Identifying nonadditive contributions to the hydrophobicity of chemically heterogeneous surfaces via dual-loop active learning.

Hydrophobic interactions drive numerous biological and synthetic processes. The materials used in these processes often possess chemically heterogeneous surfaces that are characterized by diverse chemical groups positioned in close proximity at the nanoscale; examples include functionalized nanomaterials and biomolecules, such as proteins and peptides. Nonadditive contributions to the hydrophobicity of such surfaces depend on the chemical identities and spatial patterns of polar and nonpolar groups in ways that remain poorly understood. Here, we develop a dual-loop active learning framework that combines a fast reduced-accuracy method (a convolutional neural network) with a slow higher-accuracy method (molecular dynamics simulations with enhanced sampling) to efficiently predict the hydration free energy, a thermodynamic descriptor of hydrophobicity, for nearly 200 000 chemically heterogeneous self-assembled monolayers (SAMs). Analysis of this dataset reveals that SAMs with distinct polar groups exhibit substantial variations in hydrophobicity as a function of their composition and patterning, but the clustering of nonpolar groups is a common signature of highly hydrophobic patterns. Further molecular dynamics analysis relates such clustering to the perturbation of interfacial water structure. These results provide new insight into the influence of chemical heterogeneity on hydrophobicity via quantitative analysis of a large set of surfaces, enabled by the active learning approach.

Molecular Order Affects Interfacial Water Structure and Temperature-Dependent Hydrophobic Interactions between Nonpolar Self-Assembled Monolayers.

Understanding how material properties affect hydrophobic interactions-the water-mediated interactions that drive the association of nonpolar materials-is vital to the design of materials in contact with water. Conventionally, the magnitude of the hydrophobic interactions between extended interfaces is attributed to interfacial chemical properties, such as the amount of nonpolar solvent-exposed surface area. However, recent experiments have demonstrated that the hydrophobic interactions between uniformly nonpolar self-assembled monolayers (SAMs) also depend on molecular-level SAM order. In this work, we use atomistic molecular dynamics simulations to investigate the relationship between SAM order, water structure, and hydrophobic interactions to explain these experimental observations. The SAM-SAM hydrophobic interactions calculated from the simulations increase in magnitude as SAM order increases, matching experimental observations. We explain this trend by showing that the molecular-level order of the SAM impacts the nanoscale structure of interfacial water molecules, leading to an increase in water structure near disordered SAMs. These findings are consistent with a decrease in the solvation entropy of disordered SAMs, which is confirmed by measuring the temperature dependence of hydrophobic interactions using both simulations and experiments. This study elucidates how hydrophobic interactions can be influenced by an interfacial physical property, which may guide the design of synthetic materials with fine-tuned interfacial hydrophobicity.

Structural features of interfacial water predict the hydrophobicity of chemically heterogeneous surfaces.

The hydrophobicity of an interface determines the magnitude of hydrophobic interactions that drive numerous biological and industrial processes. Chemically heterogeneous interfaces are abundant in these contexts; examples include the surfaces of proteins, functionalized nanomaterials, and polymeric materials. While the hydrophobicity of nonpolar solutes can be predicted and related to the structure of interfacial water molecules, predicting the hydrophobicity of chemically heterogeneous interfaces remains a challenge because of the complex, non-additive contributions to hydrophobicity that depend on the chemical identity and nanoscale spatial arrangements of polar and nonpolar groups. In this work, we utilize atomistic molecular dynamics simulations in conjunction with enhanced sampling and data-centric analysis techniques to quantitatively relate changes in interfacial water structure to the hydration free energy (a thermodynamically well-defined descriptor of hydrophobicity) of chemically heterogeneous interfaces. We analyze a large data set of 58 self-assembled monolayers (SAMs) composed of ligands with nonpolar and polar end groups of different chemical identity (amine, amide, and hydroxyl) in five mole fractions, two spatial patterns, and with scaled partial charges. We find that only five features of interfacial water structure are required to accurately predict hydration free energies. Examination of these features reveals mechanistic insights into the interfacial hydrogen bonding behaviors that distinguish different surface compositions and patterns. This analysis also identifies the probability of highly coordinated water structures as a unique signature of hydrophobicity. These insights provide a physical basis to understand the hydrophobicity of chemically heterogeneous interfaces and connect hydrophobicity to experimentally accessible perturbations of interfacial water structure.

The Interplay of Ligand Properties and Core Size Dictates the Hydrophobicity of Monolayer-Protected Gold Nanoparticles.

The hydrophobicity of monolayer-protected gold nanoparticles is a crucial design parameter that influences self-assembly, preferential binding to proteins and membranes, and other nano-bio interactions. Predicting the effects of monolayer components on nanoparticle hydrophobicity is challenging due to the nonadditive, cooperative perturbations to interfacial water structure that dictate hydrophobicity at the nanoscale. In this work, we quantify nanoparticle hydrophobicity by using atomistic molecular dynamics simulations to calculate local hydration free energies at the nanoparticle-water interface. The simulations reveal that the hydrophobicity of large gold nanoparticles is determined primarily by ligand end group chemistry, as expected. However, for small gold nanoparticles, long alkanethiol ligands interact to form anisotropic bundles that lead to substantial spatial variations in hydrophobicity even for homogeneous monolayer compositions. We further show that nanoparticle hydrophobicity is modulated by changing the ligand structure, ligand chemistry, and gold core size, emphasizing that single-ligand properties alone are insufficient to characterize hydrophobicity. Finally, we illustrate that hydration free energy measurements correlate with the preferential binding of propane as a representative hydrophobic probe molecule. Together, these results show that both physical and chemical properties influence the hydrophobicity of small nanoparticles and must be considered together when predicting gold nanoparticle interactions with biomolecules.

Predicting Hydrophobicity by Learning Spatiotemporal Features of Interfacial Water Structure: Combining Molecular Dynamics Simulations with Convolutional Neural Networks.

The hydrophobicity of functionalized interfaces can be quantified by the structure and dynamics of water molecules using molecular dynamics (MD) simulations, but existing methods to quantify interfacial hydrophobicity are computationally expensive. In this work, we develop a new machine learning approach that leverages convolutional neural networks (CNNs) to predict the hydration free energy (HFE) as a measure of interfacial hydrophobicity based on water positions sampled from MD simulations. We construct a set of idealized self-assembled monolayers (SAMs) with varying surface polarities and calculate their HFEs using indirect umbrella sampling calculations (INDUS). Using the INDUS-calculated HFEs as labels and physically informed representations of interfacial water density from MD simulations as input, we train and evaluate a series of neural networks to predict SAM HFEs. By systematically varying model hyperparameters, we demonstrate that a 3D CNN trained to analyze both spatial and temporal correlations between interfacial water molecule positions leads to HFE predictions that require an order of magnitude less MD simulation time than INDUS. We showcase the power of this model to explore a large design space by predicting HFEs for a set of 71 chemically heterogeneous SAMs with varying patterns and mole fractions.

Spatially Heterogeneous Water Properties at Disordered Surfaces Decrease the Hydrophobicity of Nonpolar Self-Assembled Monolayers.

Understanding the relationship between hydrophobicity and the properties of functionalized surfaces is vital to the design of materials that interact in aqueous environments. In this Letter, we use atomistic molecular dynamics simulations to investigate the effects of surface order on the hydrophobicity of self-assembled monolayers (SAMs) containing nonpolar ligands. We find that the interfacial hydrophobicity is highly correlated with SAM order and, strikingly, poorly correlated with the solvent-accessible surface area, which typically has been related to interfacial hydrophobicity. Analysis of spatial variations in both SAM and water properties reveals that the SAM-water interface is pinned near regions of disordered SAM surfaces with increased free volume, decreasing the overall interfacial hydrophobicity. Spatial variations in ligand end group positions at disordered SAM surfaces thus translate to spatial variations in hydrophobicity, yielding heterogeneous surface properties. These findings provide new insights into how surface order can alter the hydrophobicity of chemically uniform surfaces.