Learning classical density functionals for ionic fluids

Abstract

Accurate and efficient theoretical techniques for describing ionic fluids are highly desirable for many applications across the physical, biological and materials sciences. With a rigorous statistical mechanical foundation, classical density functional theory (cDFT) is an appealing approach, but the competition between strong Coulombic interactions and steric repulsion limits the accuracy of current approximate functionals. Here, we extend a recently presented machine learning (ML) approach [Sammüller et al., Proc. Natl. Acad. Sci. USA, 120, e2312484120 (2023)] designed for systems with short-ranged interactions to ionic fluids. By adopting ideas from local molecular field theory, the framework we present amounts to using neural networks to learn the local relationship between the one-body direct correlation functions and inhomogeneous density profiles for a "mimic'' short-ranged system, with effects of long-ranged interactions accounted for in a mean-field, yet well-controlled, manner. By comparing to results from molecular simulations, we show that our approach accurately describes the structure and thermodynamics of prototypical models for electrolyte solutions and ionic liquids, including size-asymmetric and multivalent systems. The framework we present acts as an important step toward extending ML approaches for cDFT to systems with accurate interatomic potentials.

Main: 8 pages, 3 figures. SI: 17 pages, 9 figures

References in corpus (26)

• The Electrostatic Screening Length in Concentrated Electrolytes Increases with Concentration
• The electric double layer has a life of its own
• Density functional theory for hard-sphere mixtures: the White-Bear version Mark II
• Underscreening in concentrated electrolytes
• Machine learning and density functional theory
• Interfacial layering in the electric double layer of ionic liquids
• Primitive Model Electrolytes in the Near and Far Field: Decay Lengths from DFT and Simulations
• Interplay of local hydrogen-bonding and long-ranged dipolar forces in simulations of confined water
• The standard mean-field treatment of inter-particle attraction in classical DFT is better than one might expect
• Finite Field Methods for the Supercell Modelling of Charged Insulator-Electrolyte Interfaces
• Local molecular field theory for the treatment of electrostatics
• Neural functional theory for inhomogeneous fluids: Fundamentals and applications
• Attraction Between Like-Charged Walls: Short-Ranged Simulations Using Local Molecular Field Theory
• Machine-learning free-energy functionals using density profiles from simulations
• Analytical classical density functionals from an equation learning network
• Dielectric response with short-ranged electrostatics
• Finite field formalism for bulk electrolyte solutions
• The Differential Capacitance as a probe for the electric double layer structure and the electrolyte bulk composition
• Physics-informed Bayesian inference of external potentials in classical density-functional theory
• Learning Neural Free-Energy Functionals with Pair-Correlation Matching
• Neural density functionals: Local learning and pair-correlation matching
• Neural density functional theory of liquid-gas phase coexistence
• A classical density functional theory for solvation across length scales
• Macroscopic surface charges from microscopic simulations
• The Primitive Model in Classical Density Functional Theory: Beyond the Standard Mean-Field Approximation
• Grand-canonical molecular dynamics simulations powered by a hybrid 4D nonequilibrium MD/MC method: Implementation in LAMMPS and applications to electrolyte solutions

Cited by in corpus (10)

• Learning the bulk and interfacial physics of liquid-liquid phase separation with neural density functionals
• Neural Density Functional Theory in Higher Dimensions with Convolutional Layers
• Gauge invariance and hyperforce correlation theory for equilibrium fluid mixtures
• Magnetic Interactions between Nanoscale Domains in Correlated Liquids
• Dielectrocapillarity for exquisite control of fluids
• The roles of bulk and surface thermodynamics in the selective adsorption of a confined azeotropic mixture
• Determining the chemical potential via universal density functional learning
• Metadensity functional learning for classical fluids: Regularizing with pair correlations
• A unified machine-learning framework for ab initio multiscale modeling of liquids
• Slater-Condon Rules and Spin-Orbit Couplings: 2-(2-(2,5-Dimethoxybenzylidene)hydrazineyl)-4-(trifluoromethyl)thiazole a test case