Molecular Dynamics Simulation of Zinc Ion in Water with an ab Initio Based Neural Network Potential

Design Principles for Gradient Porous Carbon on Aqueous Zinc-Ion Hybrid Capacitors: A Combined Molecular Dynamic and Machine Learning Study

Gradient porous carbon has become a potential electrode material for energy storage devices, including the aqueous zinc-ion hybrid capacitor (ZIHC). Compared with the sufficient studies on the fabrication of ZIHCs with high electrochemical performance, there is still lack of in-depth understanding of the underlying mechanisms of gradient porous structure for energy storage, especially the synergistic effect of ultramicropores (<1 nm) and micropores (1-2 nm). Here, we report a design principle for the gradient porous carbon structure used for ZIHC based on the data-mining machine learning (ML) method. It is clarified that the combination of 0.6-0.9 nm ultramicropore and 1.6 nm micropore achieves the highest specific capacity. Molecular dynamic simulation was further employed to investigate the electric double-layer structures in several kinds of electrified gradient porous carbon electrode/electrolyte interface. It is found that the Zn2+ ions in the 1.6 nm micropore balance the most charges of the electrode surface as the counterion with the modification of the solvation structure. Furthermore, the ML-based force field is trained and employed in the simulation of the ion charging dynamic in the gradient porous carbon electrode. Based on the free energy profile result, the remarkable benefit of the step-by-step desolvation process is found in the 0.86 and 1.6 nm gradient porous structure, which could be the origin of the enhanced ion charging dynamic and better capacity retention performance.

Structure and size of complete hydration shells of metal ions and inorganic anions in aqueous solution

The structures of nine hydrated metal ions in aqueous solution have been redetermined by large angle X-ray scattering to obtain experimental data of better quality than those reported 40-50 years ago. Accurate M-OI and M-(OI-H)⋯OII distances and M-OI(H)⋯OII bond angles are reported for the hydrated magnesium(II), aluminium(III), manganese(II), iron(II), iron(III), cobalt(II), nickel(II), copper(II) and zinc(II) ions; the subscripts I and II denote oxygen atoms in the first and second hydration sphere, respectively. Reported structures of hydrated metal ions in aqueous solution are summarized and evaluated with emphasis on a possible relationship between M-OI-OII bond angles and bonding character. Metal ions with high charge density have M-OI-OII bond angles close to 120°, indicative of a mainly electrostatic interaction with the oxygen atom in the water molecule in the first hydration shell. Metal ions forming bonds with a significant covalent contribution, as e.g. mercury(II) and tin(II), have M-OI-OII bond angles close to 109.5°. This implies that they bind to one of the free electron pairs in the water molecule. Comparison of M-O bond distances of hydrated metal ions in the solid state with one hydration shell, and in aqueous solution with in most cases at least two hydration shells, shows no significant differences. On the other hand, the X-O bond distance in hydrated oxoanions increases by ca. 0.02 Å in aqueous solution in comparison with the corresponding X-O distance in the solid state. A linear correlation is observed between volume, calculated from the van der Waals radius of the hydrated ion, and the ionic diffusion coefficient in aqueous solution. This correlation strongly indicates that monovalent metal ions, except lithium and silver(I), and singly-charged monovalent oxoanions have a single hydration shell. Divalent metal ions, bismuth(III) and the lanthanoid(III) and actinoid(III) ions have two hydration shells. Trivalent transition and tetravalent metal ions have two full hydration shells and portion of a third one. Doubly charged oxoanions have one well-defined hydration shell and an ill-defined second one.

Modeling Intermolecular Interactions with Exchange-Hole Dipole Moment Dispersion Corrections to Neural Network Potentials

Neural network potentials (NNPs) are an innovative approach for calculating the potential energy and forces of a chemical system. In principle, these methods are capable of modeling large systems with an accuracy approaching that of a high-level ab initio calculation, but with a much smaller computational cost. Due to their training to density-functional theory (DFT) data and neglect of long-range interactions, some classes of NNPs require an additional term to include London dispersion physics. In this Perspective, we discuss the requirements for a dispersion model for use with an NNP, focusing on the MLXDM (Machine Learned eXchange-Hole Dipole Moment) model developed by our groups. This model is based on the DFT-based XDM dispersion correction, which calculates interatomic dispersion coefficients in terms of atomic moments and polarizabilities, both of which can be approximated effectively using neural networks.

Spontaneous Desaturation of the Solvation Sheath for High-Performance Anti-Freezing Zinc-Ion Gel-Electrolyte

In recent years, gel-electrolyte becomes pivotal in preventing hydrogen evolution, reducing dendrite growth, and protecting the zinc metal anode for zinc-ion batteries. Herein, a polyvinyl alcohol-based water-organic hybrid gel electrolyte with Agar and dimethyl sulfoxide is designed to construct the spontaneous desaturation of the solvation sheath for reducing hydrogen evolution and dendrite growth at room temperature and even low temperature. According to experimental characterization and theoretical calculations, the well binding between multihydroxy polymer and H2 O is achieved in the hybrid desaturated gel-electrolyte to regulate the inner and outer sheath. The ionic conductivity of hybrid gel-electrolyte reaches 7.4 mS cm-1 even at -20 °C with only 0.5 m zinc trifluoromethanesulfonate (Zn(OTf)2 ). The Zn symmetric cells cycle over 1200 h under 26 and -20 °C with improved mechanical properties and electrochemical performance. The asymmetric Zn || Cu cell with hybrid gel electrolyte reaches ≈99.02% efficiency after 250 cycles. The capacity of full cell is maintained at around 74 mAh g-1 with almost unchanged retention rate from 50 to 300 cycles at -20 °C. This work provides an effective strategy for desaturated solvation to reach anti-freezing and high-density Zn energy storage devices.

A multifunctional protective layer with biomimetic ionic channel suppressing dendrite and side reactions on zinc metal anodes

A multifunctional graphitic carbon nitride (GCN) protective layer with bionic ion channels and high stability is prepared to inhibit dendrite growth and side reactions on zinc (Zn) metal anodes. The high electronegativity of the nitrogen-containing organic groups (NOGs) in the GCN layer can effectively promote the dissociation of solvated Zn²⁺ and its rapid transportation in bionic ion channels via a hopping mechanism. In addition, this GCN layer exhibits excellent mechanical strength to suppress the growth of Zn dendrites and the volume expansion of Zn metal anodes during the plating process. Consequently, the electrodeposited Zn presents a uniform and densely packed morphology with negligible side-product accumulation. As a result, the half-cell composed of the Cu-GCN anode can deliver a remarkable long-term cycling performance of 1000 h at 0.5 mA cm^-2 and 0.25 mAh cm^-2. A full cell assembled with MnO₂ cathode also displays improved long-term cycling performance (150 cycles at 200 mA g^-1) when the Cu-GCN@Zn composite anode is applied. This work deepens our understanding of the kinetics of ion migration in the interface layer and paves the way for next-generation high energy-density Zn-metal batteries (ZMBs).

Multiscale Workflow for Modeling Ligand Complexes of Zinc Metalloproteins

Zinc metalloproteins are ubiquitous, with protein zinc centers of structural and functional importance, involved in interactions with ligands and substrates and often of pharmacological interest. Biomolecular simulations are increasingly prominent in investigations of protein structure, dynamics, ligand interactions, and catalysis, but zinc poses a particular challenge, in part because of its versatile, flexible coordination. A computational workflow generating reliable models of ligand complexes of biological zinc centers would find broad application. Here, we evaluate the ability of alternative treatments, using (nonbonded) molecular mechanics (MM) and quantum mechanics/molecular mechanics (QM/MM) at semiempirical (DFTB3) and density functional theory (DFT) levels of theory, to describe the zinc centers of ligand complexes of six metalloenzyme systems differing in coordination geometries, zinc stoichiometries (mono- and dinuclear), and the nature of interacting groups (specifically the presence of zinc-sulfur interactions). MM molecular dynamics (MD) simulations can overfavor octahedral geometries, introducing additional water molecules to the zinc coordination shell, but this can be rectified by subsequent semiempirical (DFTB3) QM/MM MD simulations. B3LYP/MM geometry optimization further improved the accuracy of the description of coordination distances, with the overall effectiveness of the approach depending upon factors, including the presence of zinc-sulfur interactions that are less well described by semiempirical methods. We describe a workflow comprising QM/MM MD using DFTB3 followed by QM/MM geometry optimization using DFT (e.g., B3LYP) that well describes our set of zinc metalloenzyme complexes and is likely to be suitable for creating accurate models of zinc protein complexes when structural information is more limited.

Artificial Intelligence Applied to Battery Research: Hype or Reality?

This is a critical review of artificial intelligence/machine learning (AI/ML) methods applied to battery research. It aims at providing a comprehensive, authoritative, and critical, yet easily understandable, review of general interest to the battery community. It addresses the concepts, approaches, tools, outcomes, and challenges of using AI/ML as an accelerator for the design and optimization of the next generation of batteries—a current hot topic. It intends to create both accessibility of these tools to the chemistry and electrochemical energy sciences communities and completeness in terms of the different battery R&D aspects covered.

Automation of some macromolecular properties using a machine learning approach

In this study, we employed a newly developed method to predict macromolecular properties using a swarm artificial neural network (ANN) method as a machine learning approach. In this method, the molecular structures are represented by the feature description vectors used as training input data for a neural network. This study aims to develop an efficient approach for training an ANN using either experimental or quantum mechanics data. We aim to introduce an error model controlling the reliability of the prediction confidence interval using a bootstrapping swarm approach. We created different datasets of selected experimental or quantum mechanics results. Using this optimized ANN, we hope to predict properties and their statistical errors for new molecules. There are four datasets used in this study. That includes the dataset of 642 small organic molecules with known experimental hydration free energies, the dataset of 1475 experimental pKa values of ionizable groups in 192 proteins, the dataset of 2693 mutants in 14 proteins with given experimental values of changes in the Gibbs free energy, and a dataset of 7101 quantum mechanics heat of formation calculations. All the data are prepared and optimized using the AMBER force field in the CHARMM macromolecular computer simulation program. The bootstrapping swarm ANN code for performing the optimization and prediction is written in Python computer programming language. The descriptor vectors of the small molecules are based on the Coulomb matrix and sum over bond properties. For the macromolecular systems, they consider the chemical-physical fingerprints of the region in the vicinity of each amino acid.

Automatically Constructed Neural Network Potentials for Molecular Dynamics Simulation of Zinc Proteins

The development of accurate and efficient potential energy functions for the molecular dynamics simulation of metalloproteins has long been a great challenge for the theoretical chemistry community. An artificial neural network provides the possibility to develop potential energy functions with both the efficiency of the classical force fields and the accuracy of the quantum chemical methods. In this work, neural network potentials were automatically constructed by using the ESOINN-DP method for typical zinc proteins. For the four most common zinc coordination modes in proteins, the potential energy, atomic forces, and atomic charges predicted by neural network models show great agreement with quantum mechanics calculations and the neural network potential can maintain the coordination geometry correctly. In addition, MD simulation and energy optimization with the neural network potential can be readily used for structural refinement. The neural network potential is not limited by the function form and complex parameterization process, and important quantum effects such as polarization and charge transfer can be accurately considered. The algorithm proposed in this work can also be directly applied to proteins containing other metal ions.

Conformational Shifts of Stacked Heteroaromatics: Vacuum vs. Water Studied by Machine Learning

Stacking interactions play a crucial role in drug design, as we can find aromatic cores or scaffolds in almost any available small molecule drug. To predict optimal binding geometries and enhance stacking interactions, usually high-level quantum mechanical calculations are performed. These calculations have two major drawbacks: they are very time consuming, and solvation can only be considered using implicit solvation. Therefore, most calculations are performed in vacuum. However, recent studies have revealed a direct correlation between the desolvation penalty, vacuum stacking interactions and binding affinity, making predictions even more difficult. To overcome the drawbacks of quantum mechanical calculations, in this study we use neural networks to perform fast geometry optimizations and molecular dynamics simulations of heteroaromatics stacked with toluene in vacuum and in explicit solvation. We show that the resulting energies in vacuum are in good agreement with high-level quantum mechanical calculations. Furthermore, we show that using explicit solvation substantially influences the favored orientations of heteroaromatic rings thereby emphasizing the necessity to include solvation properties starting from the earliest phases of drug design.

Integrating Machine Learning with the Multilayer Energy-Based Fragment Method for Excited States of Large Systems

In this work we have combined machine learning techniques with our recently developed multilayer energy-based fragment method for studying excited states of large systems. The photochemically active and inert regions are separately treated with the complete active space self-consistent field method and the trained models. This method is demonstrated to provide accurate energies and gradients leading to essentially the same excited-state potential energy surfaces and nonadiabatic dynamics compared with full ab initio results. Furthermore, in conjunction with the use of machine learning models, this method is highly parallel and exhibits low-scaling computational cost. Finally, the present work could encourage the marriage of machine learning with fragment-based electronic structure methods to explore photochemistry of large systems.