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Research

Hai Pham-Van Lab

Research Themes

Our research connects fundamental statistical physics with computational materials science and functional nanostructures. We combine analytical theory, numerical simulation, first-principles calculations, machine learning, and experimental collaboration to uncover organizing principles and design materials for energy, sensing, and wave control.

Gauge Symmetry and Statistical Mechanics

Classical, quantum, and open-system statistical physics organized by symmetry and geometry.

We develop gauge- and geometry-based descriptions of many-body statistical mechanics. The work focuses on exact Ward identities, force and hyperforce relations, quantum statistical geometry, orientational gauge structure, and reservoir-relative covariance in open classical and quantum dynamics. The broader goal is to use symmetry to reveal universal constraints on equilibrium, response, relaxation, and dissipation.

Representative research directions

  • Unified symmetry structures for equilibrium and nonequilibrium many-body systems.
  • Classical and quantum gauge connections, sum rules, and response identities.
  • Orientational gauge symmetry in molecular and anisotropic fluids.
  • Reservoir-induced covariance breaking, relaxation bounds, and dissipation in open systems.

Selected publications

Colloidal Self-Assembly and Soft Matter

Predicting how particle geometry and interactions create finite clusters and ordered soft materials.

We investigate how particle shape, surface patchiness, confinement, and droplet evaporation organize colloids into finite clusters and ordered structures. Statistical-mechanical models and particle-based simulations are used to predict assembly pathways, structural transitions, phase behavior, and collective dynamics, with links to colloidal molecules, photonic structures, and optical metamaterials.

Representative research directions

  • Evaporation-driven assembly of spherical, patchy, Janus, and dumbbell colloids.
  • Cluster geometry, formation kinetics, and structural transitions under confinement.
  • Phase behavior and crystal design in colloid–droplet mixtures.
  • Connections between self-assembled nanoclusters and plasmonic or metamaterial response.

Selected publications

Computational Materials Physics and AI for Energy

First-principles and data-driven discovery of materials for photocatalysis and sustainable hydrogen.

We combine density-functional theory, ab initio molecular dynamics, phonon and electronic-structure calculations, and machine learning to discover and understand materials for photocatalysis and hydrogen production. Particular attention is paid to two-dimensional materials, MXenes, defect and vacancy engineering, band-edge alignment, adsorption energetics, optical response, and experimentally relevant stability.

Representative research directions

  • Data-driven screening of MXenes and other two-dimensional catalysts for the hydrogen evolution reaction.
  • Defect, oxygen-vacancy, doping, and phase engineering of photocatalytic semiconductors.
  • Electronic, optical, transport, thermal, and dynamical stability from first principles.
  • Physics-informed descriptors and interpretable machine learning for accelerated materials discovery.

Selected publications

  • A two-stage machine-learning framework for the discovery of stable boron-based and sulfur-terminated MXenes as promising hydrogen evolution reaction catalysts International Journal of Hydrogen Energy 242, 155583 (2026). DOI: 10.1016/j.ijhydene.2026.155583
  • Favourable bandgap energies of two-dimensional MgAl2X4 (X = S/Se/Te) semiconductors for solar water splitting: A first-principles investigation Chemical Physics 607, 113197 (2026). DOI: 10.1016/j.chemphys.2026.113197
  • Chladni-mode selection rules for phonon-driven hydrogen evolution on 2D catalysts Physica Scripta 101, 035906 (2026). DOI: 10.1088/1402-4896/ae35b3
  • Effect of Oxygen-Vacancy Concentration on the Structural, Electronic, and Optical Properties of Bi2WO6 Photocatalyst: A DFT Study Journal of Electronic Materials 55, 7954–7967 (2026). DOI: 10.1007/s11664-026-12994-9

Functional Nanomaterials, Plasmonics, and Metamaterials

Architected materials for ultrasensitive sensing, electromagnetic-wave control, and functional devices.

We study nanostructured materials whose optical, electromagnetic, magnetic, or sensing performance emerges from controlled architecture. The research includes SERS-active plasmonic substrates, porous-silicon microcavities, graphene–nanotube hybrids, coding metamaterial absorbers, and magnetic nanoparticles, developed through numerical modeling, materials fabrication, spectroscopy, and collaborative experiments.

Representative research directions

  • Hybrid plasmonic–photonic structures for ultrasensitive molecular and environmental sensing.
  • Silver, gold, graphene, carbon-nanotube, and porous-silicon platforms for SERS.
  • Broadband microwave and terahertz absorbers based on coding and defect-engineered metamaterials.
  • Functional magnetic and oxide nanomaterials for sensing, environmental, and biomedical applications.

Selected publications

  • Silver Nanoparticles-Decorated Porous Silicon Microcavity as a High-Performance SERS Substrate for Ultrasensitive Detection of Trace-Level Molecules Nanomaterials 15, 1007 (2025). DOI: 10.3390/nano15131007
  • Reduced graphene oxide-carbon nanotubes nanocomposites-decorated porous silver nanodendrites for highly efficient SERS sensing Optical Materials 162, 116935 (2025). DOI: 10.1016/j.optmat.2025.116935
  • Broadband microwave coding metamaterial absorbers Scientific Reports 10, 1810 (2020). DOI: 10.1038/s41598-020-58774-1
  • Optimizing fabrication parameters of Fe3O4 nanoparticles for enhancing magnetic hyperthermia efficiency Materials Chemistry and Physics 343, 130983 (2025). DOI: 10.1016/j.matchemphys.2025.130983
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