Corrigendum: OpenCL-accelerated first-principles calculations of all-electron quantum perturbations on HPC resources.

[This corrects the article DOI: 10.3389/fchem.2023.1156891.].

OpenCL-accelerated first-principles calculations of all-electron quantum perturbations on HPC resources.

We have proposed, for the first time, an OpenCL implementation for the all-electron density-functional perturbation theory (DFPT) calculations in FHI-aims, which can effectively compute all its time-consuming simulation stages, i.e., the real-space integration of the response density, the Poisson solver for the calculation of the electrostatic potential, and the response Hamiltonian matrix, by utilizing various heterogeneous accelerators. Furthermore, to fully exploit the massively parallel computing capabilities, we have performed a series of general-purpose graphics processing unit (GPGPU)-targeted optimizations that significantly improved the execution efficiency by reducing register requirements, branch divergence, and memory transactions. Evaluations on the Sugon supercomputer have shown that notable speedups can be achieved across various materials.

Targeting Atmospheric Oxidants Can Better Reduce Sulfate Aerosol in China: H2O2 Aqueous Oxidation Pathway Dominates Sulfate Formation in Haze.

Particulate sulfate is one of the most important components in the atmosphere. The observation of rapid sulfate aerosol production during haze events provoked scientific interest in the multiphase oxidation of SO2 in aqueous aerosol particles. Diverse oxidation pathways can be enhanced or suppressed under different aerosol acidity levels and high ionic strength conditions of atmospheric aerosol. The importance of ionic strength to sulfate multiphase chemistry has been verified under laboratory conditions, though studies in the actual atmosphere are still limited. By utilizing online observations and developing an improved solute strength-dependent chemical thermodynamics and kinetics model (EF-T&K model, EF is the enhancement factor that represents the effect of ionic strength on an aerosol aqueous-phase reaction), we provided quantitative evidence that the H2O2 pathway was enhanced nearly 100 times and dominated sulfate formation for entire years (66%) in Tianjin (a northern city in China). TMI (oxygen catalyzed by transition-metal ions) (14%) and NO2 (14%) pathways got the second-highest contributions. Machine learning supported the result that aerosol sulfate production was more affected by the H2O2 pathway. The collaborative effects of atmospheric oxidants and SO2 on sulfate aerosol production were further investigated using the improved EF-T&K model. Our findings highlight the effectiveness of adopting target oxidant control as a new direction for sustainable mitigation of sulfate, given the already low SO2 concentrations in China.

Machine learning and theoretical analysis release the non-linear relationship among ozone, secondary organic aerosol and volatile organic compounds.

Fine particulate matter (PM2.5) and ozone (O3) pollutions are prevalent air quality issues in China. Volatile organic compounds (VOCs) have significant impact on the formation of O3 and secondary organic aerosols (SOA) contributing PM2.5. Herein, we investigated 54 VOCs, O3 and SOA in Tianjin from June 2017 to May 2019 to explore the non-linear relationship among O3, SOA and VOCs. The monthly patterns of VOCs and SOA concentrations were characterized by peak values during October to March and reached a minimum from April to September, but the observed O3 was exactly the opposite. Machine learning methods resolved the importance of individual VOCs on O3 and SOA that alkenes (mainly ethylene, propylene, and isoprene) have the highest importance to O3 formation; alkanes (Cn, n ≥ 6) and aromatics were the main source of SOA formation. Machine learning methods revealed and emphasized the importance of photochemical consumptions of VOCs to O3 and SOA formation. Ozone formation potential (OFP) and secondary organic aerosol formation potential (SOAFP) calculated by consumed VOCs quantitatively indicated that more than 80% of the consumed VOCs were alkenes which dominated the O3 formation, and the importance of consumed aromatics and alkenes to SOAFP were 40.84% and 56.65%, respectively. Therein, isoprene contributed the most to OFP at 41.45% regardless of the season, while aromatics (58.27%) contributed the most to SOAFP in winter. Collectively, our findings can provide scientific evidence on policymaking for VOCs controls on seasonal scales to achieve effective reduction in both SOA and O3.

Machine learning combined with the PMF model reveal the synergistic effects of sources and meteorological factors on PM2.5 pollution.

PM2.5 pollution is a complex process mainly affected by emission sources and meteorological conditions. However, it is hard to accurately assess the effects of emission sources and meteorological conditions on the variation of PM2.5 concentrations in the complex atmospheric environment. In this study, the Random Forest model with Shapley Additive exPlanations (RF-SHAP) and Partial Dependence Plot (RF-PDP) was combined with Positive Matrix Factorization (PMF) to evaluate the impacts of various factors on PM2.5 pollution. The results show that anthropogenic emissions and meteorological conditions contributed about 67% (40.5 µg/m3) and 33% (19.7 µg/m3) to variation in PM2.5 concentrations, respectively. Specifically, secondary nitrate (SN) had the greatest impact among all sources (about 45%). Hence, we further explore the impacts of the primary sources and meteorological conditions on SN formation. Coal combustion and vehicle emissions significantly contribute to the formation of SN by providing a large number of precursor NOX. Additionally, the RF-PDP method was further employed to estimate the synergistic effects of primary sources and meteorological conditions on SN formation. The results help reveal strategies to simultaneously reduce SN by controlling primary emissions under suitable meteorological conditions. This work also suggests that the machine learning model can utilize online datasets well and provide a reliable approach for analyzing the causes of PM2.5 pollution.

Texture classification by texton: statistical versus binary.

Using statistical textons for texture classification has shown great success recently. The maximal response 8 (Statistical_MR8), image patch (Statistical_Joint) and locally invariant fractal (Statistical_Fractal) are typical statistical texton algorithms and state-of-the-art texture classification methods. However, there are two limitations when using these methods. First, it needs a training stage to build a texton library, thus the recognition accuracy will be highly depended on the training samples; second, during feature extraction, local feature is assigned to a texton by searching for the nearest texton in the whole library, which is time consuming when the library size is big and the dimension of feature is high. To address the above two issues, in this paper, three binary texton counterpart methods were proposed, Binary_MR8, Binary_Joint, and Binary_Fractal. These methods do not require any training step but encode local feature into binary representation directly. The experimental results on the CUReT, UIUC and KTH-TIPS databases show that binary texton could get sound results with fast feature extraction, especially when the image size is not big and the quality of image is not poor.

Correlating linactant efficiency and self-assembly: structural basis of line activity in molecular monolayers.

Surfactants exhibit characteristic phenomena, including the reduction of interfacial free energy, self-assembly into aggregates, and even the formation of lyotropic liquid crystalline phases at high concentrations. Our research has shown that a semifluorinated phosphonic acid can act as the two-dimensional analogue of a surfactant-a linactant-by reducing the line tension between hydrocarbon-rich and fluorocarbon-rich phases in a Langmuir monolayer. This linactant can also self-assemble into nanoscale clusters in a monolayer. Here, we explore the dependence of linactant behavior on molecular structure. Members of a homologous series of partially fluorinated phosphonic acids were synthesized and tested as linactants: CF(3)(CF(2))(n-1)(CH(2))(m)PO(3)H (abbreviated as FnHmPO(3)). The tests revealed that linactants with longer hydrophobic chains were most efficient in lowering line tension. For linactants with the same overall chain length, the length of the fluorocarbon block was correlated with efficiency. Thus, the linactant efficiency was ranked in the order F8H8PO(3) < F10H6PO(3) < F8H11PO(3) < F10H9PO(3). In all cases, linactant-containing Langmuir-Blodgett monolayers exhibited nanoscale molecular clusters with characteristic dimensions of 20-30 nm; enhanced linactant efficiency was systematically correlated with larger clusters.