You can also find my articles on my Google Scholar profile.


  1. Kevin P. Greenman, Ava P. Soleimany, and Kevin K. Yang, “Benchmarking Uncertainty Quantification for Protein Engineering”. (In preparation).

Peer-Reviewed Journals

  1. Simon Axelrod, Daniel Schwalbe-Koda, Somesh Mohapatra, James Damewood, Kevin P. Greenman, and Rafael Gómez-Bombarelli, “Learning Matter: Materials Design with Machine Learning and Atomistic Simulations”. Acc. Mater. Res. (2022), 3(3), 343–357. DOI: 10.1021/accountsmr.1c00238. (Paper)
  2. Kevin P. Greenman, William H. Green, and Rafael Gómez-Bombarelli, “Multi-fidelity prediction of molecular optical peaks with deep learning”. Chemical Science (2022), 13(4), 1152 - 1162. DOI: 10.1039/D1SC05677H. (Paper | Code)
  3. Salwan Butrus, Kevin Greenman, Eshita Khera, Irina Kopyeva, and Akira Nishii, “An Undergraduate-Led, Research-Based Course that Complements a Traditional Chemical Engineering Curriculum”. Chemical Engineering Education (2020), 54(2). (Paper)
  4. Kevin Greenman, Logan Williams, and Emmanouil Kioupakis, “Lattice-constant and band-gap tuning in wurtzite and zincblende BInGaN alloys”. J. Appl. Phys. (2019), 126(055702). DOI: 10.1063/1.5108731. (Paper | Code)


  1. Kevin Greenman and Peilin Liao (2018), “Computational Catalysis: Creating a User-Friendly Tool for Research and Education”. The Summer Undergraduate Research Fellowship (SURF) Symposium. Paper 129.
  2. Kevin Greenman and Peilin Liao (2018), “Computational Catalysis with Density Functional Theory,”