Publications

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

(*) denotes equal contribution; (^†) denotes corresponding author

Peer-Reviewed Journal Papers

DOI Preprint Code Akshay Subramanian, James Damewood, Juno Nam, Kevin P. Greenman, Avni P. Singhal, Rafael Gómez-Bombarelli. Symmetry-constrained generation of diverse low-bandgap molecules with Monte Carlo tree search. Chemical Science 2025, 16, 10503–10511.

DOI Preprint Code Kevin P. Greenman, Ava P. Amini, Kevin K. Yang. Benchmarking uncertainty quantification for protein engineering. PLOS Computational Biology 2025, 21(1), e1012639.

DOI Preprint Code Esther Heid, Kevin P. Greenman, Yunsie Chung, Shih-Cheng Li, David E. Graff, Florence H. Vermeire, Haoyang Wu, William H. Green, Charles J. McGill. Chemprop: A Machine Learning Package for Chemical Property Prediction. Journal of Chemical Information and Modeling 2024, 64(1), 9–17.

DOI Preprint Code 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, Klavs F. Jensen. Autonomous, multiproperty-driven molecular discovery: from predictions to measurements and back. Science 2023, 382(6677), eadi1407.

DOI Preprint Code Akshay Subramanian*, Kevin P. Greenman*, Alexis Gervaix, Tzuhsiung Yang, Rafael Gómez-Bombarelli. Automated patent extraction powers generative modeling in focused chemical spaces. Digital Discovery 2023, 2(4), 1006–1015.

DOI Simon Axelrod, Daniel Schwalbe-Koda, Somesh Mohapatra, James Damewood, Kevin P. Greenman, Rafael Gómez-Bombarelli. Learning Matter: Materials Design with Machine Learning and Atomistic Simulations. Accounts of Materials Research 2022, 3(3), 343–357.

DOI Preprint Code Kevin P. Greenman, William H. Green, Rafael Gómez-Bombarelli. Multi-fidelity prediction of molecular optical peaks with deep learning. Chemical Science 2022, 13(4), 1152–1162.

Paper Salwan Butrus, Kevin Greenman, Eshita Khera, Irina Kopyeva, Akira Nishii. An Undergraduate-Led, Research-Based Course that Complements a Traditional Chemical Engineering Curriculum. Chemical Engineering Education 2020, 54(2).

DOI Preprint Code Kevin Greenman, Logan Williams, Emmanouil Kioupakis. Lattice-constant and band-gap tuning in wurtzite and zincblende BInGaN alloys. Journal of Applied Physics 2019, 126, 055702.

Conference Workshop Papers

Nofit Segal, Aviv Netanyahu, Kevin P. Greenman, Pulkit Agrawal, Rafael Gómez-Bombarelli. Known Unknowns: Out-of-Distribution Property Prediction in Materials and Molecules. NeurIPS AI4Mat Workshop 2024. Spotlight.

Akshay Subramanian, James Damewood, Juno Nam, Kevin P. Greenman, Avni P. Singhal, Rafael Gómez-Bombarelli. Symmetry-Constrained Generation of Diverse Low-Bandgap Molecules with Monte Carlo Tree Search. NeurIPS AI4Mat Workshop 2024.

Kevin P. Greenman, Ava P. Amini, Kevin K. Yang. Benchmarking Uncertainty Quantification for Protein Engineering. ICLR Machine Learning for Drug Discovery (MLDD) Workshop 2022.

Preprint / Submitted / Under Review

Preprint Code Nofit Segal, Aviv Netanyahu, Kevin P. Greenman, Pulkit Agrawal, Rafael Gómez-Bombarelli. Known Unknowns: Out-of-Distribution Property Prediction in Materials and Molecules. arXiv 2025, 2502.05970.

In Preparation

David E. Graff, Nathan K. Morgan, Jackson W. Burns, Anna C. Doner, Brian Li, Shih-Cheng Li, Joel Manu, Angiras Menon, Hao-Wei Pang, Haoyang Wu, Akshat Shirish Zalte, Jonathan W. Zheng, Connor W. Coley, William H. Green^†, Kevin P. Greenman^†. Chemprop v2: An Efficient, Modular Machine Learning Package for Chemistry. (In preparation).

Yizhe Chen, Shomik Verma, Kevin P. Greenman, Haoyu Yin, Zhihao Wang, Lanjing Wang, Jiali Li, Rafael Gomez-Bombarelli, Aron Walsh, Xiaonan Wang. A unified active learning framework for photosensitizer design. (In preparation).

Kevin P. Greenman, Temujin Orkhon, William H. Green, Rafael Gómez-Bombarelli. Multi-Fidelity Deep Learning for Data-Efficient Molecular Property Models from Experimental and Computational Data. (In preparation).

Kevin P. Greenman, Rui-Xi Wang, Juno Nam, Akshay Subramanian, Jurgis Ruza, Joonyoung F. Joung, Minhi Han, William H. Green, Sungnam Park, Rafael Gómez-Bombarelli. Benchmarking predictions of near-infrared absorption with physics-based and machine learning methods. (In preparation).

Other

Kevin Greenman, Peilin Liao. Computational Catalysis: Creating a User-Friendly Tool for Research and Education. The Summer Undergraduate Research Fellowship Symposium 2018, Paper 129.

DOI Kevin Greenman, Peilin Liao. Computational Catalysis with Density Functional Theory. nanoHUB resource 2018.