| A framework for evaluating the chemical knowledge and reasoning abilities of large language models against the expertise of chemists A Mirza, N Alampara, S Kunchapu, M Ríos-García, B Emoekabu, ... Nature Chemistry, 1-8, 2025 | 135* | 2025 |
| 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 | 125 | 2025 |
| Probing the limitations of multimodal language models for chemistry and materials research N Alampara, M Schilling-Wilhelmi, M Ríos-García, I Mandal, P Khetarpal, ... Nature computational science 5 (10), 952-961, 2025 | 41* | 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 | 12 | 2024 |
| Lessons from the trenches on evaluating machine-learning systems in materials science N Alampara, M Schilling-Wilhelmi, KM Jablonka arXiv preprint arXiv:2503.10837, 2025 | 6 | 2025 |
| General-Purpose Models for the Chemical Sciences: LLMs and Beyond N Alampara, A Aneesh, M Ríos-García, A Mirza, M Schilling-Wilhelmi, ... arXiv preprint arXiv:2507.07456, 2025 | 4* | 2025 |
| 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 | 4 | 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 | 3 | 2024 |
| Lifting the benchmark iceberg with item-response theory M Schilling-Wilhelmi, N Alampara, KM Jablonka AI for Accelerated Materials Design-ICLR 2025, 0 | 1 | |
