Modelling chemical processes in explicit solvents with machine learning potentials
Solvent effects influence all stages of the chemical processes, modulating the stability of intermediates and transition states, as well as altering reaction rates and product ratios. However, accurately modelling these effects remains challenging. Here, we present a general strategy for generating...
| Main Authors: | , , |
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| Format: | Internet publication |
| Language: | English |
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2023
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| _version_ | 1797113233865179136 |
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| author | Zhang, H Juraskova, V Duarte, F |
| author_facet | Zhang, H Juraskova, V Duarte, F |
| author_sort | Zhang, H |
| collection | OXFORD |
| description | Solvent effects influence all stages of the chemical processes, modulating the stability of intermediates and transition states, as well as altering reaction rates and product ratios. However, accurately modelling these effects remains challenging. Here, we present a general strategy for generating reactive machine learning potentials (MLPs) to model chemical processes in solution. Our approach combines active learning with descriptor-based selectors and automation, enabling the construction of data-efficient training sets that span the relevant chemical and conformational space. We demonstrate the versatility of this strategy by applying it to investigate a Diels-Alder reaction in water and methanol. The generated MLPs exhibit excellent agreement with experimental data and provide insights into the differences in reaction rates observed between the two solvents. Our strategy offers an efficient approach to the routine modelling of chemical reactions in solution, opening up avenues for studying complex chemical processes in an efficient manner. |
| first_indexed | 2024-04-23T08:25:34Z |
| format | Internet publication |
| id | oxford-uuid:62374c57-914a-427a-9f35-dddd56cedc24 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-04-23T08:25:34Z |
| publishDate | 2023 |
| record_format | dspace |
| spelling | oxford-uuid:62374c57-914a-427a-9f35-dddd56cedc242024-04-12T10:24:19ZModelling chemical processes in explicit solvents with machine learning potentialsInternet publicationhttp://purl.org/coar/resource_type/c_7ad9uuid:62374c57-914a-427a-9f35-dddd56cedc24EnglishSymplectic Elements2023Zhang, HJuraskova, VDuarte, FSolvent effects influence all stages of the chemical processes, modulating the stability of intermediates and transition states, as well as altering reaction rates and product ratios. However, accurately modelling these effects remains challenging. Here, we present a general strategy for generating reactive machine learning potentials (MLPs) to model chemical processes in solution. Our approach combines active learning with descriptor-based selectors and automation, enabling the construction of data-efficient training sets that span the relevant chemical and conformational space. We demonstrate the versatility of this strategy by applying it to investigate a Diels-Alder reaction in water and methanol. The generated MLPs exhibit excellent agreement with experimental data and provide insights into the differences in reaction rates observed between the two solvents. Our strategy offers an efficient approach to the routine modelling of chemical reactions in solution, opening up avenues for studying complex chemical processes in an efficient manner. |
| spellingShingle | Zhang, H Juraskova, V Duarte, F Modelling chemical processes in explicit solvents with machine learning potentials |
| title | Modelling chemical processes in explicit solvents with machine learning potentials |
| title_full | Modelling chemical processes in explicit solvents with machine learning potentials |
| title_fullStr | Modelling chemical processes in explicit solvents with machine learning potentials |
| title_full_unstemmed | Modelling chemical processes in explicit solvents with machine learning potentials |
| title_short | Modelling chemical processes in explicit solvents with machine learning potentials |
| title_sort | modelling chemical processes in explicit solvents with machine learning potentials |
| work_keys_str_mv | AT zhangh modellingchemicalprocessesinexplicitsolventswithmachinelearningpotentials AT juraskovav modellingchemicalprocessesinexplicitsolventswithmachinelearningpotentials AT duartef modellingchemicalprocessesinexplicitsolventswithmachinelearningpotentials |