Data-Efficient Iterative Training of Gaussian Approximation Potentials: Application to Surface Structure Determination of Rutile IrO₂ and RuO₂

Machine-learning interatomic potentials like Gaussian Approximation Potentials (GAPs) constitute a powerful class of surrogate models to computationally involved first-principles calculations. At similar predictive quality but significantly reduced cost, they could leverage otherwise barely tractable extensive sampling as in global surface structure determination (SSD). This efficiency is jeopardized though, if an a priori unknown structural and chemical search space as in SSD requires an excessive number of first-principles data for the GAP training.To this end, we present a general and data-efficient iterative training protocol that blends the creation of new training data with the actual surface exploration process. Demonstrating this protocol with the SSD of low-index facets of rutile IrO2 and RuO2 , the involved simulated annealing on the basis of the refining GAP identifies a number of unknown terminations even in the restricted sub-space of (1×1) surface unit-cells. Especially in an O-poor environment, some of these, then metal-rich terminations, are thermodynamically most stable and are reminiscent of complexions as discussed for complex ceramic materials

On the Role of Long-Range Electrostatics in Machine-Learned Interatomic Potentials for Complex Battery Materials

Modeling complex energy materials such as solid-state electrolytes (SSEs) realistically at the atomistic level strains the capabilities of state-of-the-art theoretical approaches. On one hand, the system sizes and simulation time scales required are prohibitive for first-principles methods such as the density functional theory. On the other hand, parameterizations for empirical potentials are often not available, and these potentials may ultimately lack the desired predictive accuracy. Fortunately, modern machine learning (ML) potentials are increasingly able to bridge this gap, promising first-principles accuracy at a much reduced computational cost. However, the local nature of these ML potentials typically means that long-range contributions arising, for example, from electrostatic interactions are neglected. Clearly, such interactions can be large in polar materials such as electrolytes, however. Herein, we investigate the effect that the locality assumption of ML potentials has on lithium mobility and defect formation energies in the SSE Li7P3S11. We find that neglecting long-range electrostatics is unproblematic for the description of lithium transport in the isotropic bulk. In contrast, (field-dependent) defect formation energies are only adequately captured by a hybrid potential combining ML and a physical model of electrostatic interactions. Broader implications for ML-based modeling of energy materials are discussed

Coal fly ash: Linking immersion freezing behavior and physicochemical particle properties

To date, only a few studies have investigated the potential of coal fly ash particles to trigger heterogeneous ice nucleation in cloud droplets. The presented measurements aim at expanding the sparse dataset and improving process understanding of how physicochemical particle properties can influence the freezing behavior of coal fly ash particles immersed in water. Firstly, immersion freezing measurements were performed with two single particle techniques, i.e., the Leipzig Aerosol Cloud Interaction Simulator (LACIS) and the SPectrometer for Ice Nuclei (SPIN). The effect of suspension time on the efficiency of the coal fly ash particles when immersed in a cloud droplet is analyzed based on the different residence times of the two instruments and employing both dry and wet particle generation. Secondly, two cold-stage setups, one using microliter sized droplets (Leipzig Ice Nucleation Array) and one using nanoliter sized droplets (WeIzmann Supercooled Droplets Observation on Microarray setup) were applied. We found that coal fly ash particles are comparable to mineral dust in their immersion freezing behavior when being dry generated. However, a significant decrease in immersion freezing efficiency was observed during experiments with wet-generated particles in LACIS and SPIN. The efficiency of wet-generated particles is in agreement with the cold-stage measurements. In order to understand the reason behind the deactivation, a series of chemical composition, morphology, and crystallography analyses (single particle mass spectrometry, scanning electron microscopy coupled with energy dispersive X-ray microanalysis, X-ray diffraction analysis) were performed with dry- and wet-generated particles. From these investigations, we conclude that anhydrous CaSO4 and CaO - which, if investigated in pure form, show the same qualitative immersion freezing behavior as observed for dry-generated coal fly ash particles - contribute to triggering heterogeneous ice nucleation at the particle-water interface. The observed deactivation in contact with water is related to changes in the particle surface properties which are potentially caused by hydration of CaSO4 and CaO. The contribution of coal fly ash to the ambient population of ice-nucleating particles therefore depends on whether and for how long particles are immersed in cloud droplets

Kernel charge equilibration: Efficient and accurate prediction of molecular dipole moments with a machine-learning enhanced electron density model

Abstract State-of-the-art machine learning (ML) interatomic potentials use local representations of atomic environments to ensure linear scaling and size-extensivity. This implies a neglect of long-range interactions, most prominently related to electrostatics. To overcome this limitation, we herein present a ML framework for predicting charge distributions and their interactions termed kernel charge equilibration (kQEq). This model is based on classical charge equilibration (QEq) models expanded with an environment-dependent electronegativity. In contrast to previously reported neural network models with a similar concept, kQEq takes advantage of the linearity of both QEq and Kernel Ridge Regression to obtain a closed-form linear algebra expression for training the models. Furthermore, we avoid the ambiguity of charge partitioning schemes by using dipole moments as reference data. As a first application, we show that kQEq can be used to generate accurate and highly data-efficient models for molecular dipole moments.</jats:p

Coal fly ash: linking immersion freezing behavior and physicochemical particle properties

To date, only a few studies have investigated the potential of coal fly ash particles to trigger heterogeneous ice nucleation in cloud droplets. The presented measurements aim at expanding the sparse dataset and improving process understanding of how physicochemical particle properties can influence the freezing behavior of coal fly ash particles immersed in water. Firstly, immersion freezing measurements were performed with two single particle techniques, i.e., the Leipzig Aerosol Cloud Interaction Simulator (LACIS) and the SPectrometer for Ice Nuclei (SPIN). The effect of suspension time on the efficiency of the coal fly ash particles when immersed in a cloud droplet is analyzed based on the different residence times of the two instruments and employing both dry and wet particle generation. Secondly, two cold-stage setups, one using microliter sized droplets (Leipzig Ice Nucleation Array) and one using nanoliter sized droplets (WeIzmann Supercooled Droplets Observation on Microarray setup) were applied. We found that coal fly ash particles are comparable to mineral dust in their immersion freezing behavior when being dry generated. However, a significant decrease in immersion freezing efficiency was observed during experiments with wet-generated particles in LACIS and SPIN. The efficiency of wet-generated particles is in agreement with the cold-stage measurements. In order to understand the reason behind the deactivation, a series of chemical composition, morphology, and crystallography analyses (single particle mass spectrometry, scanning electron microscopy coupled with energy dispersive X-ray microanalysis, X-ray diffraction analysis) were performed with dry- and wet-generated particles. From these investigations, we conclude that anhydrous CaSO4 and CaO – which, if investigated in pure form, show the same qualitative immersion freezing behavior as observed for dry-generated coal fly ash particles – contribute to triggering heterogeneous ice nucleation at the particle–water interface. The observed deactivation in contact with water is related to changes in the particle surface properties which are potentially caused by hydration of CaSO4 and CaO. The contribution of coal fly ash to the ambient population of ice-nucleating particles therefore depends on whether and for how long particles are immersed in cloud droplets

DNA-binding proteins as site-specific nucleases

Summary DNA‐binding proteins can be converted into site‐specific nucleases by linking them to the chemical nuclease 1,10‐phenanthroline‐copper. This can be readily accomplished by converting a minor groove‐proximal amino acid to a cysteine residue using site‐directed mutagenesis and then chemically modifying the sulphydryl group with 5‐iodoacetamido‐1,10‐ phenanthroline‐copper. These chimeric scission reagents can be used as rare cutters to analyse chromosomal DNA, to test predictions based on high‐resolution nuclear magnetic resonance and X‐ray crystal structures, and to locate binding sites of proteins within genomes