Talks

Invited Talks

Kevin P. Greenman. “Chemprop and Related Projects”. Axiom Bio Journal Club (Virtual) (May 2025).

Kevin P. Greenman. “Multi-fidelity deep learning for data-efficient molecular property models from experimental and computational data”. MIT Machine Learning in Biology Working Group, Cambridge, MA, USA (May 2024).

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

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).

Contributed Talks

Kevin P. Greenman. “Harnessing AI for Scientific Discovery: Achievements, Opportunities, and Ethical Reflections”. Society of Catholic Scientists Conference, Mundelein, IL, USA (June 2024). YouTube

Kevin Greenman. “Chemprop v1.7.1 & v2.0.0: Stable Release and Benchmarks”. Machine Learning for Pharmaceutical Discovery and Synthesis Consortium Meeting, Cambridge, MA, USA (April 2024).

Kevin Greenman. “Chemprop v2.0.0: New Features and Updates”. Machine Learning for Pharmaceutical Discovery and Synthesis Consortium Meeting, Cambridge, MA, USA (October 2023).

Kevin P. Greenman, Akshay Subramanian, Alexis Gervaix, Rafael Gómez-Bombarelli. “Automatic chemical dataset generation, labeling, and modeling from patent literature queries”. ACS Fall Meeting, Chicago, IL, USA (August 2022).

Kevin P. Greenman, William H. Green, Rafael Gómez-Bombarelli. “Multi-Fidelity Deep Learning and Active Learning for Molecular Optical Properties”. International Symposium on Molecular Spectroscopy, Urbana, IL, USA (June 2022).

Kevin P. Greenman, William H. Green, Rafael Gómez-Bombarelli. “Transfer Learning for Prediction of Absorption and Emission Spectra from Multi-fidelity Data”. AIChE Annual Meeting, Boston, MA, USA (November 2021).

Kevin P. Greenman, Simon Axelrod, William H. Green, Rafael Gómez-Bombarelli. “Predicting absorption spectra of molecular dyes using deep learning”. ACS Spring Meeting (Virtual) (April 2021).

Kevin Greenman, Logan Williams, Emmanouil Kioupakis. “Lattice Constant and Band Gap Tuning in BInGaN Alloys for Next-Generation LEDs”. APS March Meeting, Boston, MA, USA (March 2019).

Kevin Greenman. “Computational Catalysis – Creating a User-Friendly Tool for Research and Education”. nanoHUB 3-minute Research Talk, West Lafayette, IN, USA (August 2018).

Workshop Talks

Kevin P. Greenman, Haoyang Wu, William H. Green. “Chemprop: Datasets and Machine Learning Software for Chemical Property Prediction”. Division of Catalysis Science and Technology (CATL) – Open Source Software Workshops, ACS Fall Meeting, San Francisco, CA, USA (August 2023). Code

Charles McGill, Michael Forsuelo, Kevin P. Greenman. “An Introduction to Chemprop”. Enko (Virtual) (February 2022).