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Pages
Luis Itza Vazquez-Salazar
About me
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Posts
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Blog Post number 1
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portfolio
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publications
Tunnelling of heavy atoms in double well potentials using analytical model
Published in To be Submitted, 2010
Double well potentials
Recommended citation: Vazquez-Salazar, L.I. (2010). "Tunnelling of heavy atoms in double well potentials using analytical mode." Journal 1. 1(2).
MARTINI Coarse Grained models of imidazolium-based ionic liquids: from nanostructural organization to liquid-liquid extractions
Published in Green Chemistry, 2020
Martini IL
Recommended citation: Vazquez-Salazar, L. I., Selle, M., De Vries, A. H., Marrink, S. J., and Souza, P. C. (2020)." Martini coarse-grained models of imidazolium-based ionic liquids: from nanostructural organization to liquid–liquid extraction." Green Chemistry., 22(21), 7376-7386. https://doi.org/10.1039/D0GC01823F
Impact of the Characteristics of Quantum Chemical Databases on Machine Learning Prediction of Tautomerization Energies
Published in Journal of Chemical Theory and Computation, 2021
Tautomers and Machine Learning
Recommended citation: Vazquez-Salazar, L. I., Boittier, E., Unke, O. T., and Meuwly, M.(2021)." Impact of the characteristics of quantum chemical databases on machine learning predictions of tautomerization energies." Journal of Chemical Theory and Computation.,17 (8), 4769-4785 https://pubs.acs.org/doi/10.1021/acs.jctc.1c00363
Uncertainty quantification for predictions of atomistic neural networks
Published in Chemical Science, 2022
Uncertainty Quantification
Recommended citation: Vazquez-Salazar, L. I., Boittier E.D., and Meuwly M.(2022)."Uncertainty quantification for predictions of atomistic neural networks." Chemical Science. https://doi.org/10.1039/D2SC04056E
talks
MARTINI coarse-grained modelling of imidazolium-based ionic liquids
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I presented the most relevant results of my master thesis.
More data or better data? How the training data influences machine learned predictions in Chemistry
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Nowadays, Machine Learning(ML) methods routinely achieve high accuracy in short times and are becoming another standard tool for computational/theoretical chemists. However, ML methods require large quantities of data to train them to achieve the desired/required results.
The generation of data for chemical applications is not a trivial task and requires hours of computation from ab-initio methods. Nevertheless, the rule of thumb from computer science that large amounts of data will beat the best algorithms is still followed. Keeping in mind that the generation of data is not always feasible or practical, we reviewed the influence of common databases on the training of ML models. Our results indicate that common databases present redundancies that reduce the quality of prediction of an ML model. Therefore, there is a need to ‘clean’ and augment databases to assure the best prediction and, at the same time, obtain a procedure for the creation of databases with the minimum amount of computational effort.
Towards better chemical databases for atomistic machine learning
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Machine learning (ML) has revolutionized the field of atomistic simulations. It is now possible to obtain high-quality predictions of chemical properties such as total energies, forces, or dipoles at a low computational cost. Currently, the field is at a stage at which atomistic simulations in the gas phase on the sub-microsecond time scale with ab-initio MP2 quality can be carried out routinely. Given that the computational effort to evaluate such a statistical model is independent of the quality of the input data, the most significant bottleneck for devising yet better ML models is the considerable amount of data required to train them. Although the community consensus is that more data naturally leads to better performance; it has been found that this working hypothesis is not necessarily correct for predicting chemical properties with models trained on commonly used databases such as QM9 or ANI-1. Consequently, there is a need to identify how to obtain suitable data for training ML models and for established databases on how to add/remove information while retaining the best performance of the model.
teaching
Estructura de la Materia
Undergraduate course, Facultad de Quimica, UNAM, 2017
The course is an introduction to quantum mechanics in chemistry for undergraduate students in chemistry, chemical engineering ,farmaceutical chemistry and food chemistry.
Fundamentos de Espectroscopia
Undergraduate course, Facultad de Quimica, UNAM, 2017
The course that I taught was the practical (laboratory) part of the course of fundamentals of spectroscopy. The course is an introductions to the physics of oscillations and waves additionally some topics of acoustics and optics are revisted. It is oriented to students of the BSc. Chemistry on their 2nd year (3rd Semester).