Tiago Botari

Senior Researcher, Scientific Machine Learning @ ASML

I work at the intersection of scientific machine learning, materials science, and engineering. I started out in computational physics, simulating 2D materials and dynamic systems, and gradually moved into materials informatics and text-mined databases. These days I build physics-informed and hybrid ML models that bring simulations and experimental data together, using them to advance materials for the semiconductor industry and to crack problems in inverse modeling, metrology, and defect inspection on the chip line. I care a lot about keeping these models interpretable and well-calibrated, so the AI behind them stays trustworthy and easy to audit.

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Materials Science × Machine Learning

I started in computational materials science — running atomistic simulations of 2D materials such as silicene, graphene, and carbon nitrides, and building text-mined materials databases and composition–property models. That foundation now shapes how I apply machine learning: grounding data-driven models in physics so they stay reliable on real materials and manufacturing problems.

Atomistic Simulation (DFT, MD) 2D Materials Materials Informatics Composition–Property Prediction Physics-Informed ML Surrogate Models