Abstract

Chemical potential of species in solution is essential for understanding various chemical processes at interfaces. Molecular dynamics (MD) simulations, constrained by fixed compositions, cannot satisfy a constant chemical potential condition as solute species can migrate to the interface and deplete the bulk due to solute-interface interactions. In this study, we introduce a simple and computationally efficient approach named iterative constant chemical potential molecular dynamics (iCµMD) simulation, which helps simulate targeted molar concentrations of species in solution. iCµMD overcomes the limitations of conventional MD by adjusting the number of species in the solution to reach a target concentration (chemical potential). We demonstrate our approach using solid-liquid and liquid-air interfacial systems as case studies. Specifically, we perform classical force field-based MD simulations of NaClaq-air and NaClaq-graphite interfaces and machine learning interatomic potential (MLIP)-based MD simulations of the Na₂SO₄,aq-graphene interface. Our results show that the iCµMD approach efficiently achieves the desired bulk ion concentration within two iterations and can also be integrated with MLIP-driven simulations which enable constant potential simulations with DFT‐level accuracy. We show that iCµMD offers a robust and simple computational framework for constant chemical potential simulations as its only requirement is to be able to converge interfacial simulations with a measurable bulk region.

Abstract

Chemical potential of species in solution is essential for understanding various chemical processes at interfaces. Molecular dynamics (MD) simulations, constrained by fixed compositions, cannot satisfy a constant chemical potential condition as solute species can migrate to the interface and deplete the bulk due to solute-interface interactions. In this study, we introduce a simple and computationally efficient approach named iterative constant chemical potential molecular dynamics (iCµMD) simulation, which helps simulate targeted molar concentrations of species in solution. iCµMD overcomes the limitations of conventional MD by adjusting the number of species in the solution to reach a target concentration (chemical potential). We demonstrate our approach using solid-liquid and liquid-air interfacial systems as case studies. Specifically, we perform classical force field-based MD simulations of NaClaq-air and NaClaq-graphite interfaces and machine learning interatomic potential (MLIP)-based MD simulations of the Na₂SO₄,aq-graphene interface. Our results show that the iCµMD approach efficiently achieves the desired bulk ion concentration within two iterations and can also be integrated with MLIP-driven simulations which enable constant potential simulations with DFT‐level accuracy. We show that iCµMD offers a robust and simple computational framework for constant chemical potential simulations as its only requirement is to be able to converge interfacial simulations with a measurable bulk region.

Abstract

Chemical potential of species in solution is essential for understanding various chemical processes at interfaces. Molecular dynamics (MD) simulations, constrained by fixed compositions, cannot satisfy a constant chemical potential condition as solute species can migrate to the interface and deplete the bulk due to solute-interface interactions. In this study, we introduce a simple and computationally efficient approach named iterative constant chemical potential molecular dynamics (iCµMD) simulation, which helps simulate targeted molar concentrations of species in solution. iCµMD overcomes the limitations of conventional MD by adjusting the number of species in the solution to reach a target concentration (chemical potential). We demonstrate our approach using solid-liquid and liquid-air interfacial systems as case studies. Specifically, we perform classical force field-based MD simulations of NaClaq-air and NaClaq-graphite interfaces and machine learning interatomic potential (MLIP)-based MD simulations of the Na₂SO₄,aq-graphene interface. Our results show that the iCµMD approach efficiently achieves the desired bulk ion concentration within two iterations and can also be integrated with MLIP-driven simulations which enable constant potential simulations with DFT‐level accuracy. We show that iCµMD offers a robust and simple computational framework for constant chemical potential simulations as its only requirement is to be able to converge interfacial simulations with a measurable bulk region.

Abstract

Chemical potential of species in solution is essential for understanding various chemical processes at interfaces. Molecular dynamics (MD) simulations, constrained by fixed compositions, cannot satisfy a constant chemical potential condition as solute species can migrate to the interface and deplete the bulk due to solute-interface interactions. In this study, we introduce a simple and computationally efficient approach named iterative constant chemical potential molecular dynamics (iCµMD) simulation, which helps simulate targeted molar concentrations of species in solution. iCµMD overcomes the limitations of conventional MD by adjusting the number of species in the solution to reach a target concentration (chemical potential). We demonstrate our approach using solid-liquid and liquid-air interfacial systems as case studies. Specifically, we perform classical force field-based MD simulations of NaClaq-air and NaClaq-graphite interfaces and machine learning interatomic potential (MLIP)-based MD simulations of the Na₂SO₄,aq-graphene interface. Our results show that the iCµMD approach efficiently achieves the desired bulk ion concentration within two iterations and can also be integrated with MLIP-driven simulations which enable constant potential simulations with DFT‐level accuracy. We show that iCµMD offers a robust and simple computational framework for constant chemical potential simulations as its only requirement is to be able to converge interfacial simulations with a measurable bulk region.