Predicting Chemical Reaction Barriers with a Machine Learning Model
In the past few decades, tremendous advances have been made in the understanding of catalysis at solid surfaces. Despite this, most discoveries of materials for improved catalytic performance are made by a slow trial and error process in an experimental laboratory. Computational simulations have beg...
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| Veröffentlicht in: | Catalysis letters Jg. 149; H. 9; S. 2347 - 2354 |
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| Abstract | In the past few decades, tremendous advances have been made in the understanding of catalysis at solid surfaces. Despite this, most discoveries of materials for improved catalytic performance are made by a slow trial and error process in an experimental laboratory. Computational simulations have begun to provide a way to rationally design materials for optimizing catalytic performance, but due to the high computational expense of calculating transition state energies, simulations cannot adequately screen the phase space of materials. In this work, we attempt to mitigate this expense by using a machine learning approach to predict the most expensive and most important parameter in a catalyst’s affinity for a reaction: the reaction barrier. Previous methods which used the step reaction energy as the only parameter in a linear regression had a mean absolute error (MAE) on the order of 0.4 eV, too high to be used predictively. In our work, we achieve a MAE of about 0.22 eV, a marked improvement towards the goal of computational prediction of catalytic activity.
Graphical Abstract |
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| AbstractList | In the past few decades, tremendous advances have been made in the understanding of catalysis at solid surfaces. Despite this, most discoveries of materials for improved catalytic performance are made by a slow trial and error process in an experimental laboratory. Computational simulations have begun to provide a way to rationally design materials for optimizing catalytic performance, but due to the high computational expense of calculating transition state energies, simulations cannot adequately screen the phase space of materials. In this work, we attempt to mitigate this expense by using a machine learning approach to predict the most expensive and most important parameter in a catalyst’s affinity for a reaction: the reaction barrier. Previous methods which used the step reaction energy as the only parameter in a linear regression had a mean absolute error (MAE) on the order of 0.4 eV, too high to be used predictively. Here in our work, we achieve a MAE of about 0.22 eV, a marked improvement towards the goal of computational prediction of catalytic activity. In the past few decades, tremendous advances have been made in the understanding of catalysis at solid surfaces. Despite this, most discoveries of materials for improved catalytic performance are made by a slow trial and error process in an experimental laboratory. Computational simulations have begun to provide a way to rationally design materials for optimizing catalytic performance, but due to the high computational expense of calculating transition state energies, simulations cannot adequately screen the phase space of materials. In this work, we attempt to mitigate this expense by using a machine learning approach to predict the most expensive and most important parameter in a catalyst's affinity for a reaction: the reaction barrier. Previous methods which used the step reaction energy as the only parameter in a linear regression had a mean absolute error (MAE) on the order of 0.4 eV, too high to be used predictively. In our work, we achieve a MAE of about 0.22 eV, a marked improvement towards the goal of computational prediction of catalytic activity. In the past few decades, tremendous advances have been made in the understanding of catalysis at solid surfaces. Despite this, most discoveries of materials for improved catalytic performance are made by a slow trial and error process in an experimental laboratory. Computational simulations have begun to provide a way to rationally design materials for optimizing catalytic performance, but due to the high computational expense of calculating transition state energies, simulations cannot adequately screen the phase space of materials. In this work, we attempt to mitigate this expense by using a machine learning approach to predict the most expensive and most important parameter in a catalyst’s affinity for a reaction: the reaction barrier. Previous methods which used the step reaction energy as the only parameter in a linear regression had a mean absolute error (MAE) on the order of 0.4 eV, too high to be used predictively. In our work, we achieve a MAE of about 0.22 eV, a marked improvement towards the goal of computational prediction of catalytic activity. Graphical Abstract In the past few decades, tremendous advances have been made in the understanding of catalysis at solid surfaces. Despite this, most discoveries of materials for improved catalytic performance are made by a slow trial and error process in an experimental laboratory. Computational simulations have begun to provide a way to rationally design materials for optimizing catalytic performance, but due to the high computational expense of calculating transition state energies, simulations cannot adequately screen the phase space of materials. In this work, we attempt to mitigate this expense by using a machine learning approach to predict the most expensive and most important parameter in a catalyst's affinity for a reaction: the reaction barrier. Previous methods which used the step reaction energy as the only parameter in a linear regression had a mean absolute error (MAE) on the order of 0.4 eV, too high to be used predictively. In our work, we achieve a MAE of about 0.22 eV, a marked improvement towards the goal of computational prediction of catalytic activity. Graphical |
| Audience | Academic |
| Author | Gauthier, Joseph A. Singh, Aayush R. Rohr, Brian A. Nørskov, Jens K. |
| Author_xml | – sequence: 1 givenname: Aayush R. surname: Singh fullname: Singh, Aayush R. organization: Department of Chemical Engineering, SUNCAT Center for Interface Science and Catalysis, Stanford University, SLAC National Accelerator Laboratory, SUNCAT Center for Interface Science and Catalysis – sequence: 2 givenname: Brian A. surname: Rohr fullname: Rohr, Brian A. organization: Department of Chemical Engineering, SUNCAT Center for Interface Science and Catalysis, Stanford University, SLAC National Accelerator Laboratory, SUNCAT Center for Interface Science and Catalysis – sequence: 3 givenname: Joseph A. surname: Gauthier fullname: Gauthier, Joseph A. organization: Department of Chemical Engineering, SUNCAT Center for Interface Science and Catalysis, Stanford University, SLAC National Accelerator Laboratory, SUNCAT Center for Interface Science and Catalysis – sequence: 4 givenname: Jens K. surname: Nørskov fullname: Nørskov, Jens K. email: norskov@stanford.edu organization: Department of Chemical Engineering, SUNCAT Center for Interface Science and Catalysis, Stanford University, SLAC National Accelerator Laboratory, SUNCAT Center for Interface Science and Catalysis, Department of Physics, Denmark Technical University |
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| SubjectTerms | Catalysis Catalytic activity Chemical reactions Chemistry Chemistry and Materials Science Computer simulation Costs Design optimization Industrial Chemistry/Chemical Engineering INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY Machine learning Organometallic Chemistry Parameters Physical Chemistry Solid surfaces |
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| Title | Predicting Chemical Reaction Barriers with a Machine Learning Model |
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