Modeling Equilibration Dynamics of Hyperthermal Products of Surface Reactions Using Scalable Neural Network Potential with First-Principles Accuracy.

Equilibration dynamics of hot oxygen atoms following the dissociation of O2 on Pd(100) and Pd(111) surfaces are investigated by molecular dynamics simulations based on a scalable neural network potential enabling first-principles description of the interaction of O2 and O interacting with variable Pd supercells. By analyzing hundreds of trajectories with appropriate initial sampling, the measured distance distribution of equilibrated atom pairs on Pd(111) is well reproduced. However, our results on Pd(100) suggest that the ballistic motion of hot atoms predicted previously is a rare event under ideal conditions, while initial molecular orientation and surface thermal fluctuation could significantly affect the overall postdissociation dynamics. On both surfaces, dissociated hyperthermal oxygen atoms primarily locate their nascent positions and experience a similar random walk motion nearby.

Searching Configurations in Uncertainty Space: Active Learning of High-Dimensional Neural Network Reactive Potentials.

Neural network (NN) potential energy surfaces (PESs) have been widely used in atomistic simulations with ab initio accuracy. While constructing NN PESs, their training data points are often sampled by molecular dynamics trajectories. This strategy can be however inefficient for reactive systems involving rare events. Here, we develop an uncertainty-driven active learning strategy to automatically and efficiently generate high-dimensional NN-based reactive potentials, taking a gas-surface reaction as an example. The difference between two independent NN models is used as a simple and differentiable uncertainty metric, allowing us to quickly search in the uncertainty space and place new samples at which the PES is less reliable. By interfacing this algorithm with the first-principles simulation package, we demonstrate that a globally accurate NN potential of the H2 + Ag(111) system can be constructed with merely ∼150 data points. This PES can be further refined to describe H2 dissociation on Ag(100) by adding ∼130 more configurations on this facet. The entire process is completely automatic and self-terminated once the relative error criterion is fulfilled. Impressively, data points sampled by this uncertainty-driven strategy are substantially fewer than by the traditional trajectory-based sampling. The final NN PES not only converges well the quantum dissociation probability of the molecule but also well-reproduces the phonon properties of the substrate and is capable of describing surface temperature effects. These results show the potential of this active learning approach in developing high-dimensional NN reactive potentials in gas and condensed phases.

Automatically growing global reactive neural network potential energy surfaces: A trajectory-free active learning strategy.

An efficient and trajectory-free active learning method is proposed to automatically sample data points for constructing globally accurate reactive potential energy surfaces (PESs) using neural networks (NNs). Although NNs do not provide the predictive variance as the Gaussian process regression does, we can alternatively minimize the negative of the squared difference surface (NSDS) given by two different NN models to actively locate the point where the PES is least confident. A batch of points in the minima of this NSDS can be iteratively added into the training set to improve the PES. The configuration space is gradually and globally covered without the need to run classical trajectory (or equivalently molecular dynamics) simulations. Through refitting the available analytical PESs of H3 and OH3 reactive systems, we demonstrate the efficiency and robustness of this new strategy, which enables fast convergence of the reactive PESs with respect to the number of points in terms of quantum scattering probabilities.

Effects of chironomid larvae and Limnodrilus hoffmeisteri bioturbation on the distribution and flux of chromium at the sediment-water interface.

The impacts of chironomid larvae and the tubificid worm Limnodrilus hoffmeisteri on the distribution and flux of the heavy metal chromium (Cr) across the sediment-water interface were investigated with a 21-day laboratory microcosm experiment. The two studied species feature different bioturbation modes involving bioirrigation and upward bioconveyance. The Cr concentrations in the overlying water and pore water were measured and compared using treatments with bioturbation by a single species and by combinations of both species and a treatment with no organisms. The results indicated that both bioturbation modes significantly increased the Cr concentrations in the overlying water and pore water. The overlying water had lower Cr concentrations than the pore water. Little variation in the Cr concentrations was observed in the treatment without organisms. Both species enhanced the Cr flux from the pore water to the overlying water. The worm treatments had a great impact on the Cr concentration in the overlying water through intensive upward conveyance activity, while the chironomid larvae treatments exerted significant effects on the Cr variation in the pore water and Cr flux across the interface via bioirrigation activity. These findings reveal the importance of bioturbation in biogeochemical processes in freshwater ecosystems.