Gianmarco Terrones

Gianmarco Terrones

Graduate Student

Massachusetts Institute of Technology

Gianmarco joined the Kulik group in December 2020 as a PhD student in Chemical Engineering. He received his B.S. in Chemical Engineering from Caltech in June 2020. At Caltech, he worked in the Lewis group on bubble positioning in devices for solar fuel production and in the Callies group on a parallelized model for ocean dynamics. In the Kulik group, Gianmarco’s research concerns multi-objective earth abundant materials design.

Interests
  • machine learning
  • excited state properties
  • materials design
Education

  • BS in Chemical Engineering, 2020

    Caltech

Publications

  1. Data-driven Screening and Discovery of Metal-organic Frameworks as C2 Adsorbents from Over 900 Experimental Isotherms (2024)
  2. Metal–Organic Framework Stability in Water and Harsh Environments from Data-Driven Models Trained on the Diverse WS24 Data Set (2024)
  3. Visible light–mediated aza Paternò–Büchi reaction of acyclic oximes and alkenes to azetidines (2024)
  4. A semi-automated, high-throughput approach for the synthesis and identification of highly photo-cytotoxic iridium complexes (2024)
  5. Visible-Light-Mediated Macrocyclization for the Formation of Azetine-Based Dimers (2024)
  6. Machine Learning Prediction of the Experimental Transition Temperature of Fe(II) Spin-Crossover Complexes (2024)
  7. SESAMI APP: An Accessible Interface for Surface Area Calculation of Materials from Adsorption Isotherms (2023)
  8. A Database of Ultrastable MOFs Reassembled from Stable Fragments with Machine Learning Models (2023)
  9. Low-cost machine learning prediction of excited state properties of iridium-centered phosphors (2023)
  10. Active Learning Exploration of Transition Metal Complexes to Discover Method-Insensitive and Synthetically Accessible Chromophores (2023)
  11. Effects of MOF Linker Rotation and Functionalization on Methane Uptake and Diffusion (2023)
  12. MOFSimplify, machine learning models with extracted stability data of three thousand metal–organic frameworks (2022)