19 citations · 39 across the 6 of their papers we have counts for
6 papers
General-Purpose Models for the Chemical Sciences: LLMs and Beyond
Nawaf Alampara, Anagha Aneesh, Martiño Ríos-García +6
Data-driven techniques have a large potential to transform and accelerate the chemical sciences. However, chemical sciences also pose the unique challenge of very diverse, small, f…
Lessons from the trenches on evaluating machine-learning systems in materials science
Nawaf Alampara, Mara Schilling-Wilhelmi, Kevin Maik Jablonka
Measurements are fundamental to knowledge creation in science, enabling consistent sharing of findings and serving as the foundation for scientific discovery. As machine learning s…
Probing the limitations of multimodal language models for chemistry and materials research
Nawaf Alampara, Mara Schilling-Wilhelmi, Martiño Ríos-García +5
Recent advancements in artificial intelligence have sparked interest in scientific assistants that could support researchers across the full spectrum of scientific workflows, from…
Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry
Yoel Zimmermann, Adib Bazgir, Zartashia Afzal +141
Here, we present the outcomes from the second Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry, which engaged participants across global hyb…
From Text to Insight: Large Language Models for Materials Science Data Extraction
Mara Schilling-Wilhelmi, Martiño Ríos-García, Sherjeel Shabih +5
The vast majority of materials science knowledge exists in unstructured natural language, yet structured data is crucial for innovative and systematic materials design. Traditional…
Are large language models superhuman chemists?
Adrian Mirza, Nawaf Alampara, Sreekanth Kunchapu +32
Large language models (LLMs) have gained widespread interest due to their ability to process human language and perform tasks on which they have not been explicitly trained. Howeve…