You can also find my articles on my Google Scholar profile.
- Kevin P. Greenman, Ava P. Soleimany, and Kevin K. Yang, “Benchmarking Uncertainty Quantification for Protein Engineering”. (In preparation).
- 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)
- 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)
- 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)
- 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)
- 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.
- Kevin Greenman and Peilin Liao (2018), “Computational Catalysis with Density Functional Theory,” https://nanohub.org/resources/28763.