: Current Opportunities and State of the Art. In Advances in Organic Crystal Chemistry: Comprehensive Reviews 2015; 2015. https://doi.org/10.1007/978-4-431-55555-1_8.Lee, B. Do; Lee, J.-W.; Park, W. B.; Park, J.; Cho, M.-Y.; Pal Singh, S.; Pyo, M.; Sohn, K.-S. Powder X‐Ray Diffraction Pattern Is All You Need for Machine‐Learning‐Based Symmetry Identification and Property Prediction. Adv. Intell. Syst. 2022. https://doi.org/10.1002/aisy.202200042.David, W. I. F.; Shankland, K. Structure Determination from Powder Diffraction Data. Acta Crystallographica Section A: Foundations of Crystallography. 2008. https://doi.org/10.1107/S0108767307064252.Krauss, I. R.; Merlino, A.; Vergara, A.; Sica, F. An Overview of Biological Macromolecule Crystallization. International Journal of Molecular Sciences. 2013. https://doi.org/10.3390/ijms140611643.Jumper, J.; Evans, R.; Pritzel, A.; Green, T.; Figurnov, M.; Ronneberger, O.; Tunyasuvunakool, K.; Bates, R.; Žídek, A.; Potapenko, A.; Bridgland, A.; Meyer, C.; Kohl, S. A. A.; Ballard, A. J.; Cowie, A.; Romera-Paredes, B.; Nikolov, S.; Jain, R.; Adler, J.; Back, T.; Petersen, S.; Reiman, D.; Clancy, E.; Zielinski, M.; Steinegger, M.; Pacholska, M.; Berghammer, T.; Bodenstein, S.; Silver, D.; Vinyals, O.; Senior, A. W.; Kavukcuoglu, K.; Kohli, P.; Hassabis, D. Highly Accurate Protein Structure Prediction with AlphaFold. Nature 2021. https://doi.org/10.1038/s41586-021-03819-2.Greasley, J.; Hosein, P. Exploring Supervised Machine Learning for Multi-Phase Identification and Quantification from Powder X-Ray Diffraction Spectra. J. Mater. Sci. 2023. https://doi.org/10.1007/s10853-023-08343-4.Schuetzke, J.; Benedix, A.; Mikut, R.; Reischl, M. Enhancing Deep-Learning Training for Phase Identification in Powder X-Ray Diffractograms. IUCrJ 2021. https://doi.org/10.1107/S2052252521002402.Chakraborty, A.; Sharma, R. A Deep Crystal Structure Identification System for X-Ray Diffraction Patterns. Vis. Comput. 2022. https://doi.org/10.1007/s00371-021-02165-8.Suzuki, Y.; Hino, H.; Hawai, T.; Saito, K.; Kotsugi, M.; Ono, K. Symmetry Prediction and Knowledge Discovery from X-Ray Diffraction Patterns Using an Interpretable Machine Learning Approach. Sci. Rep. 2020. https://doi.org/10.1038/s41598-020-77474-4.Jin, C.; Netrapalli, P.; Ge, R.; Kakade, S. M.; Jordan, M. I. On Nonconvex Optimization for Machine Learning. J. ACM 2021. https://doi.org/10.1145/3418526.Dauphin, Y. N.; De Vries, H.; Bengio, Y. Equilibrated Adaptive Learning Rates for Non-Convex Optimization. In Advances in Neural Information Processing Systems; 2015.Martí-Rujas, J. Structural Elucidation of Microcrystalline MOFs from Powder X-Ray Diffraction. Dalt. Trans. 2020. https://doi.org/10.1039/d0dt02802a.Pan, Z.; Cheung, E. Y.; Harris, K. D. M.; Constable, E. C.; Housecroft, C. E. A Case Study in Direct-Space Structure Determination from Powder x-Ray Diffraction Data: Finding the Hydrate Structure of an Organic Molecule with Significant Conformational Flexibility. Cryst. Growth Des. 2005. https://doi.org/10.1021/cg050212d.Howard, J.; Gugger, S. Deep Learning for Coders with Fastai and PyTorch. O’Reilly Media 2020.Saha, S.; Vignarajan, J.; Frost, S. A Fast and Fully Automated System for Glaucoma Detection Using Color Fundus Photographs. Sci. Reports 2023 131 2023, 13 (1), 1–11. https://doi.org/10.1038/s41598-023-44473-0.Mathivanan, S. K.; Sonaimuthu, S.; Murugesan, S.; Rajadurai, H.; Shivahare, B. D.; Shah, M. A. Employing Deep Learning and Transfer Learning for Accurate Brain Tumor Detection. Sci. Reports 2024 141 2024, 14 (1), 1–15. https://doi.org/10.1038/s41598-024-57970-7.Esteva, A.; Kuprel, B.; Novoa, R. A.; Ko, J.; Swetter, S. M.; Blau, H. M.; Thrun, S. Dermatologist-Level Classification of Skin Cancer with Deep Neural Networks. Nat. 2017 5427639 2017, 542 (7639), 115–118. https://doi.org/10.1038/nature21056.Ho, J.; Jain, A.; Abbeel, P. Denoising Diffusion Probabilistic Models. In Advances in Neural Information Processing Systems; 2020; Vol. 2020-December.Chao, C. H.; Sun, W. F.; Cheng, B. W.; Lo, Y. C.; Chang, C. C.; Liu, Y. L.; Chang, Y. L.; Chen, C. P.; Lee, C. Y. Denoising Likelihood Score Matching for Conditional Score-based Data Generation. In ICLR 2022 - 10th International Conference on Learning Representations; 2022.Graulis, S.; Chateigner, D.; Downs, R. T.; Yokochi, A. F. T.; Quirós, M.; Lutterotti, L.; Manakova, E.; Butkus, J.; Moeck, P.; Le Bail, A. Crystallography Open Database - An Open-Access Collection of Crystal Structures. J. Appl. Crystallogr. 2009, 42 (4). https://doi.org/10.1107/S0021889809016690.Axelrod, S.; Gómez-Bombarelli, R. GEOM, Energy-Annotated Molecular Conformations for Property Prediction and Molecular Generation. Sci. Data 2022. https://doi.org/10.1038/s41597-022-01288-4Krizhevsky, A.; Sutskever, I.; Hinton, G. E. ImageNet Classification with Deep Convolutional Neural Networks. Commun. ACM 2017. https://doi.org/10.1145/3065386.He, K.; Zhang, X.; Ren, S.; Sun, J. Deep Residual Learning for Image Recognition. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition; 2016. https://doi.org/10.1109/CVPR.2016.90.Huang, G.; Liu, Z.; Van Der Maaten, L.; Weinberger, K. Q. Densely Connected Convolutional Networks. 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