Towards predictive design of electrolyte solutions by accelerating <i>ab initio</i> simulation with neural networks

Zhang, Junji; Pagotto, Joshua; Duignan, Timothy T.

doi:10.1039/d2ta02610d

Cited by 8 publications

(7 citation statements)

References 125 publications

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“…Similarly, longrange interactions are found to be unimportant for monovalent ion pairing. 38,143 Then, LMF theory and the SS model suggest that neural network potentials should be accurate for modeling solutions of monovalent ions, in agreement with recent work, 55,152 especially when complemented with theoretical methods that account for the effects of long-range interactions. 153 In contrast, LMF theory suggests that shortrange neural network models will have difficulty describing solutions containing multivalent ions, as well as interfaces, because they neglect important unbalanced forces arising from long-range interactions.…”

Section: Discussionsupporting

confidence: 79%

“…A study of such generally applicable LMF corrections seems particularly relevant at this time because we are currently experiencing a revolution in molecular model development, especially when it comes to modeling water and aqueous solutions. Quantum mechanics-based simulations are becoming increasingly accurate and practical, resulting in quantitative, nonempirical predictions from first principles. − The permeation of data science into theoretical chemistry has led to data-driven approaches to water model development and models with high predictive accuracy. ,− Similarly, advances in machine learning have led to an abundance of neural network potentials for modeling properties of water and aqueous solutions with near quantum mechanical accuracy and significantly lower cost. − Almost all of these new approaches generate better descriptions of the strong short-range forces than traditional empirical force fields.…”

Section: Introductionmentioning

confidence: 99%

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Local Molecular Field Theory for Coulomb Interactions in Aqueous Solutions

2023

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Coulomb interactions play a crucial role in a wide array of processes in aqueous solutions but present conceptual and computational challenges to both theory and simulations. We review recent developments in an approach addressing these challengeslocal molecular field (LMF) theory. LMF theory exploits an exact and physically suggestive separation of intermolecular Coulomb interactions into strong short-range and uniformly slowly varying long-range components. This allows us to accurately determine the averaged effects of the long-range components on the short-range structure using effective single particle fields and analytical corrections, greatly reducing the need for complex lattice summation techniques used in most standard approaches. The simplest use of these ideas in aqueous solutions leads to the short solvent (SS) model, where both solvent–solvent and solute–solvent Coulomb interactions have only short-range components. Here we use the SS model to give a simple description of pairing of nucleobases and biologically relevant ions in water.

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Section: Discussionsupporting

confidence: 79%

Section: Introductionmentioning

confidence: 99%

Local Molecular Field Theory for Coulomb Interactions in Aqueous Solutions

2023

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“…In the following paragraphs, we focus on a specific, recently developed approach for developing ab-initio -based machine-learning models called “Deep Potential Molecular Dynamics” (DeePMD) . Even though there are many other machine-learning methods that rely on similar concepts, − the DeePMD approach has been quite successful by simplifying and automating the process of potential energy generation . Efficiency improvements based on model compression now allow simulations of tens of thousands of atoms on GPU-based clusters at the rate of multiple ns of real time per wall-clock day.…”

Section: Discussionmentioning

confidence: 99%

Dynamics of Aqueous Electrolyte Solutions: Challenges for Simulations

Panagiotopoulos

Yue

2023

J. Phys. Chem. B

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This Perspective article focuses on recent simulation work on the dynamics of aqueous electrolytes. It is well-established that full-charge, nonpolarizable models for water and ions generally predict solution dynamics that are too slow in comparison to experiments. Models with reduced (scaled) charges do better for solution diffusivities and viscosities but encounter issues describing other dynamic phenomena such as nucleation rates of crystals from solution. Polarizable models show promise, especially when appropriately parametrized, but may still miss important physical effects such as charge transfer. First-principles calculations are starting to emerge for these properties that are in principle able to capture polarization, charge transfer, and chemical transformations in solution. While direct ab initio simulations are still too slow for simulations of large systems over long time scales, machine-learning models trained on appropriate first-principles data show significant promise for accurate and transferable modeling of electrolyte solution dynamics.

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“…However, most considered pure water solvent or implicitly treated the ions, even though they play a vital role in biological processes. For example, the ionic atmosphere has a crucial effect on the secondary and tertiary structure’s stability, the binding of charged drugs and proteins, and nucleic acid folding. − On the other hand, previous ML potentials that captured the explicit interaction of ions also explicitly treated the solvent, i.e., they were fully all-atom models. − …”

Section: Introductionmentioning

confidence: 99%

Developing an Implicit Solvation Machine Learning Model for Molecular Simulations of Ionic Media

Coste,

Slejko,

Zavadlav

et al. 2023

J. Chem. Theory Comput.

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Molecular dynamics (MD) simulations of biophysical systems require accurate modeling of their native environment, i.e., aqueous ionic solution, as it critically impacts the structure and function of biomolecules. On the other hand, the models should be computationally efficient to enable simulations of large spatiotemporal scales. Here, we present the deep implicit solvation model for sodium chloride solutions that satisfies both requirements. Owing to the use of the neural network potential, the model can capture the many-body potential of mean force, while the implicit water treatment renders the model inexpensive. We demonstrate our approach first for pure ionic solutions with concentrations ranging from physiological to 2 M. We then extend the model to capture the effective ion interactions in the vicinity and far away from a DNA molecule. In both cases, the structural properties are in good agreement with all-atom MD, showcasing a general methodology for the efficient and accurate modeling of ionic media.

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Towards predictive design of electrolyte solutions by accelerating ab initio simulation with neural networks

Abstract: Electrolyte solutions play a vital role in a vast range of important materials chemistry applications. For example, they are a crucial component in batteries, fuel cells, supercapacitors, electrolysis and carbon...

Cited by 8 publications

References 125 publications

Local Molecular Field Theory for Coulomb Interactions in Aqueous Solutions

Local Molecular Field Theory for Coulomb Interactions in Aqueous Solutions

Dynamics of Aqueous Electrolyte Solutions: Challenges for Simulations

Developing an Implicit Solvation Machine Learning Model for Molecular Simulations of Ionic Media

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