Talks
Invited Talks
Kevin P. Greenman. “Artificial Intelligence and Physics-Based Simulations for (Bio)Molecular and Materials Design”. Physics Department, University of Rome Tor Vergata (Rome, Italy) (November 2025).
Kevin P. Greenman. “Optical Property Prediction and Molecular Discovery through Multi-Fidelity Deep Learning and Computational Chemistry”. MIT Signals Information and Algorithms (Prof. Gregory Wornell) Group Meeting (Virtual) (September 2025).
Kevin P. Greenman. “AI-driven chemistry research and undergraduate skill development with Chemprop”. ACS Fall Meeting, Washington, D.C., USA (August 2025).
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, Rui-Xi Wang, Joonyoung F. Joung, Minhi Han, William H. Green, Sungnam Park, Rafael Gómez-Bombarelli. “Toward Accurate Prediction of Near-Infrared Absorption: Physics-Based Calculations, Machine Learning, and the Crucial Role of Data”. AIChE Annual Meeting, Boston, MA, USA (November 2025).
Kevin P. Greenman, Temujin Orkhon, William H. Green, Rafael Gómez-Bombarelli. “Data-Driven Strategy Selection for Multi-Fidelity Modeling in Autonomous Molecular Discovery”. AIChE Annual Meeting, Boston, MA, USA (November 2025).
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).