5 citations · 7 across the 5 of their papers we have counts for
5 papers
Beyond Text and Tables: Vision-Language Model Integration in ComProScanner for Extracting Materials Data from Scientific Figures with High Accuracy
Aritra Roy, Enrico Grisan, Chiara Gattinoni +1
Automated extraction of materials composition-property data from scientific literature has advanced considerably with the development of large language model-based pipelines; howev…
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry
Aritra Roy, Kevin Shen, Andrew MacBride +350
Large language models (LLMs) are rapidly changing how researchers in materials science and chemistry discover, organize, and act on scientific knowledge. This paper analyzes a broa…
ComProScanner: A multi-agent based framework for composition-property structured data extraction from scientific literature
Aritra Roy, Enrico Grisan, John Buckeridge +1
Since the advent of various pre-trained large language models, extracting structured knowledge from scientific text has experienced a revolutionary change compared with traditional…
34 Examples of LLM Applications in Materials Science and Chemistry: Towards Automation, Assistants, Agents, and Accelerated Scientific Discovery
Yoel Zimmermann, Adib Bazgir, Alexander Al-Feghali +32
Large Language Models (LLMs) are reshaping many aspects of materials science and chemistry research, enabling advances in molecular property prediction, materials design, scientifi…
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…