A classical reactive potential for molecular clusters of sulphuric acid and water

We present a two-state empirical valence bond (EVB) potential describing interactions between sulphuric acid and water molecules and designed to model proton transfer between them within a classical dynamical framework. The potential has been developed in order to study the properties of molecular clusters of these species, which are thought to be relevant to atmospheric aerosol nucleation. The particle swarm optimisation method has been used to fit the parameters of the EVB model to density functional theory (DFT) calculations. Features of the parametrised model and DFT data are compared and found to be in satisfactory agreement. In particular, it is found that a single sulphuric acid molecule will donate a proton when clustered with four water molecules at 300 K and that this threshold is temperature dependent

A high-resolution infrared spectroscopic investigation of the halogen atom-HCN entrance channel complexes solvated in superfluid helium droplets

Rotationally resolved infrared spectra are reported for the X-HCN (X = Cl, Br, I) binary complexes solvated in helium nanodroplets. These results are directly compared with that obtained previously for the corresponding X-HF complexes [J. M. Merritt, J. K\"upper, and R. E. Miller, PCCP, 7, 67 (2005)]. For bromine and iodine atoms complexed with HCN, two linear structures are observed and assigned to the $^{2}\Sigma_{1/2}$ and $^{2}\Pi_{3/2}$ ground electronic states of the nitrogen and hydrogen bound geometries, respectively. Experiments for HCN + chlorine atoms give rise to only a single band which is attributed to the nitrogen bound isomer. That the hydrogen bound isomer is not stabilized is rationalized in terms of a lowering of the isomerization barrier by spin-orbit coupling. Theoretical calculations with and without spin-orbit coupling have also been performed and are compared with our experimental results. The possibility of stabilizing high-energy structures containing multiple radicals is discussed, motivated by preliminary spectroscopic evidence for the di-radical Br-HCCCN-Br complex. Spectra for the corresponding molecular halogen HCN-X$_{2}$ complexes are also presented.Comment: 20 pages, 15 figures, 6 tables, RevTe

Improvements to the APBS biomolecular solvation software suite

The Adaptive Poisson-Boltzmann Solver (APBS) software was developed to solve the equations of continuum electrostatics for large biomolecular assemblages that has provided impact in the study of a broad range of chemical, biological, and biomedical applications. APBS addresses three key technology challenges for understanding solvation and electrostatics in biomedical applications: accurate and efficient models for biomolecular solvation and electrostatics, robust and scalable software for applying those theories to biomolecular systems, and mechanisms for sharing and analyzing biomolecular electrostatics data in the scientific community. To address new research applications and advancing computational capabilities, we have continually updated APBS and its suite of accompanying software since its release in 2001. In this manuscript, we discuss the models and capabilities that have recently been implemented within the APBS software package including: a Poisson-Boltzmann analytical and a semi-analytical solver, an optimized boundary element solver, a geometry-based geometric flow solvation model, a graph theory based algorithm for determining p$K_a$ values, and an improved web-based visualization tool for viewing electrostatics

Reactions at surfaces studied by ab initio dynamics calculations

Due to the development of efficient algorithms and the improvement of computer power it is now possible to map out potential energy surfaces (PES) of reactions at surfaces in great detail. This achievement has been accompanied by an increased effort in the dynamical simulation of processes on surfaces. The paradigm for simple reactions at surfaces -- the dissociation of hydrogen on metal surfaces -- can now be treated fully quantum dynamically in the molecular degrees of freedom from first principles, i.e., without invoking any adjustable parameters. This relatively new field of ab initio dynamics simulations of reactions at surfaces will be reviewed. Mainly the dissociation of hydrogen on clean and adsorbate covered metal surfaces and on semiconductor surfaces will be discussed. In addition, the ab initio molecular dynamics treatment of reactions of hydrogen atoms with hydrogen-passivated semiconductor surfaces and recent achievements in the ab initio description of laser-induced desorption and further developments will be addressed.Comment: 33 pages, 19 figures, submitted to Surf. Sci. Rep. Other related publications can be found at http://www.rz-berlin.mpg.de/th/paper.htm

Toward Fast and Reliable Potential Energy Surfaces for Metallic Pt Clusters by Hierarchical Delta Neural Networks.

Data-driven machine learning force fields (MLFs) are more and more popular in atomistic simulations and exploit machine learning methods to predict energies and forces for unknown structures based on the knowledge learned from an existing reference database. The latter usually comes from density functional theory calculations. One main drawback of MLFs is that physical laws are not incorporated in the machine learning models, and instead, MLFs are designed to be very flexible to simulate complex quantum chemistry potential energy surface (PES). In general, MLFs have poor transferability, and hence, a very large trainset is required to span all the target feature space to get a reliable MLF. This procedure becomes more troublesome when the PES is complicated, with a large number of degrees of freedom, in which building a large database is inevitable and very expensive, especially when accurate but costly exchange-correlation functionals have to be used. In this manuscript, we exploit a high-dimensional neural network potential (HDNNP) on Pt clusters of sizes from 6 to 20 as one example. Our standard level of energy calculation is DFT GGA (PBE) using a plane wave basis set. We introduce an approximate but fast level with the PBE functional and a minimal atomic orbital basis set, and then, a more accurate but expensive level, using a hybrid functional or nonlocal vdW functional and a plane wave basis set, is reliably predicted by learning the difference with HDNNP. The results show that such a differential approach (named ΔHDNNP) can deliver very accurate predictions (error <10 meV/atom) in reference to converged basis set energies as well as more accurate but expensive xc functionals. The overall speedup can be as large as 900 for a 20 atom Pt cluster. More importantly, ΔHDNNP shows much better transferability due to the intrinsic smoothness of the delta potential energy surface, and accordingly, one can use much smaller trainset data to obtain better accuracy than the conventional HDNNP. A multilayer ΔHDNNP is thus proposed to obtain very accurate predictions versus expensive nonlocal vdW functional calculations in which the required trainset is further reduced. The approach can be easily generalized to any other machine learning methods and opens a path to study the structure and dynamics of Pt clusters and nanoparticles

Importance of equivariant features in machine-learning interatomic potentials for reactive chemistry at metal surfaces

Reactive chemistry of molecular hydrogen at surfaces, notably dissociative sticking and hydrogen evolution, play a crucial role in energy storage, fuel cells, and chemical synthesis. Copper is a particularly interesting metal for studying these processes due to its widespread use as both a catalyst in industry and a model catalyst in fundamental research. Theoretical studies can help to decipher underlying mechanisms and reaction design, but studying these systems computationally is challenging due to the complex electronic structure of metal surfaces and the high sensitivity towards reaction barriers. In addition, ab initio molecular dynamics, based on density functional theory, is too computationally demanding to explicitly simulate reactive sticking or desorption probabilities. A promising solution to such problems can be provided through high-dimensional machine learning-based interatomic potentials (MLIPs). Despite the remarkable accuracy and fidelity of MLIPs, particularly in molecular and bulk inorganic materials simulations, their application to different facets of hybrid systems and the selection of appropriate representations remain largely unexplored. This paper addresses these issues and investigates how feature equivariance in MLIPs impacts adaptive sampling workflows and data efficiency. Specifically, we develop high-dimensional MLIPs to investigate reactive hydrogen scattering on copper surfaces and compare the performance of various MLIPs that use equivariant features for atomic representation (PaiNN) with those that use invariant representations (SchNet). Our findings demonstrate that using equivariant features can greatly enhance the accuracy and reliability of MLIPs for gas surface dynamics and that this approach should become the standard in this field

Modelling silver thin film growth on zinc oxide

Ag thin film growth on ZnO substrates has been investigated theoretically using multi-timescale simulation methods. The models are based on an atomistic approach where the interactions between atoms are treated classically using a mixture of fixed and variable charge potential energy functions. After some preliminary tests it was found that existing fixed charge potential functions were unreliable for surface growth simulations. This resulted in the development of a ReaxFF variable charge potential fitted to Ag/ZnO surface interactions. Ab initio models of simple crystal structures and surface configurations were used for potential fitting and testing. The dynamic interaction of the Ag atoms with the ZnO surface was first investigated using single point depositions, via molecular dynamics, whereby the Ag impacted various points on an irreducible symmetry zone of the ZnO surface at a range of energies. This enabled the determination of the relative numbers of atoms that could penetrate, reflect or bond to the surface as a function of incident energy. The results showed that at an energy of up to 10 eV, most atoms deposited adsorbed on top of the surface layer. The second part of the dynamic interaction involved a multi-timescale technique whereby molecular dynamics (MD) was used in the initial stages followed by an adaptive kinetic Monte Carlo (AKMC) approach to model the diffusion over the surface between impacts. An impact energy of 3 eV was chosen for this investigation. Ag was grown on various ZnO surfaces including perfect polar, O-deficient and surfaces with step edges. Initial growth suggests that Ag prefers to be spread out across a perfect surface until large clusters are forced to form. After further first layer growth, subsequent Ag atoms begin to deposit on the existing Ag clusters and are unlikely to join the first layer. Ag island formation (as mentioned within the literature) can then occur via this growth mechanism. O-deficient regions of ZnO surfaces result in unfavourable Ag adsorption sites and cause cluster formation to occur away from O-vacancies. In contrast, ZnO step edges attract deposited Ag atoms and result in the migration of surface Ag atoms to under-coordinated O atoms in the step edge. Various improvements have been made to the existing methodology in which transitions are determined. A new method for determining defects within a system, by considering the coordination number of atoms, is shown to increase the number of transitions found during single ended search methods such as the relaxation and translation (RAT) algorithm. A super-basin approach based on the mean rate method is also introduced as a method of accelerating a simulation when small energy barriers dominate. This method effectively combines states connected by small energy barriers into a single large basin and calculates the mean time to escape such basin. To accelerate growth simulations further and allow larger systems to be considered, a lattice based adaptive kinetic Monte Carlo (LatAKMC) method is developed. As off-lattice AKMC and MD results suggest Ag resides in highly symmetric adsorption sites and that low energy deposition events lead to no penetrating Ag atoms or surface deformation, the on-lattice based approach is used to grow Ag on larger perfect polar ZnO surfaces. Results from the LatAKMC approach agree with off-lattice AKMC findings and predict Ag island formation. Critical island sizes of Ag on ZnO are also approximated using a mean rate approach. Single Ag atoms are placed above an existing Ag cluster and all transition states are treated as belonging to a single large super-basin . Results indicate that small Ag clusters on the perfect ZnO surface grow in the surface plane until a critical island size of around 500 atoms is reached. Once a critical island size is reached, multiple Ag ad-atoms will deposit on the island before existing Ag atoms join the cluster layer and hence islands will grow upwards. A marked difference is seen for second layer critical island sizes; second layer Ag islands are predicted to be two orders of magnitude smaller (< 7 atoms). This analysis suggests that Ag on ZnO (000 ̄1) may exhibit Stranski-Krastanov (layer plus island) growth