Center for Theoretical and Computational Chemistry
Department of Chemistry | University of Warwick
Danjo De Chavez
Bio
I am Danjo De Chavez, a computational chemist and machine learning researcher originally from the Philippines and currently based in the United Kingdom. I am presently a Marie Skłodowska-Curie Actions Postdoctoral Fellow at University of Warwick, where I develop next-generation machine learning-based methods for multiscale quantum chemical simulations, orbital-free density functional theory, and kinetic energy functionals.
My research lies at the intersection of quantum chemistry, computational chemistry, and artificial intelligence, with a focus on developing efficient and scalable computational tools to solve complex chemical and materials science problems. Over the years, I have held research appointments in Japan, Sweden, Australia, and the United Kingdom, collaborating with world-leading scientists and contributing to internationally recognized open-source software projects such as OpenMolcas and FHI-aims.
I am passionate about pushing the boundaries of theoretical chemistry through method development, scientific programming, and interdisciplinary collaboration.
Education
My academic journey has always been deeply rooted in computational research.
I earned my Bachelor of Science in Biochemistry from University of the Philippines Manila, where I began my research career in computer-aided drug discovery. My undergraduate thesis focused on homology modeling, pharmacophore generation, and molecular docking for antimalarial drug design. After graduation, I joined academia as a junior faculty member and became involved in teaching laboratory courses while expanding my research experience in classical molecular dynamics and computational chemistry.
I was later awarded the prestigious Japanese Government (Monbukagakusho) Scholarship for both my Master’s and PhD studies at Hokkaido University in Japan. During this period, I joined a computational catalysis laboratory where I specialized in density functional theory (DFT) and investigated mechanocatalysis and mechanochemistry. My doctoral work focused on understanding how mechanical forces influence catalytic activity and reaction selectivity. During this time, I also developed simulation and analysis tools, including OpenMechanochem, a Python-based module for mechanochemical simulations.
Early Career
Toward the end of my PhD, my work in computational mechanochemistry received research funding, allowing me to continue as a Postdoctoral Researcher at Hokkaido University. There, I further developed computational tools and studied the mechanistic effects of external mechanical stress on chemical reactions.
While this opportunity allowed me to deepen my expertise, I recognized the importance of broadening my scientific scope. To expand into more fundamental quantum chemical theory, I moved to Uppsala University in Sweden, where I worked under Professor Roland Lindh as a postdoctoral researcher in quantum chemical method development. My work focused on advancing electronic structure methods in OpenMolcas, including improvements to MP2, CASPT2, Cholesky decomposition, and machine learning applications for multiconfigurational quantum chemistry.
I then joined CSIRO in Australia as a Visiting Scientist, where I worked on machine learning-based molecular property prediction and reinforcement learning-driven molecular design.
Currently at University of Warwick, I continue to expand my research into quantum embedding methods, nonadiabatic molecular dynamics, and AI-driven quantum simulations, with the long-term goal of building intelligent computational frameworks that accelerate discovery in chemistry, catalysis, and materials science.