About Me

Anthony Val C. Camposano

Postdoctoral researcher, Linköping University, Sweden

I work in computational physics and molecular simulation. My current research uses machine learning to model biopolymer self-assembly. Previously, I developed reactive models for water and silica and worked on quantum transport simulations.

Research overview · Google Scholar · GitHub

Research interests

Molecular dynamics and Monte Carlo simulation; machine learning for molecular systems; reactive force fields; scientific computing.

Research and education

• Postdoctoral research, Linköping University (April 2026–present). Machine learning models of biopolymer self-assembly.
• PhD in Physics, University of Oslo (October 2021–December 2025). Reactive silica–water models using the Vashishta potential. Thesis: Parameterisation of a Reactive Silica-Water Model using the Vashishta Functional Form.
• MS in Physics, University of San Carlos (June 2017–July 2020). Quantum transport in nanostructures using the lattice Weyl-Wigner formalism. Thesis: 1D Density Functional Theory + Lattice Weyl-Wigner Function Quantum Transport Modelling.

Selected publications

1. Silica–water model using the Vashishta force field. The Journal of Chemical Physics (2026).
2. 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 (2025).
3. Genetic Algorithm Workflow for Parameterization of a Water Model Using the Vashishta Force Field. The Journal of Physical Chemistry B (2025).

Selected presentations

• Machine Learning an All-Atom Empirical Force Field for a Water-Silica System, European Geosciences Union General Assembly (2024).
• Non-equilibrium quantum transport using the lattice Weyl-Wigner formalism, Asian Network School and Workshop on Complex Condensed Matter Systems (poster, 2019).

Selected workshops and schools

• Flowing Matter in Porous Media, CECAM workshop (2025).
• Summer CINECA School: Dive into the World of HPC (2024).
• Machine Learning Interatomic Potentials: Theory and Practice, CECAM school (2023).