292 citations · 559 across the 18 of their papers we have counts for
6 papers · 1 filter
An autonomous living database for perovskite photovoltaics
Sherjeel Shabih, Hampus Näsström, Sharat Patil +16
Scientific discovery is severely bottlenecked by the inability of manual curation to keep pace with exponential publication rates. This creates a widening knowledge gap. This is es…
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…
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…
Less can be more for predicting properties with large language models
Nawaf Alampara, Santiago Miret, Kevin Maik Jablonka
Predicting properties from coordinate-category data -- sets of vectors paired with categorical information -- is fundamental to computational science. In materials science, this ch…
14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathon
Kevin Maik Jablonka, Qianxiang Ai, Alexander Al-Feghali +50
Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To…
Big-Data Science in Porous Materials: Materials Genomics and Machine Learning
Kevin Maik Jablonka, Daniele Ongari, Seyed Mohamad Moosavi +1
By combining metal nodes with organic linkers we can potentially synthesize millions of possible metal organic frameworks (MOFs). At present, we have libraries of over ten thousand…