| Are large language models superhuman chemists? A Mirza, N Alampara, S Kunchapu, M Ríos-García, B Emoekabu, ... arXiv preprint arXiv:2404.01475, 2024 | 24 | 2024 |
| From text to insight: large language models for materials science data extraction M Schilling-Wilhelmi, M Ríos-García, S Shabih, MV Gil, S Miret, CT Koch, ... arXiv preprint arXiv:2407.16867, 2024 | 10 | 2024 |
| Probing the limitations of multimodal language models for chemistry and materials research N Alampara, M Schilling-Wilhelmi, M Ríos-García, I Mandal, P Khetarpal, ... arXiv preprint arXiv:2411.16955, 2024 | 3 | 2024 |
| Tailoring Gene Transfer Efficacy through the Arrangement of Cationic and Anionic Blocks in Triblock Copolymer Micelles K Leer, LS Reichel, M Wilhelmi, JC Brendel, A Traeger ACS Macro Letters 13 (2), 158-165, 2024 | 1 | 2024 |
| From text to insight: large language models for chemical data extraction M Schilling-Wilhelmi, M Ríos-García, S Shabih, MV Gil, S Miret, CT Koch, ... Chemical Society Reviews, 2025 | | 2025 |
| Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Y Zimmermann, A Bazgir, Z Afzal, F Agbere, Q Ai, N Alampara, ... arXiv preprint arXiv:2411.15221, 2024 | | 2024 |
| Using machine-learning and large-language-model extracted data to predict copolymerizations M Schilling-Wilhelmi, KM Jablonka AI for Accelerated Materials Design-Vienna 2024, 2024 | | 2024 |
| MACBENCH: A multimodal chemistry and materials science benchmark N Alampara, I Mandal, P Khetarpal, HS Grover, M Schilling-Wilhelmi, ... | | |