Richard G. Hennig

According to our database, Richard G. Hennig authored at least 9 papers between 2015 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

PropMolFlow: property-guided molecule generation with geometry-complete flow matching.

Cheng Zeng, Jirui Jin, Connor Ambrose, George Karypis, Mark K. Transtrum, Ellad B. Tadmor, Richard G. Hennig, Adrian Roitberg, Stefano Martiniani, Mingjie Liu

Nat. Comput. Sci., March, 2026

MolCrystalFlow: Molecular Crystal Structure Prediction via Flow Matching.

Cheng Zeng, Harry W. Sullivan, Thomas Egg, Maya M. Martirossyan, Philipp Höllmer, Jirui Jin, Richard G. Hennig, Adrian Roitberg, Stefano Martiniani, Ellad B. Tadmor, Mingjie Liu

CoRR, February, 2026

2025

MolGuidance: Advanced Guidance Strategies for Conditional Molecular Generation with Flow Matching.

Jirui Jin, Cheng Zeng, Pawan Prakash, Ellad B. Tadmor, Adrian Roitberg, Richard G. Hennig, Stefano Martiniani, Mingjie Liu

CoRR, December, 2025

Guided Diffusion for the Discovery of New Superconductors.

Pawan Prakash, Jason B. Gibson, Zhongwei Li, Gabriele Di Gianluca, Juan Esquivel, Eric Fuemmeler, Benjamin Geisler, Jung Soo Kim, Adrian Roitberg, Ellad B. Tadmor, Mingjie Liu, Stefano Martiniani, Gregory R. Stewart, James J. Hamlin, Peter J. Hirschfeld, Richard G. Hennig

CoRR, September, 2025

All that structure matches does not glitter.

Maya M. Martirossyan, Thomas Egg, Philipp Höllmer, George Karypis, Mark K. Transtrum, Adrian Roitberg, Mingjie Liu, Richard G. Hennig, Ellad B. Tadmor, Stefano Martiniani

CoRR, September, 2025

Open Materials Generation with Stochastic Interpolants.

Philipp Höllmer, Thomas Egg, Maya M. Martirossyan, Eric Fuemmeler, Zeren Shui, Amit Gupta, Pawan Prakash, Adrian Roitberg, Mingjie Liu, George Karypis, Mark K. Transtrum, Richard G. Hennig, Ellad B. Tadmor, Stefano Martiniani

Proceedings of the Forty-second International Conference on Machine Learning, 2025

2024

When More Data Hurts: Optimizing Data Coverage While Mitigating Diversity Induced Underfitting in an Ultra-Fast Machine-Learned Potential.

Jason B. Gibson, Tesia D. Janicki, Ajinkya C. Hire, Chris Bishop, J. Matthew D. Lane, Richard G. Hennig

CoRR, 2024

Accelerating superconductor discovery through tempered deep learning of the electron-phonon spectral function.

Jason B. Gibson, Ajinkya C. Hire, Philip M. Dee, Oscar Barrera, Benjamin Geisler, Peter J. Hirschfeld, Richard G. Hennig

CoRR, 2024

2015

Comparison of polynomial approximations to speed up planewave-based quantum Monte Carlo calculations.

William D. Parker, C. J. Umrigar, Dario Alfè, F. R. Petruzielo, Richard G. Hennig, John W. Wilkins

J. Comput. Phys., 2015