PhD candidate
Research group | CONDPHYS, NJORD
Main supervisor | Anders Malthe-Sørenssen
Co-supervisor | Henrik Andersen Sveinsson
Affiliation | Department of Physics, UiO
Contact | a.v.camposano@fys.uio.no
Short bio
I took my undergraduate degree from 2009 to 2014 at the University of San Carlos (Cebu, Philippines). For my thesis, I studied the spectrum of water clusters (H2O)n using density functional theory in ADF-SCM software. In 2015-2017 I was a research assistant under the PhilLidar project that aims to map the natural resources in the Philippines. To do this, we use machine learning tools to extract the appropriate hydrological resources from remote sensing data. From 2017 to 2020, I took my master's degree in the same university. I studied one-dimensional nanostructures using density functional theory in combination with superfield theory.
Currently, I'm a Ph.D. Research Fellow at the University of Oslo. I’ll be working under the project "Machine-learning-based molecular modelling of nanoscale geological processes".
Research interests and hobbies
My research interests are centered around the following topics: computational physics, complex system, condensed matter physics.
I spend my leisure time reading books and learning web development. I also like leisure walking and hiking.
CompSci project
Machine-learning-based molecular modelling of nanoscale geological processes
Geological processes shape the earth, erect mountains and shatter the crust during earthquakes. Many geological processes ultimately involve details at the nanoscale. For example, in reactions where rocks expand in the presence of water, atomic scale details may determine whether water can make it to the reaction site, or whether the reaction is shut off.
In this project, we will use machine learning to tailor models to specific nanoscale geological processes. We will traverse the traditionally infeasible parameter spaces of interatomic force fields and adapt them to the problem at hand. For example, if we want to investigate the properties of a water film between silica surfaces at extreme pressures, we train the potential to describe just that, using potentially both empirical and more fundamental calculated properties.
I will learn to use machine learning for optimizing interatomic force fields. I will also learn to run high-throughput atomistic simulations to incrementally improve simulations and analyses using various machine learning techniques. These skills are useful both in an academic and an industrial setting.
The project may include collaborations with the Center for Advanced Computing and Simulations at University of Southern California.
Doctoral thesis
Parameterization of a reactive silica-water model using the Vashishta functional form
Water is known to weaken materials like rock and glass, a critical process in everything from earthquake dynamics to the durability of ceramics. Computer simulations can help us understand this weakening at the atomic level, showing how water molecules interact with a material as it breaks. However, existing simulation methods have faced a trade-off: they are often too slow to model the millions of atoms needed to see a crack propagate realistically, or they are not accurate enough to capture the chemical reactions happening at the crack's tip.
This thesis presents a new, computationally efficient model that tackles this dilemma. We have developed a tool that is both fast and allows for chemical reactions by using a specific framework, the Vashishta potential, to model atomic interactions. We optimized the model to successfully simulate water's behavior over a wide range of conditions and its reaction with a silica surface. These capabilities enable larger and longer simulations, providing a powerful new resource for understanding material failure in aqueous environments in geology, materials science, and engineering.
Publications
CompSci publications
Anthony Val C. Camposano, Even Marius Nordhagen, Henrik Andersen Sveinsson, and Anders Malthe-Sørenssen (2025) "Genetic algorithm workflow for parameterization of a water model using the Vashishta force field" The Journal of Physical Chemistry B 129 (4), 1331-1342
https://doi.org/10.1021/acs.jpcb.4c06389 | Full text in Research Archive
Anthony Val C. Camposano, Even Marius Nordhagen, Anders Malthe-Sørenssen, and Henrik Andersen Sveinsson (2025) “An extended energy-biased aggregation-volume-bias Monte Carlo (EB-AVBMC) method for nucleation simulation of a reactive water potential” Journal of Chemical Theory and Computation 21 (14) 6769-6776
https://doi.org/10.1021/acs.jctc.5c00722 | Full text in Research Archive
Previous publications
None yet.
