Schulz, Hannes

A generative model for inorganic materials design

Zeni, C., Pinsler, R., Zügner, D., Fowler, A., Horton, M., Fu, X., Wang, Z., Shysheya, A., Crabbé, J., Ueda, S., et al. (2025) · Nature

Machine Learning in Materials ScienceComputational Drug Discovery Methods

Cited by 518

Scalable emulation of protein equilibrium ensembles with generative deep learning

Lewis, S., Hempel, T., Jiménez-Luna, J., Gastegger, M., Xie, Y., Foong, A. Y. K., Satorras, V. G., Abdin, O., Veeling, B. S., Zaporozhets, I., et al. (2025) · Science

Protein Structure and DynamicsComputational Drug Discovery Methods

Cited by 390

Scalable emulation of protein equilibrium ensembles with generative deep learning

Lewis, S., Hempel, T., Jiménez Luna, J., Gastegger, M., Xie, Y., Foong, A. Y. K., García Satorras, V., Abdin, O., Veeling, B. S., Zaporozhets, I., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory)

Protein Structure and DynamicsComputational Drug Discovery Methods

Cited by 349

Towards Deep Conversational Recommendations

Li, R., Kahou, S., Schulz, H., Michalski, V., Charlin, L., & Pal, C. (2018) · arXiv

Recommender Systems and TechniquesSpeech and dialogue systems

Cited by 242