I develop computational and quantitative approaches to understand complex systems across the physical, mathematical, and educational sciences.

Computational Molecular Science

Matter exhibits an extraordinary diversity of dynamical behaviours across molecular and nanoscale systems. My research investigates these phenomena through atomistic simulations, molecular dynamics, and statistical mechanics, with particular emphasis on gas–surface interactions, nanoparticles, porous materials, and nonequilibrium processes.

The objective is to reveal the microscopic mechanisms governing molecular motion, energy transfer, adsorption, diffusion, and chemical dynamics, providing fundamental insights relevant to materials science, surface chemistry, and nanotechnology.

Latest publications:

  • J.C. Castro-Palacio, H. Thake, R. Maurer, G. Held and R. Grau-Crespo. Understanding the Chiral Modification of the Cu{111} Surface with Aspartic Acid (manuscript in preparation).
  • M. Urbiztondo, J.C. Castro-Palacio, R. Grau-Crespo and S. Hamad. Gradient-Aware Genetic Algorithms for Global Optimization of Molecular Adsorption on Surfaces, submitted to ACS Materials Au (May 2026).

Mathematical Modelling and Stochastic Systems

Many natural, economic, and social phenomena are inherently uncertain. My research develops probabilistic and computational methods to describe stochastic systems and quantify uncertainty across diverse application domains.
Current work focuses on probability theory, stochastic analysis, and quantitative modelling, with applications ranging from social and behavioural sciences to quantitative finance. By combining rigorous mathematics with computational methods, this research seeks to explain how randomness shapes the behaviour of complex systems.

Latest publications:

Quantitative Science of Education

Our educational research applies quantitative methods to investigate how students acquire scientific knowledge and how instructional practices can be improved through evidence-based approaches.
Research focuses on higher education, physics education, learning analytics, and the statistical evaluation of teaching innovations, contributing to the development of more effective educational environments and scientifically informed pedagogical practices.

Latest publications:

  • L. Velazquez, B. Atenas, N. Cruz Hernández, J. C. Castro Palacio, and J. A. Monsoriu, Credit Allocation Matters: From Workload Misalignment to Student Progression and Graduation Costs, submitted to Studies in Higher Education.
  • B. Atenas, L. Velazquez, N. Cruz Hernández, J. C. Castro Palacio, and J. A. Monsoriu, Efficiency limits of graduation rates in structured curricula: Insights from solvable stochastic models, submitted to Chaos: An Interdisciplinary Journal of Nonlinear Science.