A high-dimensional neural network potential for CoO.

The CoOspinel is an important material in oxidation catalysis. Its properties under catalytic conditions, i.e.

Ion Effects on Terahertz Spectra of Microsolvated Clusters.

Water clusters containing Na and Cl ions play a key role in the atmospheric chemistry of sea salt aerosols. While Na is clearly buried deep inside, Cl appears to be a chameleon since evidence for both surface-localized and interior solvation states are reported. Thus, disclosing the preferred location of Cl within clusters remains challenging.

Nuclear Quantum Effects in Liquid Water Are Marginal for Its Average Structure but Significant for Dynamics.

Isotopic substitution, which can be realized in both experiment and computer simulations, is a direct approach to assess the role of nuclear quantum effects on the structure and dynamics of matter. However, the impact of nuclear quantum effects on the structure of liquid water as probed in experiment by comparing normal to heavy water has remained controversial. To settle this issue, we employ a highly accurate machine-learned high-dimensional neural network potential to perform converged coupled cluster-quality path integral simulations of liquid HO versus DO at ambient conditions.

Hydrogen diffusion on Ni(100): A combined machine-learning, ring polymer molecular dynamics, and kinetic Monte Carlo study.

We introduce a methodological framework coupling machine-learning potentials, ring polymer molecular dynamics (RPMD), and kinetic Monte Carlo (kMC) to draw a comprehensive physical picture of the collective diffusion of hydrogen atoms on metal surfaces. For the benchmark case of hydrogen diffusion on a Ni(100) surface, the hydrogen adsorption and diffusion energetics and its dependence on the local coverage is described via a neural-network potential, where the training data are computed via periodic density functional theory (DFT) and include all relevant optimized diffusion and desorption paths, sampled by nudged elastic band optimizations and molecular dynamics simulations. Nuclear quantum effects, being crucial for processes involving hydrogen at low temperatures, are treated by RPMD.

Heat application in live cell imaging.

Thermal heating of biological samples allows to reversibly manipulate cellular processes with high temporal and spatial resolution. Manifold heating techniques in combination with live-cell imaging were developed, commonly tailored to customized applications. They include Peltier elements and microfluidics for homogenous sample heating as well as infrared lasers and radiation absorption by nanostructures for spot heating.

Data-Efficient Active Learning for Thermodynamic Integration: Acidity Constants of BiVO in Water.

The protonation state of molecules and surfaces is pivotal in various disciplines, including (electro-)catalysis, geochemistry, biochemistry, and pharmaceutics. Accurately and efficiently determining acidity constants is critical yet challenging, particularly when explicitly considering the electronic structure, thermal fluctuations, anharmonic vibrations, and solvation effects. In this research, we employ thermodynamic integration accelerated by committee Neural Network potentials, training a single machine learning model that accurately describes the relevant protonated, deprotonated, and intermediate states.

Theoretical terahertz spectroscopy of free radical solutes in solution: an EPR spin probe in water.

Free radical species are used as spin labels in electron paramagnetic resonance (EPR) spectroscopy of biomolecular systems in water, for instance in the frame of Overhauser dynamic nuclear polarization (ODNP) relaxometry to probe the local hydration water dynamics close to protein surfaces in aqueous environments. Widely used in this context are nitroxide spin probes such as TEMPO, PROXYL or MTSL derivatives. Here, we study the THz spectroscopy of HMI (2,2,3,4,5,5-HexaMethylImidazolidin-1-oxyl) in water at ambient conditions which has been recently investigated as to how its EPR properties depend on its solvation pattern in water.

Hybrid Quantum Mechanical, Molecular Mechanical, and Machine Learning Potential for Computing Aqueous-Phase Adsorption Free Energies on Metal Surfaces.

Performing reliable computer simulations of elementary processes occurring at metal-water interfaces is pivotal for novel catalyst design in sustainable energy applications. Computational catalyst design hinges on the ability to reliably and efficiently compute the potential energy surface (PES) of the system. Due to the large system sizes needed for studying processes at liquid water-metal interfaces, these systems can currently not be described using density functional theory (DFT).

Accelerating Fourth-Generation Machine Learning Potentials Using Quasi-Linear Scaling Particle Mesh Charge Equilibration.

Machine learning potentials (MLPs) have revolutionized the field of atomistic simulations by describing atomic interactions with the accuracy of electronic structure methods at a small fraction of the cost. Most current MLPs construct the energy of a system as a sum of atomic energies, which depend on information about the atomic environments provided in the form of predefined or learnable feature vectors. If, in addition, nonlocal phenomena like long-range charge transfer are important, fourth-generation MLPs need to be used, which include a charge equilibration (Qeq) step to take the global structure of the system into account.

On the Theoretical Quantification of Radii of Atoms in Molecules.

Despite the fundamental importance of radii of atoms in molecules for numerous applications in physics and chemistry, comprehensive methods for their theoretical evaluation are still scarce. Here, we present quantum chemistry-based approaches for evaluation of radii of atoms in molecules and assess their robustness by studying the agreement of van der Waals and solvent-excluded surfaces constructed by them with reference molecular surfaces. By studying a large data set of 1235 molecules, it is shown that estimation of radii via effective and free atomic volumes can accurately take the dependence of atomic radii on the chemical environment into account.

Early Stages of FUS Droplet Formation via Liquid-Liquid Phase Separation.

The phase behavior of complex biomolecular solutions may explain different cellular processes, including the organization of cells by membraneless organelles. The early stages of phase separation are crucial to understanding the underlying mechanism and identifying biomolecules that trigger or drive the transition. Here, we analyze the early events of liquid-liquid phase separation (LLPS) of FUS by multiangle time-resolved static and dynamic light scattering.

Electron iso-density surfaces provide a thermodynamically consistent representation of atomic and molecular surfaces.

The surface area of atoms and molecules plays a crucial role in shaping many physiochemical properties of materials. Despite its fundamental importance, precisely defining atomic and molecular surfaces has long been a puzzle. Among the available definitions, a straightforward and elegant approach by Bader describes a molecular surface as an iso-density surface beyond which the electron density drops below a certain cut-off.

Photo-induced structural dynamics of -nitrophenol by ultrafast electron diffraction.

The photo-induced dynamics of -nitrophenol, particularly its photolysis, has garnered significant scientific interest as a potential source of nitrous acid in the atmosphere. Although the photolysis products and preceding photo-induced electronic structure dynamics have been investigated extensively, the nuclear dynamics accompanying the non-radiative relaxation of -nitrophenol on the ultrafast timescale, which include an intramolecular proton transfer step, have not been experimentally resolved. Herein, we present a direct observation of the ultrafast nuclear motions mediating photo-relaxation using ultrafast electron diffraction.

Perspective: Atomistic simulations of water and aqueous systems with machine learning potentials.

As the most important solvent, water has been at the center of interest since the advent of computer simulations. While early molecular dynamics and Monte Carlo simulations had to make use of simple model potentials to describe the atomic interactions, accurate ab initio molecular dynamics simulations relying on the first-principles calculation of the energies and forces have opened the way to predictive simulations of aqueous systems. Still, these simulations are very demanding, which prevents the study of complex systems and their properties.

Molecular Crowding: The History and Development of a Scientific Paradigm.

It is now generally accepted that macromolecules do not act in isolation but "live" in a crowded environment, that is, an environment populated by numerous different molecules. The field of molecular crowding has its origins in the far 80s but became accepted only by the end of the 90s. In the present issue, we discuss various aspects that are influenced by crowding and need to consider its effects.

How to train a neural network potential.

The introduction of modern Machine Learning Potentials (MLPs) has led to a paradigm change in the development of potential energy surfaces for atomistic simulations. By providing efficient access to energies and forces, they allow us to perform large-scale simulations of extended systems, which are not directly accessible by demanding first-principles methods. In these simulations, MLPs can reach the accuracy of electronic structure calculations, provided that they have been properly trained and validated using a suitable set of reference data.

Hydrogen atom scattering at the AlO(0001) surface: a combined experimental and theoretical study.

Investigating atom-surface interactions is the key to an in-depth understanding of chemical processes at interfaces, which are of central importance in many fields - from heterogeneous catalysis to corrosion. In this work, we present a joint experimental and theoretical effort to gain insights into the atomistic details of hydrogen atom scattering at the α-AlO(0001) surface. Surprisingly, this system has been hardly studied to date, although hydrogen atoms as well as α-AlO are omnipresent in catalysis as reactive species and support oxide, respectively.

High-Dimensional Neural Network Potentials for Accurate Prediction of Equation of State: A Case Study of Methane.

Machine learning-based interatomic potentials, such as those provided by neural networks, are increasingly important in molecular dynamics simulations. In the present work, we consider the applicability and robustness of machine learning molecular dynamics to predict the equation of state properties of methane by using high-dimensional neural network potentials (HDNNPs). We investigate two different strategies for generating training data: one strategy based upon bulk representations using periodic cells and another strategy based upon clusters of molecules.

The emerging role of ATP as a cosolute for biomolecular processes.

ATP is an important small molecule that appears at outstandingly high concentration within the cellular medium. Apart from its use as a source of energy and a metabolite, there is increasing evidence for important functions as a cosolute for biomolecular processes. Owned to its solubilizing kosmotropic triphosphate and hydrophobic adenine moieties, ATP is a versatile cosolute that can interact with biomolecules in various ways.

Intracellular spatially-targeted chemical chaperones increase native state stability of mutant SOD1 barrel.

Amyotrophic lateral sclerosis (ALS) is a progressive neurological disorder with currently no cure. Central to the cellular dysfunction associated with this fatal proteinopathy is the accumulation of unfolded/misfolded superoxide dismutase 1 (SOD1) in various subcellular locations. The molecular mechanism driving the formation of SOD1 aggregates is not fully understood but numerous studies suggest that aberrant aggregation escalates with folding instability of mutant apoSOD1.

Accurate Fourth-Generation Machine Learning Potentials by Electrostatic Embedding.

In recent years, significant progress has been made in the development of machine learning potentials (MLPs) for atomistic simulations with applications in many fields from chemistry to materials science. While most current MLPs are based on environment-dependent atomic energies, the limitations of this locality approximation can be overcome, e.g.

Machine learning transferable atomic forces for large systems from underconverged molecular fragments.

Machine learning potentials (MLP) enable atomistic simulations with first-principles accuracy at a small fraction of the costs of electronic structure calculations. Most modern MLPs rely on constructing the potential energy, or a major part of it, as a sum of atomic energies, which are given as a function of the local chemical environments up to a cutoff radius. Since analytic forces are readily available, nowadays it is common practice to make use of both, reference energies and forces, for training these MLPs.