Accelerating charge estimation in molecular dynamics simulations using physics-informed neural networks: corrosion applications
Abstract Molecular Dynamics (MD) simulations are used to understand the effects of corrosion on metallic materials in salt brine. Reactive force fields in classical MD enable accurate modeling of bond formation and breakage in the aqueous medium and at the metal-electrolyte interface, while also fac...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-024-01495-0 |
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| author | Aditya Venkatraman Mark A. Wilson David Montes de Oca Zapiain |
| author_facet | Aditya Venkatraman Mark A. Wilson David Montes de Oca Zapiain |
| author_sort | Aditya Venkatraman |
| collection | DOAJ |
| description | Abstract Molecular Dynamics (MD) simulations are used to understand the effects of corrosion on metallic materials in salt brine. Reactive force fields in classical MD enable accurate modeling of bond formation and breakage in the aqueous medium and at the metal-electrolyte interface, while also facilitating dynamic partial charge equilibration. However, MD simulations are computationally intensive and unsuitable for modeling the long time scales characteristic of corrosive phenomena. To address this, we develop reduced-order machine learning models that provide accurate and efficient predictions of charge density in corrosive environments. Specifically, we use Long Short-Term Memory (LSTM) networks to forecast charge density evolution based on atomic environments represented by Smooth Overlap of Atomic Positions (SOAP) descriptors. A physics-informed loss function enforces charge neutrality and electronegativity equivalence. The atomic charges predicted by the deep learning model trained on this work were obtained two orders of magnitude faster than those from molecular dynamics (MD) simulations, with an error of less than 3% compared to the MD-obtained charges, even in extrapolative scenarios, while adhering to physical constraints. This demonstrates the excellent accuracy, computational efficiency, and validity of the developed model. Lastly, even though developed for corrosion, these protocols are formulated in a phenomenon-agnostic manner, allowing application to various variable-charge interatomic potentials and related fields. |
| format | Article |
| id | doaj-art-d93125d2a28b402ea0109a09d521c883 |
| institution | Kabale University |
| issn | 2057-3960 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Computational Materials |
| spelling | doaj-art-d93125d2a28b402ea0109a09d521c8832025-02-09T12:46:40ZengNature Portfolionpj Computational Materials2057-39602025-02-0111111410.1038/s41524-024-01495-0Accelerating charge estimation in molecular dynamics simulations using physics-informed neural networks: corrosion applicationsAditya Venkatraman0Mark A. Wilson1David Montes de Oca Zapiain2Sandia National LaboratoriesSandia National LaboratoriesSandia National LaboratoriesAbstract Molecular Dynamics (MD) simulations are used to understand the effects of corrosion on metallic materials in salt brine. Reactive force fields in classical MD enable accurate modeling of bond formation and breakage in the aqueous medium and at the metal-electrolyte interface, while also facilitating dynamic partial charge equilibration. However, MD simulations are computationally intensive and unsuitable for modeling the long time scales characteristic of corrosive phenomena. To address this, we develop reduced-order machine learning models that provide accurate and efficient predictions of charge density in corrosive environments. Specifically, we use Long Short-Term Memory (LSTM) networks to forecast charge density evolution based on atomic environments represented by Smooth Overlap of Atomic Positions (SOAP) descriptors. A physics-informed loss function enforces charge neutrality and electronegativity equivalence. The atomic charges predicted by the deep learning model trained on this work were obtained two orders of magnitude faster than those from molecular dynamics (MD) simulations, with an error of less than 3% compared to the MD-obtained charges, even in extrapolative scenarios, while adhering to physical constraints. This demonstrates the excellent accuracy, computational efficiency, and validity of the developed model. Lastly, even though developed for corrosion, these protocols are formulated in a phenomenon-agnostic manner, allowing application to various variable-charge interatomic potentials and related fields.https://doi.org/10.1038/s41524-024-01495-0 |
| spellingShingle | Aditya Venkatraman Mark A. Wilson David Montes de Oca Zapiain Accelerating charge estimation in molecular dynamics simulations using physics-informed neural networks: corrosion applications npj Computational Materials |
| title | Accelerating charge estimation in molecular dynamics simulations using physics-informed neural networks: corrosion applications |
| title_full | Accelerating charge estimation in molecular dynamics simulations using physics-informed neural networks: corrosion applications |
| title_fullStr | Accelerating charge estimation in molecular dynamics simulations using physics-informed neural networks: corrosion applications |
| title_full_unstemmed | Accelerating charge estimation in molecular dynamics simulations using physics-informed neural networks: corrosion applications |
| title_short | Accelerating charge estimation in molecular dynamics simulations using physics-informed neural networks: corrosion applications |
| title_sort | accelerating charge estimation in molecular dynamics simulations using physics informed neural networks corrosion applications |
| url | https://doi.org/10.1038/s41524-024-01495-0 |
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