Schwaller, Philippe

Two-dimensional materials from high-throughput computational exfoliation of experimentally known compounds

Mounet, N., Gibertini, M., Schwaller, P., Campi, D., Merkys, A., Marrazzo, A., Sohier, T., Castelli, I. E., Cepellotti, A., Pizzi, G., et al. (2018) · Nature Nanotechnology

Machine Learning in Materials Science2D Materials and Applications

Cited by 1,863

Augmenting large language models with chemistry tools

M. Bran, A., Cox, S., Schilter, O., Baldassari, C., White, A. D., & Schwaller, P. (2024) · Nature Machine Intelligence

Machine Learning in Materials ScienceComputational Drug Discovery Methods

Cited by 794

Leveraging large language models for predictive chemistry

Jablonka, K. M., Schwaller, P., Ortega-Guerrero, A., & Smit, B. (2024) · Nature Machine Intelligence

Machine Learning in Materials ScienceComputational Drug Discovery Methods

Cited by 378

Prediction of chemical reaction yields using deep learning

Schwaller, P., Vaucher, A. C., Laino, T., & Reymond, J.-L. (2021) · Machine Learning: Science and Technology

Machine Learning in Materials ScienceComputational Drug Discovery Methods

Cited by 291

14 examples of how LLMs can transform materials science and chemistry: A reflection on a large language model hackathon

Jablonka, K. M., Ai, Q., Al-Feghali, A., Badhwar, S., Bocarsly, J. D., Bran, A. M., Bringuier, S., Brinson, L. C., Choudhary, K., Circi, D., et al. (2023) · Digital Discovery

Machine Learning in Materials ScienceArtificial Intelligence Applications

Cited by 218

Humanity's Last Exam

Phan, L., Gatti, A., Han, Z., Li, N., Hu, J., Zhang, H., Zhang, C. B. C., Shaaban, M., Ling, J., Shi, S., et al. (2025) · arXiv

Topic ModelingMultimodal Machine Learning Applications

Cited by 65