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Abstract. The hydroxyl (OH), hydroperoxy (HO2), and organic peroxy (RO2)radicals play important roles in atmospheric chemistry. In the presence ofnitrogen oxides (NOx), reactions between OH and volatile organiccompounds (VOCs) can initiate a radical propagation cycle that leads to theproduction of ozone and secondary organic aerosols. Previous measurements ofthese radicals under low-NOx conditions in forested environmentscharacterized by emissions of biogenic VOCs, including isoprene andmonoterpenes, have shown discrepancies with modeled concentrations. During the summer of 2016, OH, HO2, and RO2 radical concentrationswere measured as part of the Program for Research on Oxidants:Photochemistry, Emissions, and Transport – Atmospheric Measurements ofOxidants in Summer (PROPHET-AMOS) campaign in a midlatitude deciduousbroadleaf forest. Measurements of OH and HO2 were made by laser-inducedfluorescence–fluorescence assay by gas expansion (LIF-FAGE) techniques,and total peroxy radical (XO2) mixing ratios were measured by the Ethane CHemical AMPlifier (ECHAMP) instrument. Supporting measurements ofphotolysis frequencies, VOCs, NOx, O3, and meteorological datawere used to constrain a zero-dimensional box model utilizing either theRegional Atmospheric Chemical Mechanism (RACM2) or the Master ChemicalMechanism (MCM). Model simulations tested the influence of HOxregeneration reactions within the isoprene oxidation scheme from the LeuvenIsoprene Mechanism (LIM1). On average, the LIM1 models overestimated daytimemaximum measurements by approximately 40 % for OH, 65 % for HO2,and more than a factor of 2 for XO2. Modeled XO2 mixing ratioswere also significantly higher than measured at night. Addition of RO2 + RO2 accretion reactions for terpene-derived RO2 radicals tothe model can partially explain the discrepancy between measurements andmodeled peroxy radical concentrations at night but cannot explain thedaytime discrepancies when OH reactivity is dominated by isoprene. Themodels also overestimated measured concentrations of isoprene-derivedhydroxyhydroperoxides (ISOPOOH) by a factor of 10 during the daytime,consistent with the model overestimation of peroxy radical concentrations.Constraining the model to the measured concentration of peroxy radicalsimproves the agreement with the measured ISOPOOH concentrations, suggestingthat the measured radical concentrations are more consistent with themeasured ISOPOOH concentrations. These results suggest that the models maybe missing an important daytime radical sink and could be overestimating therate of ozone and secondary product formation in this forest.

 

We consider a discrete non-linear Schrödinger equation on Z and show that, after adding a small potential localized in the time-frequency space, one can construct a three-parametric family of non-decaying spacetime quasiperiodic solutions to this equation. The proof is based on the Craig–Wayne–Bourgain method combined with recent techniques of dealing with Anderson localization for two-dimensional quasiperiodic operators with degenerate frequencies.

 

Learning multi-agent system dynamics has been extensively studied for various real-world applications, such as molecular dynamics in biology, multi-body system in physics, and particle dynamics in material science. Most of the existing models are built to learn single system dynamics, which learn the dynamics from observed historical data and predict the future trajectory. In practice, however, we might observe multiple systems that are generated across different environments, which differ in latent exogenous factors such as temperature and gravity. One simple solution is to learn multiple environment-specific models, but it fails to exploit the potential commonalities among the dynamics across environments and offers poor prediction results where per-environment data is sparse or limited. Here, we present GG-ODE (Generalized Graph Ordinary Differential Equations), a machine learning framework for learning continuous multi-agent system dynamics across environments. Our model learns system dynamics using neural ordinary differential equations (ODE) parameterized by Graph Neural Networks (GNNs) to capture the continuous interaction among agents. We achieve the model generalization by assuming the dynamics across different environments are governed by common physics laws that can be captured via learning a shared ODE function. The distinct latent exogenous factors learned for each environment are incorporated into the ODE function to account for their differences. To improve model performance, we additionally design two regularization losses to (1) enforce the orthogonality between the learned initial states and exogenous factors via mutual information minimization; and (2) reduce the temporal variance of learned exogenous factors within the same system via contrastive learning. Experiments over various physical simulations show that our model can accurately predict system dynamics, especially in the long range, and can generalize well to new systems with few observations. 

The superior size and power scaling potential of ferroelectric-gated Mott transistors makes them promising building blocks for developing energy-efficient memory and logic applications in the post-Moore’s Law era. The close to metallic carrier density in the Mott channel, however, imposes the bottleneck for achieving substantial field effect modulation via a solid-state gate. Previous studies have focused on optimizing the thickness, charge mobility, and carrier density of single-layer correlated channels, which have only led to moderate resistance switching at room temperature. Here, we report a record high nonvolatile resistance switching ratio of 38,440% at 300 K in a prototype Mott transistor consisting of a ferroelectric PbZr_0.2Ti_0.8O₃gate and anRNiO₃(R: rare earth)/La_0.67Sr_0.33MnO₃composite channel. The ultrathin La_0.67Sr_0.33MnO₃buffer layer not only tailors the carrier density profile inRNiO₃through interfacial charge transfer, as corroborated by first-principles calculations, but also provides an extended screening layer that reduces the depolarization effect in the ferroelectric gate. Our study points to an effective material strategy for the functional design of complex oxide heterointerfaces that harnesses the competing roles of charge in field effect screening and ferroelectric depolarization effects.

 

A graph-based machine learning model is built to predict atom dynamics from their static structure, which, in turn, unveils the predictive power of static structure in dynamical evolution of disordered phases.