Tunnelling of heavy atoms in double well potentials using analytical model
Vazquez-Salazar, L.I. (2010). "Tunnelling of heavy atoms in double well potentials using analytical mode." Journal 1. 1(2).
CV
Education
- B.Sc. in Chemistry, National Autonomous University of Mexico, 2017
- M.Sc. in Chemistry, University of Groningen, 2019
- Ph.D. in Chemistry, University of Basel, 2024
Work experience
- SNSF Postdoctoral Fellow (2024)
- Institute for Theoretical Physics, Heidelberg University
- Application of machine learning generative models for backmapping structures from Coarse-Graining to All-Atom resolution.
- Supervisor: Prof. Dr. Tristan Bereau
- Lecturer (2017)
- Facultad de Quimica, UNAM:
- Two lectures: Fundamentos de espectroscopia (3rd Semester BSc.Chemistry) and estructura de la materia (2nd Semester BSc. Chemistry)
- Research Assistant (2015-2016)
- Instituto de Geofisica, UNAM
- Mathematical and computational modelling of multiscale phenomena applied to fluid dynamics.
- Supervisor: Professor Emeritus Ismael Herrera
Skills
- Coding: Python, Fortran, Bash, Mathematica, LaTex
- Molecular Modelling Software: Gromacs, Gaussian, CHARMM, ORCA, MOLPRO
Publications
MARTINI Coarse Grained models of imidazolium-based ionic liquids: from nanostructural organization to liquid-liquid extractions
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.
Impact of the Characteristics of Quantum Chemical Databases on Machine Learning Prediction of Tautomerization Energies
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
Uncertainty quantification for predictions of atomistic neural networks
Vazquez-Salazar, L. I., Boittier E.D., and Meuwly M.(2022)."Uncertainty quantification for predictions of atomistic neural networks." Chemical Science.
Talks
MARTINI coarse-grained modelling of imidazolium-based ionic liquids
Talk at European Conference on Computational and Theoretical Chemistry, Perugia, Italy
More data or better data? How the training data influences machine learned predictions in Chemistry
Talk at Barcelona MMSML Workshop: Methods in Molecular Simulations and Machine Learning, Barcelona, Spain
Towards better chemical databases for atomistic machine learning
Talk at ACS Fall: Harnessing the Power of Data, San Francisco, California, United States