activity
20202024
most cited14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathon

214 citations · 246 across the 5 of their papers we have counts for

collaborators

5 papers

cs.IR2024

Combining Language and Graph Models for Semi-structured Information Extraction on the Web

Zhi Hong, Kyle Chard, Ian Foster

Relation extraction is an efficient way of mining the extraordinary wealth of human knowledge on the Web. Existing methods rely on domain-specific training data or produce noisy ou…

cond-mat.mtrl-sci2023★ 214 cited

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…

cs.CL2022★ 11 cited

The Diminishing Returns of Masked Language Models to Science

Zhi Hong, Aswathy Ajith, Gregory Pauloski +3

Transformer-based masked language models such as BERT, trained on general corpora, have shown impressive performance on downstream tasks. It has also been demonstrated that the dow…

cs.CL2021★ 1 cited

AI- and HPC-enabled Lead Generation for SARS-CoV-2: Models and Processes to Extract Druglike Molecules Contained in Natural Language Text

Zhi Hong, J. Gregory Pauloski, Logan Ward +3

Researchers worldwide are seeking to repurpose existing drugs or discover new drugs to counter the disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). A…

q-bio.BM2020★ 20 cited

Targeting SARS-CoV-2 with AI- and HPC-enabled Lead Generation: A First Data Release

Yadu Babuji, Ben Blaiszik, Tom Brettin +15

Researchers across the globe are seeking to rapidly repurpose existing drugs or discover new drugs to counter the the novel coronavirus disease (COVID-19) caused by severe acute re…