Alvaro Vazquez-Mayagoitia, PhD

9700 S Cass Ave, Lemont, IL, 60439 · Office:240/1128 · (630) 252-0171 · [email protected]

I am a Computational Scientist working at Argonne National Laboratory in the Computational Science Division.

My expertise is in Computational Chemistry and Data Science in extreme scale environments.

Full CV


I am interested in applications and in the development for scientific codes for chemistry and materials to solve challenging problems, particularly, in those cases where the scientific problems only can be solved using leadership class supercomputers.

I am constantly looking for projects in chemistry, materials science, and artificial intelligence, that could use ALCF resources at capability pursuing high impact scientific outcomes. An important fraction of my time is dedicated to service those projects to prepare proposals, review and give them support.

I conduct research to study materials for solar cells, bioplastics, and energy storage.


  • Development of High Performance Computing codes for atomistic simulations

    • Porting parallel codes to accelerator base architectures
    • Benchmarking and tuning parallel algorithms in Leadership class DOE computers
  • AI/ML applied to chemical properties

    • Prediction of global and local properties
    • Data mining of literature and population of datasets with experimental and simulated data
    • Generation of reference data and training to produce ML Force Fields with ab initio accuracy
  • Molecular weak interactions

    • BSSE corrections of energies and geometries
    • Semiempirical and Double Hybrid Functionals in &out of equilibrium structures
  • DFT approach to chemical reactivity

    • Local, global and condensed reactivity indexes
    • Use of AIM theories, own code to compute Hirshfeld Population Analysis
    • Studies on substituent effect


Álvaro Vázquez-Mayagoitia is an expert in computational chemistry and data science. His experience spans both methods and applications of electronic structure theory with high-performance computing.

He joined the Argonne National Laboratory at the Argonne Leadership Computing Facility in 2011, as part of an Early Science Program ALCF-2. In 2013, he accepted a position as part of the ALCF Computational Science team. As a member of that team, Alvaro actively participated in the continuous enhancement of features and performance of a number of quantum chemistry codes, including NWChem, BigDFT, Quantum-Espresso, MADNESS, VASP and FHI-aims. With the goal of efficiently using ALCF resources and accelerating simulations, he has worked on the optimization of codes and libraries for Argonne’s petascale systems, like Intrepid, Mira, Theta (Intel KNL), and ThetaGPU.

In 2018, Álvaro joined the Computational Science Division where he provides support, advice and training for scientific projects in fields related to chemistry and materials science. Most recently, he has assisted in the preparation and review of proposals for grants sponsored by the U.S. Department of Energy and the National Science Foundation.

Prior to joining ANL, Alvaro held a postdoctoral position at Oak Ridge National Laboratory and the University of Tennessee working with MADNESS and NWChem codes. He developed tools for molecular spectroscopy and evaluated weak interactions.

In addition to supporting teams participating in Early Science, INCITE and ALCC projects, Álvaro is collaborating with multiple projects in the ALCF Data Science Program that are deploying software for Machine Learning approaches and Complex Workflows to accelerate chemical and materials discovery. His work with these projects has contributed to advancements in predicting organic dyes to improve solar cells, and in the study of inner forces of molecular crystals with applications in medicine and electronics.

Álvaro also led the ALCF Postdoctoral Committee, where he managed the recruitment and hiring of postdoctoral researchers to work on both computational science projects and software development efforts for next-generation supercomputers. As part of this role, he is actively involved in mentoring ALCF postdocs to ensure they are developing the skills and experience needed to advance their careers.


  • Nwchem, NwchemEX


  • FHIaims


  • MOLAN, Active Sampling, miniGAP