Álvaro Vázquez‐Mayagoitia

Argonne National Laboratory — United States

Materials Science · Physical Sciences

22h-index2.8kcitations106works2.12yr avg
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Research Topics

Machine Learning in Materials Science(33), Computational Drug Discovery Methods(20), Advanced Chemical Physics Studies(13), X-ray Diffraction in Crystallography(9), Spectroscopy and Quantum Chemical Studies(8)

Publications106 total

Fusing Delta Learning and Machine Learned Interatomic Potentials for Efficient High Throughput Calculation of Surface Kinetic Parameters
2026Open Access
Graph Neural Network models to predict redox potentials, and Iron(II) complexes redox dataset
Zenodo (CERN European Organization for Nuclear Research)·2026Open Access
Redox potential prediction of Fe( <scp>ii</scp> )/Fe( <scp>iii</scp> ) complexes: a density functional theory and graph neural network approach
Digital Discovery·2026Open Access
GAP model for Hf and O developed by Sivaraman, Gallington, and Krishnamoorthy et al. (2021) v000
Open Knowledgebase of Interatomic Models (OpenKIM)·2026Open Access
Reciprocal Space Attention for Learning Long-Range Interactions
arXiv (Cornell University)·2025Open Access
Automated Learning of GNN Ensembles for Predicting Redox Potentials with Uncertainty
2025Open Access
SYCL for Performance Portability: Application Experience with Coupled Cluster Formalism in Quantum Chemistry on Exascale Systems
2025
Aurora: Architecting Argonne's First Exascale Supercomputer for Accelerated Scientific Discovery
arXiv (Cornell University)·2025Open Access
Redox Potential Prediction of Fe(II)/Fe(III) Complexes: A Density Functional Theory and Graph Neural Network Approach
2025Open Access
COLUMBUS─An Efficient and General Program Package for Ground and Excited State Computations Including Spin–Orbit Couplings and Dynamics
The Journal of Physical Chemistry A·2025· 5 citedOpen Access
Efficient Implementation of Monte Carlo Algorithms on Graphical Processing Units for Simulation of Adsorption in Porous Materials
Journal of Chemical Theory and Computation·2024· 19 cited
Intro to HPC Bootcamp: Engaging New Communities Through Energy Justice Projects
The Journal of Computational Science Education·2024· 1 citedOpen Access
NWChem and NWChemEx Plane-Wave Methods
Elsevier eBooks·2023· 3 cited
Gaussian Approximation Potentials for Accurate ThermalProperties of Two-Dimensional Materials
2023Open Access
Metadynamics enhanced training Dataset and DFT-SCAN accurate GAP Model for amorphous and liquid HfO2
Figshare·2023Open Access
Gaussian approximation potentials for accurate thermal properties of two-dimensional materials
Nanoscale·2023· 14 citedOpen Access
“Freedom of design” in chemical compound space: towards rational<i>in silico</i>design of molecules with targeted quantum-mechanical properties
Chemical Science·2023· 13 citedOpen Access
In‐Silico Device Performance Prediction of Cosensitizer Dye Pairs for Dye‐Sensitized Solar Cells
Advanced Energy Materials·2022· 5 citedOpen Access
Observation of site-selective chemical bond changes via ultrafast chemical shifts
Nature Communications·2022· 18 citedOpen Access
A Combined Machine Learning and High-Energy X-ray Diffraction Approach to Understanding Liquid and Amorphous Metal Oxides
Journal of the Physical Society of Japan·2022· 11 citedOpen Access
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Frequent Co-authors

Ganesh Sivaraman(8), Jorge Garza(7), Rubicelia Vargas(7), Jacqueline M. Cole(6), Bobby G. Sumpter(6), Chris J. Benmore(4), Venkatram Vishwanath(4), Alán Aspuru‐Guzik(4), Murat Keçeli(4), Miguel Fuentes‐Cabrera(4), Noa Marom(3), Alexandre Tkatchenko(3), Marius Stan(3), Gábor Cśanyi(3), William Huhn(3), Volker Blüm(3), Chao Yang(3), Edward J. Beard(3), Nicholas E. Jackson(3), Benjamín Sánchez-Lengeling(3)