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

(*) denotes equal contribution

Submitted / Preprint / Under Review

  1. Kevin P. Greenman, Ava P. Amini, and Kevin K. Yang, “Benchmarking Uncertainty Quantification for Protein Engineering”. bioRxiv, DOI: 10.1101/2023.04.17.536962. (Submitted). (Preprint | Code)
  2. Brent A. Koscher*, Richard B. Canty*, Matthew A. McDonald*, Kevin P. Greenman, Charles J. McGill, Camille L. Bilodeau, Wengong Jin, Haoyang Wu, Florence H. Vermeire, Brooke Jin, Travis Hart, Timothy Kulesza, Shih-Cheng Li, Tommi S. Jaakola, Regina Barzilay, Rafael Gómez-Bombarelli, William H. Green, and Klavs F. Jensen. “Autonomous, multi-property-driven molecular discovery: from predictions to measurements and back”. DOI: 10.26434/chemrxiv-2023-r7b01. (Submitted). (Preprint)
  3. Akshay Subramanian*, Kevin P. Greenman*, Alexis Gervaix, Tzuhsiung Yang, and Rafael Gómez-Bombarelli. “Automated patent extraction powers generative modeling in focused chemical spaces”. arXiv:2303.08272. (Submitted). (Preprint | Code)

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 | Preprint | 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 | Preprint | 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,”