Deep Learning of Activation Energies
Quantitative predictions of reaction properties, such as activation energy, have been limited due to a lack of available training data. Such predictions would be useful for computer-assisted reaction mechanism generation and organic synthesis planning. We develop a template-free deep learning model...
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| Format: | Article |
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American Chemical Society (ACS)
2020
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| Online Access: | https://hdl.handle.net/1721.1/125019 |
| _version_ | 1826196659532464128 |
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| author | Grambow, Colin A. Pattanaik, Lagnajit Green, William H. |
| author2 | Massachusetts Institute of Technology. Department of Chemical Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Chemical Engineering Grambow, Colin A. Pattanaik, Lagnajit Green, William H. |
| author_sort | Grambow, Colin A. |
| collection | MIT |
| description | Quantitative predictions of reaction properties, such as activation energy, have been limited due to a lack of available training data. Such predictions would be useful for computer-assisted reaction mechanism generation and organic synthesis planning. We develop a template-free deep learning model to predict the activation energy given reactant and product graphs and train the model on a new, diverse data set of gas-phase quantum chemistry reactions. We demonstrate that our model achieves accurate predictions and agrees with an intuitive understanding of chemical reactivity. With the continued generation of quantitative chemical reaction data and the development of methods that leverage such data, we expect many more methods for reactivity prediction to become available in the near future. |
| first_indexed | 2024-09-23T10:33:02Z |
| format | Article |
| id | mit-1721.1/125019 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T10:33:02Z |
| publishDate | 2020 |
| publisher | American Chemical Society (ACS) |
| record_format | dspace |
| spelling | mit-1721.1/1250192022-09-30T21:39:52Z Deep Learning of Activation Energies Grambow, Colin A. Pattanaik, Lagnajit Green, William H. Massachusetts Institute of Technology. Department of Chemical Engineering Quantitative predictions of reaction properties, such as activation energy, have been limited due to a lack of available training data. Such predictions would be useful for computer-assisted reaction mechanism generation and organic synthesis planning. We develop a template-free deep learning model to predict the activation energy given reactant and product graphs and train the model on a new, diverse data set of gas-phase quantum chemistry reactions. We demonstrate that our model achieves accurate predictions and agrees with an intuitive understanding of chemical reactivity. With the continued generation of quantitative chemical reaction data and the development of methods that leverage such data, we expect many more methods for reactivity prediction to become available in the near future. DARPA (Contract ARO W911NF-16-2-0023) 2020-05-05T17:56:54Z 2020-05-05T17:56:54Z 2020-03 Article http://purl.org/eprint/type/JournalArticle 1948-7185 1948-7185 https://hdl.handle.net/1721.1/125019 Grambow, Colin A. et al. "Deep Learning of Activation Energies." Journal of Physical Chemistry Letters 11, 8 (March 2020): 2992-2997 © 2020 American Chemical Society http://dx.doi.org/10.1021/acs.jpclett.0c00500 Journal of Physical Chemistry Letters Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf American Chemical Society (ACS) Prof. William Green |
| spellingShingle | Grambow, Colin A. Pattanaik, Lagnajit Green, William H. Deep Learning of Activation Energies |
| title | Deep Learning of Activation Energies |
| title_full | Deep Learning of Activation Energies |
| title_fullStr | Deep Learning of Activation Energies |
| title_full_unstemmed | Deep Learning of Activation Energies |
| title_short | Deep Learning of Activation Energies |
| title_sort | deep learning of activation energies |
| url | https://hdl.handle.net/1721.1/125019 |
| work_keys_str_mv | AT grambowcolina deeplearningofactivationenergies AT pattanaiklagnajit deeplearningofactivationenergies AT greenwilliamh deeplearningofactivationenergies |