Dr. Danjo De Chavez

Dr. Danjo De Chavez is a postdoctoral research fellow who joined the group in February 2025, bringing expertise in machine learning for quantum chemistry and non-adiabatic molecular dynamics.

Research Focus

Danjo’s work focuses on accelerating multiscale quantum simulations using machine learning methods. His research interests include:

• QM-in-QM Embedding: Developing frozen density embedding theory (FDET/DFET) methods in FHI-aims
• Machine Learning for Quantum Chemistry: Creating ML methods for multiconfigurational quantum chemistry
• Non-adiabatic Dynamics: Studying molecular dynamics on metal surfaces beyond Born-Oppenheimer approximation
• Mechanochemistry: Computational tools for analyzing chemical reactions under mechanical stress

Technical Expertise

Danjo is proficient in multiple programming languages including Python, R, FORTRAN, Bash, OpenMP, and MPI, and has experience with various chemical software packages.

Open-Source Contributions

Danjo is an active contributor to the scientific software community:

• OpenMolcas: Core developer
• OpenMechanochem: Creator of this Python module for mechanochemical simulations
• TrajView: Developer of this molecular trajectory viewer

Previous Experience

• Postdoctoral Researcher (2022-2024), Uppsala University: Developed machine learning methods for multiconfigurational quantum chemistry in OpenMolcas
• Postdoctoral Researcher (2021-2022), Hokkaido University: Developed computational tools for analyzing chemical reactions under mechanical stress

His diverse background in both method development and software implementation makes him a valuable addition to the computational chemistry community.