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20182026
most cited14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathon

214 citations · 686 across the 20 of their papers we have counts for

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cs.LG2025

Steering an Active Learning Workflow Towards Novel Materials Discovery via Queue Prioritization

Marcus Schwarting, Logan Ward, Nathaniel Hudson +5

Generative AI poses both opportunities and risks for solving inverse design problems in the sciences. Generative tools provide the ability to expand and refine a search space auton…

cs.LG20252 cited

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…

cs.LG20245 cited

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…

cs.LG20241 cited

Trillion Parameter AI Serving Infrastructure for Scientific Discovery: A Survey and Vision

Nathaniel Hudson, J. Gregory Pauloski, Matt Baughman +13

Deep learning methods are transforming research, enabling new techniques, and ultimately leading to new discoveries. As the demand for more capable AI models continues to grow, we…

cs.LG2020

HydroNet: Benchmark Tasks for Preserving Intermolecular Interactions and Structural Motifs in Predictive and Generative Models for Molecular Data

Sutanay Choudhury, Jenna A. Bilbrey, Logan Ward +5

Intermolecular and long-range interactions are central to phenomena as diverse as gene regulation, topological states of quantum materials, electrolyte transport in batteries, and…

cs.LG2018

DLHub: Model and Data Serving for Science

Ryan Chard, Zhuozhao Li, Kyle Chard +7

While the Machine Learning (ML) landscape is evolving rapidly, there has been a relative lag in the development of the "learning systems" needed to enable broad adoption. Furthermo…