Naveen Arunachalam

Naveen Arunachalam

Graduate Student

Massachusetts Institute of Technology

Naveen joined the Kulik group in December 2018 as a PhD student in Chemical Engineering. He received his B.S. in Chemical Engineering from Caltech in Spring 2018. At Caltech, he worked with Prof. Thomas Miller on ion diffusion in polymers and with Prof. William Goddard on olfactory protein structure determination. Currently, Naveen’s research is focused on using machine learning and experimental databases to inform inorganic catalyst discovery. Naveen holds an NSF Graduate Research Fellowship.

  • materials design
  • machine learning
  • chemical discovery
  • BS in Chemical Engineering, 2018



  1. A Transferable Recommender Approach for Selecting the Best Density Functional Approximations in Chemical Discovery (2023)
  2. Ligand Additivity Relationships Enable Efficient Exploration of Transition Metal Chemical Space (2022)
  3. MOFSimplify, machine learning models with extracted stability data of three thousand metal–organic frameworks (2022)
  4. Representations and Strategies for Transferable Machine Learning Improve Model Performance in Chemical Discovery (2022)
  5. Large-scale comparison of 3d and 4d transition metal complexes illuminates the reduced effect of exchange on second-row spin-state energetics (2020)