A neural master equation framework for multiscale modeling of molecular processes: application to atomic-scale plasma processes
Abstract Plasma-surface interactions (PSI) play a crucial role in microelectronics fabrication; however, their multiscale nature and array of complex, often unknown interactions make computational modeling of PSIs extremely difficult. To this end, we propose a general neural master equation (NME) fr...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-025-01677-4 |
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| author | Shoubhanik Nath Joseph R. Vella David B. Graves Ali Mesbah |
| author_facet | Shoubhanik Nath Joseph R. Vella David B. Graves Ali Mesbah |
| author_sort | Shoubhanik Nath |
| collection | DOAJ |
| description | Abstract Plasma-surface interactions (PSI) play a crucial role in microelectronics fabrication; however, their multiscale nature and array of complex, often unknown interactions make computational modeling of PSIs extremely difficult. To this end, we propose a general neural master equation (NME) framework that uses master equations to describe the dynamics of a molecular process, wherein neural networks learned from atomistic simulations represent unknown transitions between different system states. By leveraging the physics-based structure of master equations and data-driven state transitions, the NME framework promotes generalizability and physics interpretability, and can bridge disparate length and time scales. The framework is demonstrated for multiscale modeling of Si atomic layer etching and reactive ion etching, where the learned NME-based surface kinetic models exhibit good predictive and extrapolative capabilities for predicting experimentally relevant observables as a function of process parameters. The NME-based surface kinetic models obey physical constraints, which are violated in models based on neural ordinary differential equations. The proposed NME framework for multiscale modeling of molecular processes can pave the way for the discovery of new chemistries and materials in atomic-scale plasma processes. |
| format | Article |
| id | doaj-art-72dedf2e0ef346fab873d62146f8d282 |
| institution | Kabale University |
| issn | 2057-3960 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Computational Materials |
| spelling | doaj-art-72dedf2e0ef346fab873d62146f8d2822025-08-20T03:46:23ZengNature Portfolionpj Computational Materials2057-39602025-07-0111111210.1038/s41524-025-01677-4A neural master equation framework for multiscale modeling of molecular processes: application to atomic-scale plasma processesShoubhanik Nath0Joseph R. Vella1David B. Graves2Ali Mesbah3Department of Chemical and Biomolecular Engineering, University of California at BerkeleyU.S. Department of Energy National Laboratory, Princeton Plasma Physics LaboratoryDepartment of Chemical and Biological Engineering, Princeton University, PrincetonDepartment of Chemical and Biomolecular Engineering, University of California at BerkeleyAbstract Plasma-surface interactions (PSI) play a crucial role in microelectronics fabrication; however, their multiscale nature and array of complex, often unknown interactions make computational modeling of PSIs extremely difficult. To this end, we propose a general neural master equation (NME) framework that uses master equations to describe the dynamics of a molecular process, wherein neural networks learned from atomistic simulations represent unknown transitions between different system states. By leveraging the physics-based structure of master equations and data-driven state transitions, the NME framework promotes generalizability and physics interpretability, and can bridge disparate length and time scales. The framework is demonstrated for multiscale modeling of Si atomic layer etching and reactive ion etching, where the learned NME-based surface kinetic models exhibit good predictive and extrapolative capabilities for predicting experimentally relevant observables as a function of process parameters. The NME-based surface kinetic models obey physical constraints, which are violated in models based on neural ordinary differential equations. The proposed NME framework for multiscale modeling of molecular processes can pave the way for the discovery of new chemistries and materials in atomic-scale plasma processes.https://doi.org/10.1038/s41524-025-01677-4 |
| spellingShingle | Shoubhanik Nath Joseph R. Vella David B. Graves Ali Mesbah A neural master equation framework for multiscale modeling of molecular processes: application to atomic-scale plasma processes npj Computational Materials |
| title | A neural master equation framework for multiscale modeling of molecular processes: application to atomic-scale plasma processes |
| title_full | A neural master equation framework for multiscale modeling of molecular processes: application to atomic-scale plasma processes |
| title_fullStr | A neural master equation framework for multiscale modeling of molecular processes: application to atomic-scale plasma processes |
| title_full_unstemmed | A neural master equation framework for multiscale modeling of molecular processes: application to atomic-scale plasma processes |
| title_short | A neural master equation framework for multiscale modeling of molecular processes: application to atomic-scale plasma processes |
| title_sort | neural master equation framework for multiscale modeling of molecular processes application to atomic scale plasma processes |
| url | https://doi.org/10.1038/s41524-025-01677-4 |
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