Talks

Invited

1. Kevin P. Greenman, “Message-Passing Neural Networks for Molecular Property Prediction Using Chemprop”, nanoHUB Hands-on Data Science and Machine Learning Training Series (Virtual), April 2022. (nanoHUB | YouTube)
2. Kevin P. Greenman, “Fast, Accurate, and Generalizable Prediction of Molecular Optical Properties from Multi-fidelity Data”, ARPA-E DIFFERENTIATE Meeting, Carnegie Mellon University (Virtual), March 2022.
3. Charles McGill, Michael Forsuelo, and Kevin Greenman, “An Introduction to Chemprop”, Enko (Virtual), February 2022.

Contributed

1. Kevin P. Greenman, William H. Green, and Rafael Gómez-Bombarelli. “Multi-Fidelity Deep Learning and Active Learning for Molecular Optical Properties” International Symposium on Molecular Spectroscopy. Urbana, IL, June 2022 (Accepted).
2. Kevin P. Greenman, William H. Green, and Rafael Gómez-Bombarelli. “Transfer Learning for Prediction of Absorption and Emission Spectra from Multi-fidelity Data”. American Institute of Chemical Engineers Annual Meeting. Boston, MA, November 2021.
3. Kevin P. Greenman, Simon Axelrod, William H. Green, and Rafael Gómez-Bombarelli. “Predicting absorption spectra of molecular dyes using deep learning”. American Chemical Society Spring Meeting. Virtual, April 2021.
4. Kevin Greenman, Logan Williams, and Emmanouil Kioupakis. “Lattice Constant and Band Gap Tuning in BInGaN Alloys for Next-Generation LEDs”. American Physical Society March Meeting. Boston, MA, March 2019.
5. Kevin Greenman, “Computational Catalysis – Creating a User-Friendly Tool for Research and Education”. nanoHUB 3-minute Research Talk. West Lafayette, IN, August 2018. (nanoHUB)
6. Kevin Greenman and Peilin Liao. “Computational Catalysis: Creating a User-Friendly Tool for Research and Education”. Purdue Summer Undergraduate Research Fellowship (SURF) Symposium. West Lafayette, IN, August 2018.