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ChatGPT Research Group for Optimizing the Crystallinity of MOFs and COFs
[Image: see text] We leveraged the power of ChatGPT and Bayesian optimization in the development of a multi-AI-driven system, backed by seven large language model-based assistants and equipped with machine learning algorithms, that seamlessly orchestrates a multitude of research aspects in a chemist...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10683477/ https://www.ncbi.nlm.nih.gov/pubmed/38033801 http://dx.doi.org/10.1021/acscentsci.3c01087 |
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author | Zheng, Zhiling Zhang, Oufan Nguyen, Ha L. Rampal, Nakul Alawadhi, Ali H. Rong, Zichao Head-Gordon, Teresa Borgs, Christian Chayes, Jennifer T. Yaghi, Omar M. |
author_facet | Zheng, Zhiling Zhang, Oufan Nguyen, Ha L. Rampal, Nakul Alawadhi, Ali H. Rong, Zichao Head-Gordon, Teresa Borgs, Christian Chayes, Jennifer T. Yaghi, Omar M. |
author_sort | Zheng, Zhiling |
collection | PubMed |
description | [Image: see text] We leveraged the power of ChatGPT and Bayesian optimization in the development of a multi-AI-driven system, backed by seven large language model-based assistants and equipped with machine learning algorithms, that seamlessly orchestrates a multitude of research aspects in a chemistry laboratory (termed the ChatGPT Research Group). Our approach accelerated the discovery of optimal microwave synthesis conditions, enhancing the crystallinity of MOF-321, MOF-322, and COF-323 and achieving the desired porosity and water capacity. In this system, human researchers gained assistance from these diverse AI collaborators, each with a unique role within the laboratory environment, spanning strategy planning, literature search, coding, robotic operation, labware design, safety inspection, and data analysis. Such a comprehensive approach enables a single researcher working in concert with AI to achieve productivity levels analogous to those of an entire traditional scientific team. Furthermore, by reducing human biases in screening experimental conditions and deftly balancing the exploration and exploitation of synthesis parameters, our Bayesian search approach precisely zeroed in on optimal synthesis conditions from a pool of 6 million within a significantly shortened time scale. This work serves as a compelling proof of concept for an AI-driven revolution in the chemistry laboratory, painting a future where AI becomes an efficient collaborator, liberating us from routine tasks to focus on pushing the boundaries of innovation. |
format | Online Article Text |
id | pubmed-10683477 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | American Chemical Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-106834772023-11-30 ChatGPT Research Group for Optimizing the Crystallinity of MOFs and COFs Zheng, Zhiling Zhang, Oufan Nguyen, Ha L. Rampal, Nakul Alawadhi, Ali H. Rong, Zichao Head-Gordon, Teresa Borgs, Christian Chayes, Jennifer T. Yaghi, Omar M. ACS Cent Sci [Image: see text] We leveraged the power of ChatGPT and Bayesian optimization in the development of a multi-AI-driven system, backed by seven large language model-based assistants and equipped with machine learning algorithms, that seamlessly orchestrates a multitude of research aspects in a chemistry laboratory (termed the ChatGPT Research Group). Our approach accelerated the discovery of optimal microwave synthesis conditions, enhancing the crystallinity of MOF-321, MOF-322, and COF-323 and achieving the desired porosity and water capacity. In this system, human researchers gained assistance from these diverse AI collaborators, each with a unique role within the laboratory environment, spanning strategy planning, literature search, coding, robotic operation, labware design, safety inspection, and data analysis. Such a comprehensive approach enables a single researcher working in concert with AI to achieve productivity levels analogous to those of an entire traditional scientific team. Furthermore, by reducing human biases in screening experimental conditions and deftly balancing the exploration and exploitation of synthesis parameters, our Bayesian search approach precisely zeroed in on optimal synthesis conditions from a pool of 6 million within a significantly shortened time scale. This work serves as a compelling proof of concept for an AI-driven revolution in the chemistry laboratory, painting a future where AI becomes an efficient collaborator, liberating us from routine tasks to focus on pushing the boundaries of innovation. American Chemical Society 2023-11-10 /pmc/articles/PMC10683477/ /pubmed/38033801 http://dx.doi.org/10.1021/acscentsci.3c01087 Text en © 2023 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by/4.0/Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Zheng, Zhiling Zhang, Oufan Nguyen, Ha L. Rampal, Nakul Alawadhi, Ali H. Rong, Zichao Head-Gordon, Teresa Borgs, Christian Chayes, Jennifer T. Yaghi, Omar M. ChatGPT Research Group for Optimizing the Crystallinity of MOFs and COFs |
title | ChatGPT Research
Group for Optimizing the Crystallinity
of MOFs and COFs |
title_full | ChatGPT Research
Group for Optimizing the Crystallinity
of MOFs and COFs |
title_fullStr | ChatGPT Research
Group for Optimizing the Crystallinity
of MOFs and COFs |
title_full_unstemmed | ChatGPT Research
Group for Optimizing the Crystallinity
of MOFs and COFs |
title_short | ChatGPT Research
Group for Optimizing the Crystallinity
of MOFs and COFs |
title_sort | chatgpt research
group for optimizing the crystallinity
of mofs and cofs |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10683477/ https://www.ncbi.nlm.nih.gov/pubmed/38033801 http://dx.doi.org/10.1021/acscentsci.3c01087 |
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