Downloads provided by UsageCounts14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon
Copyright policy )NIH| Informatics and Machine Learning Modules for Research Planning, Scheduling, Simulation, and Optimization in the ASPIRE Autonomous Laboratory ,
EC| EPFLglobaLeaders ,
NSF| Molecular Design and Analysis of Flow Battery Electrolytes based on Redox Deep Eutectic Solvents ,
NSF| Collaborative Research: Framework: Machine Learning Materials Innovation Infrastructure ,
EC| Reincarnate ,
EC| BEWARE2 ,
NSF| NRT-HDR: Harnessing AI for Understanding & Designing Materials (aiM) ,
NSF| Collaborative Research: Disciplinary Improvements: Creating a FAIROS Materials Research Coordination Network (MaRCN) in the Materials Research Data Alliance ,
SNSF| NCCR MARVEL: Materials’ Revolution: Computational Design and Discovery of Novel Materials (phase II) ,
NSF| CDS&E: D3SC: Applying Video Segmentation to Coarse-grain Mapping Operators in Molecular Simulations ,
NSF| Frameworks: Garden: A FAIR Framework for Publishing and Applying AI Models for Translational Research in Science, Engineering, Education, and Industry ,
WT| unidentified ,
NIH| Learning to learn in structural biology with deep neural networks ,
EC| BIG-MAP
Imran, Alishba; Schwarting, Marcus; Jablonka, Kevin Maik; Ai, Qianxiang; Al-Feghali, Alexander; Badhwar, Shruti; Bocarsly, Joshua D.; +49 Authors
Imran, Alishba; Schwarting, Marcus; Jablonka, Kevin Maik; Ai, Qianxiang; Al-Feghali, Alexander; Badhwar, Shruti; Bocarsly, Joshua D.; Bran, Andres M.; Bringuier, Stefan; Brinson, L. Catherine; Choudhary, Kamal; Circi, Defne; Cox, Sam; de Jong, Wibe A.; Evans, Matthew L.; Gastellu, Nicolas; Genzling, Jerome; Gil, María Victoria; resid: A-2774-2017; Gupta, Ankur K.; Hong, Zhi; Kruschwitz, Sabine; Labarre, Anne; Lála, Jakub; Liu, Tao; Ma, Steven; Majumdar, Sauradeep; Merz, Garrett W.; Moitessier, Nicolas; Moubarak, Elias; Mouriño, Beatriz; Pelkie, Brenden; Pieler, Michael; Ramos, Mayk Caldas; Ranković, Bojana; Rodriques, Samuel G.; Sanders, Jacob N.; Schwaller, Philippe; Shi, Jiale; Smit, Berend; resid: B-7580-2009; Smith, Ben E.; Van Herck, Joren; Völker, Christoph; Ward, Logan; resid: I-9526-2019; Warren, Sean; Weiser, Benjamin; Zhang, Sylvester; Zhang, Xiaoqi; Zia, Ghezal Ahmad; Scourtas, Aristana; Schmidt, K. J.; Foster, Ian; White, Andrew D.; Blaiszik, Ben;
14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon
We report the findings of a hackathon focused on exploring the diverse applications of large language models in molecular and materials science.
Spain, United States, United Kingdom
- Spanish National Research Council Spain
- UNIVERSITY OF ROCHESTER
- University of Chicago United States
- EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH Switzerland
- Washington State University United States
FOS: Computer and information sciences, Computer Science - Machine Learning, Ensure access to affordable, reliable, sustainable and modern energy for all, FOS: Physical sciences, Gene Expression, http://metadata.un.org/sdg/9, Macromolecular and Materials Chemistry, http://metadata.un.org/sdg/7, Machine Learning (cs.LG), Imaging, Physics - Chemical Physics, Chemical Physics (physics.chem-ph), 3403 Macromolecular and Materials Chemistry, Condensed Matter - Materials Science, 34 Chemical Sciences, FOS: Clinical medicine, Neurosciences, Materials Science (cond-mat.mtrl-sci), Materials Science; Machine Learning; Chemical Physics, 004, Chemistry, Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation, Chemical Sciences, Synthetic Biology, Microfabrication & Bioengineering, Genetics & Genomics
FOS: Computer and information sciences, Computer Science - Machine Learning, Ensure access to affordable, reliable, sustainable and modern energy for all, FOS: Physical sciences, Gene Expression, http://metadata.un.org/sdg/9, Macromolecular and Materials Chemistry, http://metadata.un.org/sdg/7, Machine Learning (cs.LG), Imaging, Physics - Chemical Physics, Chemical Physics (physics.chem-ph), 3403 Macromolecular and Materials Chemistry, Condensed Matter - Materials Science, 34 Chemical Sciences, FOS: Clinical medicine, Neurosciences, Materials Science (cond-mat.mtrl-sci), Materials Science; Machine Learning; Chemical Physics, 004, Chemistry, Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation, Chemical Sciences, Synthetic Biology, Microfabrication & Bioengineering, Genetics & Genomics
selected citations These citations are derived from selected sources.This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).72 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 1% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 1% views 63 downloads 160 - 63views160downloads
selected citations
Citations provided by BIP!
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
Citations provided by BIP!popularityPopularity provided by BIP!
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
Popularity provided by BIP!influenceInfluence provided by BIP!
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
Influence provided by BIP!impulseImpulse provided by BIP!
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
Impulse provided by BIP!viewsViews provided by UsageCounts
Views provided by UsageCountsdownloadsDownloads provided by UsageCounts
Downloads provided by UsageCounts 72
Top 1%
Top 10%
Top 1%
63
160
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gold
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NIH| Informatics and Machine Learning Modules for Research Planning, Scheduling, Simulation, and Optimization in the ASPIRE Autonomous Laboratory, EC| EPFLglobaLeaders, NSF| Molecular Design and Analysis of Flow Battery Electrolytes based on Redox Deep Eutectic Solvents, NSF| Collaborative Research: Framework: Machine Learning Materials Innovation Infrastructure
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