Profile photo

Rafael R. Del Grande

Postdoctoral Scholar at University of California, Merced

Home Publications Portfolio Contact

Short Bio:

I am a computational physicist with a PhD in condensed matter, specialized in first-principles simulations of materials, high-performance computing, scientific software development, and automation. I have experience in both academia and industry, working on problems in nanoscience, energy, and material science. I am currently expanding into machine learning focusing on AI-driven materials simulations, discovery, and design.

You can find my CV here: CV (PDF)
You can find my publications on Publications or at Google Scholar
You can find my github profile at Github
My ORCID: ORCID

Experience

Education

  • Ph.D. in Physics, Federal University of Rio de Janeiro – June 2021. NAMOR group Phd Thesis (PDF) (Advisors: Marcos G. Menezes, Rodrigo B. Capaz)
  • M.Sc. in Physics, Federal University of Rio de Janeiro – February 2017. NAMOR group Master Thesis (PDF) (Advisors: Marcos G. Menezes, Rodrigo B. Capaz)
  • B.Sc. in Nanoscience and Nanotechnology, Federal University of Rio de Janeiro – February 2015

    • Awards and Honors

  • 2020 - Bolsa nota 10 - PhD excellence fellowship from FAPERJ (Rio de Janeiro state funding agency)

    • Awarded computational grants

  • Texas Advanced Computing Center (TACC) Frontera Pathways. Title: Ab initio excited-state forces from GW/BSE and DFPT calculations: applications to perovskites and 2D materials. Role: PI. Amount: 102,000 CPU/GPU Node Hours. Year: 2023-2025. Co-PI: David A. Strubbe
  • San Diego Supercomputer Center (through ACCESS). Title: Excited-state forces from GW/BSE and DFPT calculations. Role: PI. Amount: 10,000 CPU Node Hours and 93 GPU Node Hours. Year: 2022-2023. Co-PI: David A. Strubbe

    • Research interests:

    Theoretical condensed matter physics and material science. Computational simulation of materials, ab initio methods (DFT, GW approximation, Bethe-Salpeter equation, DFPT), molecular dynamics, tight binding. Electron-phonon and exciton-phonon interactions. Dynamics of excited states. Semiconductors, 2D materials, carbon nanotubes. Defects, heterostructures. Machine learning applied to computational material science. Scientific software development for high-performance computing (CPU and GPU).