In the quest for sustainable, high-performance energy storage, the development of advanced batteries is crucial. Addressing the complexities of battery materials and processes requires novel, data-driven research approaches. The Amsterdam Modeling Suite (AMS) offers a robust framework for simulating battery materials at multiple levels of theory, integrating atomistic engines (DFT, DFTB, ReaxFF, ML potentials) with a central driver for exploring the potential energy surfaces (PES) via molecular dynamics and Grand Canonical Monte Carlo simulations. The combination of the AMS driver with its dedicated Python interface enables the automatic screening of materials to optimize critical battery properties such as intercalation potentials, diffusion constants, activation energies, redox potentials, mechanical properties, etc. A key innovation of AMS2024 is its platform for machine learning interatomic potentials, enhancing simulation accuracy while conserving low computational resources. With ParAMS, you can train a single or a committee of machine learning potentials, from scratch, fine-tune universal models, or actively learn the PES based on target molecular dynamics simulation. This facilitates rapid prediction of material properties and reaction mechanisms, accelerating the design and testing of new battery technologies. Our data-driven approach empowers the battery R&D community to overcome traditional development barriers, enabling precise material performance predictions and the discovery of new materials and mechanisms towards realizing high-energy, stable, and safe energy storage systems.

We apply a global sensitivity method, the Hilbert-Schmidt independence criterion (HSIC), to the reparametrization of a Zn/S/H ReaxFF force field to identify the most appropriate parameters for reparametrization. Parameter selection remains a challenge in this context, as high-dimensional optimizations are prone to overfitting and take a long time but selecting too few parameters leads to poor-quality force fields. We show that the HSIC correctly and quickly identifies the most sensitive parameters and that optimizations done using a small number of sensitive parameters outperform those done using a higher-dimensional reasonable-user parameter selection. Optimizations using only sensitive parameters (1) converge faster, (2) have loss values comparable to those found with the naive selection, (3) have similar accuracy in validation tests, and (4) do not suffer from problems of overfitting. We demonstrate that an HSIC global sensitivity is a cheap optimization preprocessing step that has both qualitative and quantitative benefits which can substantially simplify and speed up ReaxFF reparametrizations.

Protons display a high chemical activity and strongly affect the charge storage capability in confined interlayer spaces of two-dimensional (2D) materials. As such, an accurate representation of proton dynamics under confinement is important for understanding and predicting charge storage dynamics in these materials. While often ignored in atomistic-scale simulations, nuclear quantum effects (NQEs), e.g., tunneling, can be significant under confinement even at room temperature. Using the thermostatted ring polymer molecular dynamics implementation of path integral molecular dynamics (PIMD) in conjunction with the ReaxFF force field, density functional tight binding (DFTB), and NequIP neural network potential simulations, we investigate the role of NQEs on proton and water transport in bulk water and aqueous electrolytes under confinement in Ti3C2 MXenes. Although overall NQEs are relatively small, especially in bulk, we find that they can alter both quantitative values and qualitative trends on both proton transport and water self-diffusion under confinement relative to classical MD predictions. Therefore, our results suggest the need for NQEs to be considered to simulate aqueous systems under confinement for both qualitative and quantitative accuracy.

This work introduces ParAMS-a versatile Python package that aims to make parametrization workflows in computational chemistry and physics more accessible, transparent, and reproducible. We demonstrate how ParAMS facilitates the parameter optimization for potential energy surface (PES) models, which can otherwise be a tedious specialist task. Because of the package's modular structure, various functionality can be easily combined to implement a diversity of parameter optimization protocols. For example, the choice of PES model and the parameter optimization algorithm can be selected independently. An illustration of ParAMS' strengths is provided in two case studies: (i) a density functional-based tight binding (DFTB) repulsive potential for the inorganic ionic crystal ZnO and (ii) a ReaxFF force field for the simulation of organic disulfides.

In inorganic zeolite formation, a direct correspondence between liquid state species in the synthesis and the supramolecular decoration of the pores in the as-made final zeolite has never been reported. In this paper, a direct link between the sodium speciation in the synthesis mixture and the pore structure and content of the final zeolite is demonstrated in the example of hydroxysodalite. Super-ions with 4 sodium cations bound by mono- and bihydrated hydroxide are identified as structure-directing agents for the formation of this zeolite. This documentation of inorganic solution species acting as a templating agent in zeolite formation opens new horizons for zeolite synthesis by design.

Atomic neural networks (ANNs) constitute a class of machine learning methods for predicting potential energy surfaces and physicochemical properties of molecules and materials. Despite many successes, developing interpretable ANN architectures and implementing existing ones efficiently are still challenging. This calls for reliable, general-purpose, and open-source codes. Here, we present a python library named PiNN as a solution toward this goal. In PiNN, we designed a new interpretable and high-performing graph convolutional neural network variant, PiNet, as well as implemented the established Behler-Parrinello neural network. These implementations were tested using datasets of isolated small molecules, crystalline materials, liquid water, and an aqueous alkaline electrolyte. PiNN comes with a visualizer called PiNNBoard to extract chemical insight "learned" by ANNs. It provides analytical stress tensor calculations and interfaces to both the atomic simulation environment and a development version of the Amsterdam Modeling Suite. Moreover, PiNN is highly modularized, which makes it useful not only as a standalone package but also as a chain of tools to develop and to implement novel ANNs. The code is distributed under a permissive BSD license and is freely accessible at https://github.com/Teoroo-CMC/PiNN/ with full documentation and tutorials.

Alkaline electrolyte solutions are important components in rechargeable batteries and alkaline fuel cells. As the ionic conductivity is thought to be a limiting factor in the performance of these devices, which are often operated at elevated temperatures, its temperature dependence is of significant interest. Here we use NaOH as a prototypical example of alkaline electrolytes, and for this system we have carried out reactive molecular dynamics simulations with an experimentally verified high-dimensional neural network potential derived from density-functional theory calculations. It is found that in concentrated NaOH solutions elevated temperatures enhance both the contributions of proton transfer to the ionic conductivity and deviations from the Nernst-Einstein relation. These findings are expected to be of practical relevance for electrochemical devices based on alkaline electrolyte solutions.

High-dimensional neural network potentials, proposed by Behler and Parrinello in 2007, have become an established method to calculate potential energy surfaces with first-principles accuracy at a fraction of the computational costs. The method is general and can describe all types of chemical interactions (e.g., covalent, metallic, hydrogen bonding, and dispersion) for the entire periodic table, including chemical reactions, in which bonds break or form. Typically, many-body atom-centered symmetry functions, which incorporate the translational, rotational, and permutational invariances of the potential energy surface exactly, are used as descriptors for the atomic environments. This chapter describes how such symmetry functions and high-dimensional neural network potentials are constructed and validated.

An extensive characterization of Co3 (PO4 )2 was performed by topological analysis according to Bader's Quantum Theory of Atoms in Molecules from the experimentally and theoretically determined electron density. This study sheds light on the reactivity of cobalt orthophosphate as a solid-state heterogeneous oxidative-dehydration and -dehydrogenation catalyst. Various faces of the bulk catalyst were identified as possible reactive sites given their topological properties. The charge accumulations and depletions around the two independent five- and sixfold-coordinated cobalt atoms, found in the topological analysis, are correlated to the orientation and population of the d-orbitals. It is shown that the (011) face has the best structural features for catalysis. Fivefold-coordinated ions in close proximity to advantageously oriented vacant coordination sites and electron depletions suit the oxygen lone pairs of the reactant, mainly for chemisorption. This is confirmed both from the multipole refinement as well as from density functional theory calculations. Nearby basic phosphate ions are readily available for C-H activation.

Universität Göttingen, Institut für Physi Tammannstr. 6, 37077 Göttingen, Germany Lehrstuhl für Theoretische Chemie, Ruh Germany † Electronic supplementary information coordinates of the atoms including ZnO(10 10) and ZnO(11 20) surfaces in co automated PT coordinate assignment. Ca coordinates, and two-dimensional PTFE Validation of the neural network by comp simulations. See DOI: 10.1039/c8sc03033b Cite this: Chem. Sci., 2019, 10, 1232

Neural network molecular dynamics simulations unravel the long-range proton transport properties of ZnO–water interfaces.

Solid-state catalysis is the most important sustainable principle in chemical production. It lowers energy consumption, avoids waste and in many cases even volatile organic compounds as solvents. Experimental charge density determination in combination with computational methods pave the way to discriminate inactive against active solid-state surfaces and assign catalytically active sites. This work investigated Co3(PO4)2 and identified the (011) face to be best suited for catalysis. Five-fold coordinated Co ions in close proximity to advantageously oriented electron depletion sites suit the reactant's oxygen-lone pairs most for chemisorption and heterogeneous C−H activation. More information can be found in the Full Paper by D. Stalke et al. on page 15786.

Interfaces between water and metal oxides exhibit many interesting phenomena like dissociation and recombination of water molecules and water exchange between the interface and the bulk liquid. Moreover, a variety of structural motifs can be found, differing in hydrogen bonding patterns and molecular orientations. Here, we report the structure and dynamics of liquid water interacting with the two most stable ZnO surfaces, (10 (1) over bar0) and (11 (2) over bar0), by means of reactive molecular dynamics simulations based on a machine learning high-dimensional neural network potential. For both surfaces, three distinct hydration layers can be observed within 10 angstrom from the surface with the first hydration layer (nearest to the surface) representing the most interesting region to investigate. There, water molecules dynamically dissociate and recombine, leading to a variety of chemical species at the interface. We characterized these species and their molecular environments by analyzing the properties of the hydrogen bonds and local geometries. At ZnO(11 (2) over bar0), some of the adsorbed hydroxide ions bridge two surface Zn ions, which is not observed at ZnO(10 (1) over bar0). For both surfaces, adsorbed water molecules always bind to a single Zn ion, and those located in proximity of the substrate are mostly "H-down" oriented for ZnO(10 (1) over bar0) and "flat-lying", i.e., parallel to the surface, for ZnO(11 (2) over bar0). The time scales for proton-transfer (PT) reactions are quite similar at the two surfaces, with the average lifetime of adsorbed hydroxide ions being around 41 +/- 3 ps until recombination. However, water exchange events, in which adsorbed water molecules leave the surface and enter the bulk liquid, happen more frequently at ZnO(11 (2) over bar0) than at ZnO(10 (1) over bar0).

Unraveling the atomistic details of solid/liquid interfaces, e.g., by means of vibrational spectroscopy, is of vital importance in numerous applications, from electrochemistry to heterogeneous catalysis. Water-oxide interfaces represent a formidable challenge because a large variety of molecular and dissociated water species are present at the surface. Here, we present a comprehensive theoretical analysis of the anharmonic OH stretching vibrations at the water/ZnO(101¯0) interface as a prototypical case. Molecular dynamics simulations employing a reactive high-dimensional neural network potential based on density functional theory calculations have been used to sample the interfacial structures. In the second step, one-dimensional potential energy curves have been generated for a large number of configurations to solve the nuclear Schrödinger equation. We find that (i) the ZnO surface gives rise to OH frequency shifts up to a distance of about 4 Å from the surface; (ii) the spectrum contains a number of overlapping signals arising from different chemical species, with the frequencies decreasing in the order ν(adsorbed hydroxide) > ν(non-adsorbed water) > ν(surface hydroxide) > ν(adsorbed water); (iii) stretching frequencies are strongly influenced by the hydrogen bond pattern of these interfacial species. Finally, we have been able to identify substantial correlations between the stretching frequencies and hydrogen bond lengths for all species.

Nuclear quantum effects (NQEs) cause the nuclei of light elements like hydrogen to delocalize, affecting numerous properties of water and aqueous solutions, such as hydrogen-bonding and proton transfer barriers. Here, we address the prototypical case of aqueous NaOH solutions by investigating the effects of quantum nuclear fluctuations on radial distribution functions, hydrogen-bonding geometries, power spectra, proton transfer barriers, proton transfer rates, water self-exchange rates around the Na+ cations, and diffusion coefficients, for the full room-temperature solubility range. These properties were calculated from classical and ring polymer molecular dynamics simulations employing a reactive high-dimensional neural network potential based on dispersion-corrected density functional theory reference calculations. We find that NQEs have a very small impact on the solvation structure around Na+, slightly strengthen the water-water and water-hydroxide hydrogen bonds, and lower the peak positions in the power spectra for the HOH bending and OH stretching modes by about 50 and 100 cm(-1), respectively. Moreover, NQEs significantly lower the proton transfer barriers, thus increasing the proton transfer rates, resulting in an increase of the diffusion coefficient in particular of OH-, as well as a decrease of the mean residence time of molecules in the first hydration shell around Na+ at high NaOH concentrations.

Density functional theory (DFT) is routinely used to calculate the adsorption energies of molecules on solid surfaces, which can be employed to derive surface phase diagrams. Such calculations become computationally expensive if the number of substrate atoms is large, which happens whenever the adsorbate coverage is small. Here, we propose an efficient method for calculating surface phase diagrams for redox-active adsorbates on semiconductors, that we apply to the important example of proton (H+) and hydride (H-) adsorbates on a ZnO surface. We identify the leading cause for the coverage dependence of the adsorption energies to be the filling and depletion of the disperse substrate conduction band. From only four DFT calculations, coupled with an analysis of the substrate electronic band structure and changes in the electrostatic potential within the substrate upon adsorption, we derive a phenomenological model that well describes the coverage-dependent adsorption energies. Moreover, our model allows us to extrapolate to the "infinite" supercell limit, where additional H adsorption leads to an arbitrarily small increase of the surface coverage. With this tool we are able to derive a surface phase diagram containing structures with extremely small H coverages (<0.002 ML), that have so far been unattainable. We expect that such models can be applied to a wide range of semiconductor substrates and redox-active adsorbates.

Sodium hydroxide, NaOH, is one of the most widely-used chemical reagents, but the structural properties of its aqueous solutions have only sparingly been characterized. Here, we automatically classify the cation coordination polyhedra obtained from molecular dynamics simulations. We find that, for example, with increasing concentration, octahedral coordination geometries become less favored, while the opposite is true for the trigonal prism. At high concentrations, the coordination polyhedra frequently deviate considerably from "ideal" polyhedra, because of an increased extent of interligand hydrogen-bonding, in which hydrogen bonds between two ligands, either OH2 or OH-, around the same Na+ are formed. In saturated solutions, with concentrations of about 19 mol L-1, ligands are frequently shared between multiple Na+ ions as a result of the deficiency of solvent molecules. This results in more complex structural patterns involving certain "characteristic" polyhedron connectivities, such as octahedra sharing ligands with capped trigonal prisms, and tetrahedra sharing ligands with trigonal bipyramids. The simulations were performed using a density-functional-theory-based reactive high-dimensional neural network potential, that was extensively validated against available neutron and X-ray diffraction data from the literature.

The dissociation of water is an important step in many chemical processes at solid surfaces. In particular, water often spontaneously dissociates near metal oxide surfaces, resulting in a mixture of H2O, H+, and OH- at the interface. Ubiquitous proton-transfer (PT) reactions cause these species to dynamically interconvert, but the underlying mechanisms are poorly understood. Here, we develop and use a reactive high-dimensional neural-network potential based on density functional theory data to elucidate the structural and dynamical properties of the interfacial species at the liquid-water-metal-oxide interface, using the nonpolar ZnO(101̅0) surface as a prototypical case. Molecular dynamics simulations reveal that water dissociation and recombination proceed via two types of PT reactions: (i) to and from surface oxide and hydroxide anions ("surface-PT") and (ii) to and from neighboring adsorbed hydroxide ions and water molecules ("adlayer-PT"). We find that the adlayer-PT rate is significantly higher than the surface-PT rate. Water dissociation is, for both types of PT, governed by a predominant presolvation mechanism, i.e., thermal fluctuations that cause the adsorbed water molecules to occasionally accept a hydrogen bond, resulting in a decreased PT barrier and an increased dissociation rate as compared to when no hydrogen bond is present. Consequently, we are able to show that hydrogen bond fluctuations govern PT events at the water-metal-oxide interface in a way similar to that in acidic and basic aqueous bulk solutions.