Rowan Seminar Series
Rowan regularly invites people working in chemistry and computer science to present their research and work to our team to promote scientific learning, communication, and community building.
14. Simone Gallarati (University of Utah & UCLA) - "Transition state featurization enables efficient selectivity predictions and reaction optimization in a sparse data regime"
June 20, 2025
13. Nick Casetti (MIT) - "Exploration of Cyclization Reaction Pathways with Neural Network Potentials"
June 13, 2025
12. Jackson Burns (MIT) - "Solid Solubility Prediction at the Limit of Experimental Accuracy"
April 24, 2025
Selected papers:11. Justin Airas (MIT) - "Developing a broadly applicable graph neural network-based implicit solvent model"
April 10, 2025
Selected papers:- Transferable Implicit Solvation via Contrastive Learning of Graph Neural Networks
- Scaling Graph Neural Networks to Large Proteins
10. Michael Hla - "Pro-1"
March 27, 2025
Selected papers:9. Andreas Copan (University of Georgia) - "Computer-Aided Kinetic Mechanism Generation"
March 14, 2025
Selected papers:- The role of stereochemistry in combustion processes
- Radical Stereochemistry: Accounting for Diastereomers in Kinetic Mechanism Development
8. Marcus Sak (Harvard)
January 31, 2025
7. Jonas Rein (MIT) - "Adventures in Oxoammonium Catalysis"
December 3, 2024
Selected papers:- Generality-oriented optimization of enantioselective aminoxyl radical catalysis
- Oxoammonium-Catalyzed Oxidation of N-Substituted Amines
6. Hannes Stärk and Bowen Jing (MIT) - "Diffusion Generative Models for Biomolecules"
October 17, 2024
Selected papers:- AlphaFold Meets Flow Matching for Generating Protein Ensembles
- Dirichlet Flow Matching with Applications to DNA Sequence Design
- DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking
5. Justin Smith (NVIDIA)
September 26, 2024
Selected papers:- Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential
- Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning
- Less is more: Sampling chemical space with active learning
- ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost
4. Zach Fried (MIT) - "Integrating Experimental and Theoretical Methods for Interstellar Molecular Discoveries"
August 29, 2024
Selected papers:- Rotational Spectrum and First Interstellar Detection of 2-Methoxyethanol Using ALMA Observations of NGC 6334I
- Automated Mixture Analysis via Structural Evaluation
3. Tim Duignan (Griffith) - "Simulating Electrolytes"
July 26, 2024
Selected papers:- The Potential of Neural Network Potentials
- Accurate, fast and generalisable first principles simulation of aqueous lithium chloride
2. Lucas Abounader (Caltech)
July 1, 2024
1. Eugene Kwan (Merck) - "Computational Physical Organic Chemistry"
June 13, 2024
Selected papers: