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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 · 1.4k across the 34 of their papers we have counts for

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Showing 2023Show all

6 papers · 1 filter

cond-mat.mtrl-sci2023★ 13 cited

Accelerating Electronic Stopping Power Predictions by 10 Million Times with a Combination of Time-Dependent Density Functional Theory and Machine Learning

Logan Ward, Ben Blaiszik, Cheng-Wei Lee +3

Knowing the rate at which particle radiation releases energy in a material, the stopping power, is key to designing nuclear reactors, medical treatments, semiconductor and quantum…

cs.RO2023★ 4 cited

Towards a Modular Architecture for Science Factories

Rafael Vescovi, Tobias Ginsburg, Kyle Hippe +14

Advances in robotic automation, high-performance computing (HPC), and artificial intelligence (AI) encourage us to conceive of science factories: large, general-purpose computation…

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…

cond-mat.mtrl-sci2023★ 59 cited

JARVIS-Leaderboard: A Large Scale Benchmark of Materials Design Methods

Kamal Choudhary, Daniel Wines, Kangming Li +35

Lack of rigorous reproducibility and validation are major hurdles for scientific development across many fields. Materials science in particular encompasses a variety of experiment…

cond-mat.mtrl-sci2023★ 1 cited

What is missing in autonomous discovery: Open challenges for the community

Phillip M. Maffettone, Pascal Friederich, Sterling G. Baird +16

Self-driving labs (SDLs) leverage combinations of artificial intelligence, automation, and advanced computing to accelerate scientific discovery. The promise of this field has give…

cond-mat.mtrl-sci2023★ 109 cited

Deep Learning for Automated Experimentation in Scanning Transmission Electron Microscopy

Sergei V. Kalinin, Debangshu Mukherjee, Kevin M. Roccapriore +9

Machine learning (ML) has become critical for post-acquisition data analysis in (scanning) transmission electron microscopy, (S)TEM, imaging and spectroscopy. An emerging trend is…