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Leandro Seixas

  • Physicist
  • Professor
  • Full Stack Developer
  • AI Engineer

Computational materials scientist and full stack developer. Researching materials through computational simulations and machine learning, and developing the tools for these simulations.

See my work Academic CV
Based at
Ilum – School of Science, CNPEM
Focus
2D materials & energy systems
Published
30 peer-reviewed papers

Things I'm building

Software that puts computational materials science in more hands.

All projects
Poraquê logo

Poraquê

Machine learning density-based operators.

Predicts the charge density and the kinetic energy density of a crystal straight from its geometry, skipping the self-consistency cycle that makes DFT expensive.

Python In development poraque.seixas.dev
Calango logo

Calango

Visual atomistic modeling.

Builds, edits and inspects atomic structures visually, with a C++ core that stays responsive on systems large enough to stall a typical viewer.

C++ In development calango.seixas.dev

Four dimensions, one practice

The same questions keep showing up in different rooms. These are the rooms.

Physicist

Electronic structure of low-dimensional materials, from topological insulators to phosphorene, studied from first principles.

  • DFT
  • 2D Materials
  • Quantum Materials
  • Alloys
  • Ab Initio Simulations

Professor

Nine years of higher education teaching across quantum mechanics, thermodynamics, solid state physics and programming.

  • Solid state physics
  • Quantum mechanics
  • Thermodynamics
  • Programming

Scientific Applications Full Stack Developer

Frontend and Backend

Research software carried the whole way: the numerical core and the API that serves it, the interface that makes a result readable, and the deployment that keeps it available to the people who need it.

  • Python
  • C++
  • Flask
  • HPC
  • Open source

AI Engineer

Models for the physical sciences

Models that learn physics from simulation data and then stand in for it: neural operators that predict electron densities directly from a crystal geometry, and machine-learned interatomic potentials that run the dynamics ab initio methods cannot reach. The whole pipeline is the job, from generating and curating the training set to validating a model against the physics and serving it in production.

  • PyTorch
  • Neural operators
  • ML interatomic potentials
  • Training pipelines
  • Materials informatics

A short introduction

Get in touch

I am a computational materials scientist specialising in ab initio simulations and machine learning for atomistic modelling. My research centres on two-dimensional materials and energy systems, combining Density Functional Theory with data-driven methods for predictive materials design.

That path started in theoretical physics, in topological insulators, phosphorene and other two-dimensional materials, and moved steadily towards the computational side: first the simulations, then the software that runs them, and now the models that let us skip ahead of the calculation entirely.

Somewhere along the way the software stopped being a side effect of the research and became part of the work itself. I build scientific applications full stack: the numerical core and the API that serves it on the backend, the interface that makes a result readable on the frontend, and the deployment that keeps the whole thing running.

Over ten years of research and nine years of university teaching sit behind that, alongside more than thirty papers and the tools I build to make this kind of work easier to reach.

Selected research

All publications