RH
Rodrigo Vargas Hernandez
Principal Investigator
McMaster University
I'm working on
10 months ago
Quantum chemistry, quantum computing, materials modeling, machine learning
About
Research integrates machine learning with quantum and molecular simulations, focusing on data‑efficient modeling, Bayesian optimization, inverse design, and differentiable programming. Contributes to open‑source quantum software and coarse‑grained materials modeling for accelerated discovery.
Bayesian optimization for high-dimensional coarse-grained model parameterization: a case study on Pebax polymer
Spiral (Imperial College London)
•2026
Meta-Learning Fourier Neural Operators for Hessian Inversion and Enhanced Variational Data Assimilation
arXiv (Cornell University)
•2025
Discrete Flow-Based Generative Models for Measurement Optimization in Quantum Computing
arXiv (Cornell University)
•2025
LivePyxel: Accelerating image annotations with a Python-integrated webcam live streaming
arXiv (Cornell University)
•2025
Scalable Bayesian Optimization for High-Dimensional Coarse-Grained Model Parameterization
arXiv (Cornell University)
•2025
GFlowNets for Hamiltonian decomposition in groups of compatible operators
ML4Phys Neurips Workshop
•2024
Spectro: A multi-modal approach for molecule elucidation using IR and NMR data
AI4Mat Neurips workshop
•2024
MOLPIPx: an end-to-end differentiable package for permutationally invariant polynomials in Python and Rust
arXiv (Cornell University)
•2024
Leveraging normalizing flows for orbital-free density functional theory
Machine Learning Science and Technology
•2024
Quantum Deep Equilibrium Models
arXiv (Cornell University)
•2024
Orbital-Free Density Functional Theory with Continuous Normalizing Flows
arXiv (Cornell University)
•2023
Fully differentiable optimization protocols for non-equilibrium steady states
New Journal of Physics 23 (12), 123006, 2021
Inverse molecular design and parameter optimization with Hückel theory using automatic differentiation
The Journal of Chemical Physics 158 (10), 104801, 2023
Gaussian Processes with Spectral Delta kernel for higher accurate Potential Energy surfaces for large molecules
arXiv preprint arXiv:2109.14074, 2021
Multi-objective optimization for retinal photoisomerization models with respect to experimental observables
The Journal of chemical physics 155 (23), 234109, 2021
Bayesian optimization for the inverse scattering problem in quantum reaction dynamics
New Journal of Physics 21 (2), 022001, 2019
Inverse design of dissipative quantum steady-states with implicit differentiation
arXiv preprint arXiv:2011.12808, 2020
Softmax parameterization of the occupation numbers for natural orbital functionals based on electron pairing approaches
The Journal of Chemical Physics 160 (24), 2024
Engineering extended Hubbard models with Zeeman excitations of ultracold Dy atoms
Journal of Physics B: Atomic, Molecular and Optical Physics 49 (23), 235501, 2016
Applications of machine learning for solving complex quantum problems
University of British Columbia, 2018
Physical extrapolation of quantum observables by generalization with Gaussian Processes
Machine Learning Meets Quantum Physics, 171-194, 2020
Modern applications of machine learning in quantum sciences
arXiv preprint arXiv:2204.04198, 2022
Pennylane: Automatic differentiation of hybrid quantum-classical computations
arXiv preprint arXiv:1811.04968, 2018
Neural networks vs Gaussian process regression for representing potential energy surfaces: A comparative study of fit quality and vibrational spectrum accuracy
The Journal of chemical physics 148 (24), 241702, 2018
Bayesian Optimization for Calibrating and Selecting Hybrid-Density Functional Models
The Journal of Physical Chemistry A 124 (20), 4053-4061, 2020
Assessing Gaussian process regression and permutationally invariant polynomial approaches to represent high-dimensional potential energy surfaces
Journal of chemical theory and computation 14 (7), 3381-3396, 2018
Extrapolating quantum observables with machine learning: Inferring multiple phase transitions from properties of a single phase
Physical review letters 121 (25), 255702, 2018
Fast evaluation of the adsorption energy of organic molecules on metals via graph neural networks
Nature Computational Science, 1-10, 2023
Six-dimensional potential energy surface for NaK–NaK collisions: Gaussian process representation with correct asymptotic form
The Journal of chemical physics 150 (6), 064106, 2019
Differentiable quantum computational chemistry with PennyLane
arXiv preprint arXiv:2111.09967, 2021
Machine-learning-corrected quantum dynamics calculations
Phys. Rev. Research 2 (3), 032051(R), 2020
Advanced Biomanufacturing Innovations
Canadian Insitute of Health Research (CIHR)
•$25,000
•2024
In-Silico Material Design in the Era of Machine Learning
Natural Sciences and Engineering Research Council (NSERC)
•$116,000
•2024
Enhancing computational chemistry with machine learning
Digital Research Alliance of Canada
•$1
•2023
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