This dataset contains example input files, training data sets and potential files related to the publication "First-principles-based Machine Learning Models for Phase Behavior and Transport Properties of CO2." by Mathur et al (2023). In this work, we developed machine learning models for CO2 based on different exchange-correlation DFT functionals. We assessed their performance on liquid densities, vapor-liquid equilibrium and transport properties.
Guo, Xuehui; Pan, Da; Daly, Ryan; Chen, Xi; Walker, John; Tao, Lei; McSpiritt, James; Zondlo, Mark
Gas-phase ammonia (NH3), emitted primarily from agriculture, contributes significantly to reactive nitrogen (Nr) deposition. Excess deposition of Nr to the environment causes acidification, eutrophication, and loss of biodiversity. The exchange of NH3 between land and atmosphere is bidirectional and can be highly heterogenous when underlying vegetation and soil characteristics differ. Direct measurements that assess the spatial heterogeneity of NH3 fluxes are lacking. To this end, we developed and deployed two fast-response, quantum cascade laser-based open-path NH3 sensors to quantify NH3 fluxes at a deciduous forest and an adjacent grassland separated by 700 m in North Carolina, United States from August to November, 2017. The sensors achieved 10 Hz precisions of 0.17 ppbv and 0.23 ppbv in the field, respectively. Eddy covariance calculations showed net deposition of NH3 (-7.3 ng NH3-N m−2 s−1) to the forest canopy and emission (3.2 ng NH3-N m−2 s−1) from the grassland. NH3 fluxes at both locations displayed diurnal patterns with absolute magnitudes largest midday and with smaller peaks in the afternoons. Concurrent biogeochemistry data showed over an order of magnitude higher NH3 emission potentials from green vegetation at the grassland compared to the forest, suggesting a possible explanation for the observed flux differences. Back trajectories originating from the site identified the upwind urban area as the main source region of NH3. Our work highlights the fact that adjacent natural ecosystems sharing the same airshed but different vegetation and biogeochemical conditions may differ remarkably in NH3 exchange. Such heterogeneities should be considered when upscaling point measurements, downscaling modeled fluxes, and evaluating Nr deposition for different natural land use types in the same landscape. Additional in-situ flux measurements accompanied by comprehensive biogeochemical and micrometeorological records over longer periods are needed to fully characterize the temporal variabilities and trends of NH3 fluxes and identify the underlying driving factors.
Numerical data is tabulated for all plots (Figures 2, 3a-b, 4-89, S1, S4a-b,d, S5a-b,d, S6-S156) and included as separate spreadsheets categorized by figure in a .zip file in the Supplementary Material. Error bars in Figure 4 show the spread of data observed for 4 and 5 trials on independent samples for MIL-101 and MOF-235, respectively. Figure 6a shows the average of triplicate filtrate test conversions with error propagated based on this spread. Figures 6b and S165 error bars on rate constants are determined based on propagated conversion uncertainty for independent trials and extracted standard deviations of pseudo-first order rate constants from linearized plots. Error bars on other plots represent propagation of experimental uncertainty on single trials.
Zhou, Mi; Peng, Liqun; Zhang, Lin; Mauzerall, Denise L.
This dataset is created for the paper titled 'Environmental Benefits and Household Costs of Clean Heating Options in Northern China' and published on Nature Sustainability. Based on a 2015 regional anthropogenic emission inventory (base case), we propose seven counterfactual scenarios in which all 2015 residential solid fuel heating in northern China switches to one of the following non-district heating options: clean coal with improved stoves (CCIS), natural gas heaters (NGH), resistance heaters (RH), or air-to-air heat pumps (AAHP). This dataset provides the following gridded information for the base case and each clean heating scenario: (1) annual residential heating emissions for PM2.5/NOx/SO2; (2) monthly mean surface PM2.5 concentrations from the WRF-Chem model; (3) annual PM2.5-related premature deaths calculated by the GEMM model; (4) 2015 population in China; (5) mask for provinces in China; (6) longitude and latitude of each grid center.
This distribution compiles numerous physical properties for 2,585 intrinsically disordered proteins (IDPs) obtained by coarse-grained molecular dynamics simulation. This combination comprises "Dataset A" as reported in "Featurization strategies for polymer sequence or composition design by machine learning" by Roshan A. Patel, Carlos H. Borca, and Michael A. Webb (DOI: 10.1039/D1ME00160D). The specific IDP sequences are sourced from version 9.0 of the DisProt database. The simulations were performed using the LAMMPS molecular dynamics engine. The interactions used for simulation are obtained from R. M. Regy , J. Thompson , Y. C. Kim and J. Mittal , Improved coarse-grained model for studying sequence dependent phase separation of disordered proteins, Protein Sci., 2021, 1371 —1379.
This item provides access to all configurations of single-chain nanoparticles analyzed in the manuscript "Sequence Patterning, Morphology, and Dispersity in Single-Chain Nanoparticles: Insights from Simulation and Machine Learning" by Roshan A. Patel, Sophia Colmenares, and Michael A. Webb (DOI: 10.1021/acspolymersau.3c00007). The single-chain nanoparticles derive from 320 unique precursor chains that are distinguished by the fraction of linker beads that decorate a fixed-length polymer backbone and the distribution or blockiness of those linker beads. The data is provided in the form of serialized object using the `pickle' python module. The data was compiled using Python version 3.8.8 and Clang 10.0.0. The Python object loaded from the .pkl file is a nested list, with the first dimension having 7,680 entries for the 7,680 unique single-chain nanoparticles produced in the aforementioned paper. Each of those 7,680 entries is itself a list with 20 entries, representing the 20 different simulation snapshots of the given single-chain nanoparticle. Each of the 20 entries is another list with two entries, with the first being a numpy.ndarray containing the x,y,z coordinates of all the beads comprising the single-chain nanoparticle and the second being a numpy.ndarray with a numerical encoding to indicate whether the beads are backbone (indicated as '0') or linker beads (indicated as '1'). Altogether, this provides 153,600 configurations of single-chain nanoparticles.
Khanna, Jaya; Medvigy, David; Fueglistaler, Stephan; Walko, Robert
More than 20% Amazon rainforest has been cleared in the past three decades triggering important hydroclimatic changes. Small-scale (~few kilometers) deforestation in the 1980s has caused thermally-triggered atmospheric circulations that increase regional cloudiness and precipitation frequency. However, these circulations are predicted to diminish as deforestation increases. Here we use multi-decadal satellite records and numerical model simulations to show a regime shift in the regional hydroclimate accompanying increasing deforestation in Rondônia, Brazil. Compared to the 1980s, present-day deforested areas in downwind western Rondônia are found to be wetter than upwind eastern deforested areas during the local dry season. The resultant precipitation change in the two regions is approximately ±25% of the deforested area mean. Meso-resolution simulations robustly reproduce this transition when forced with increasing deforestation alone, showing a negligible role of large-scale climate variability. Furthermore, deforestation-induced surface roughness reduction is found to play an essential role in the present-day dry season hydroclimate. Our study illustrates the strong scale-sensitivity of the climatic response to Amazonian deforestation and suggests that deforestation is sufficiently advanced to have caused a shift from a thermally- to a dynamically-driven hydroclimatic regime.
This dataset provides the data generated during the project analyzing ‘Food Consumption Strategies for Addressing Air Pollution, Climate Change, Water Use, and Public Health in China’. It includes the code for generating the alternative dietary scenarios, for analyzing the health impacts of alternative diets, and for visualization of results.
The dataset contains the model file for the Global Adjoint Tomography Model 25 (GLAD-M25). The model file contains parameters defined on the spectral-element mesh and is recommend to be used in SPECFEM3D GLOBE for seismic wave simulation at the global scale.
Chen, Xu; Li, Zhongshu; Gallagher, Kevin P.; Mauzerall, Denise L.
Power sector decarbonization requires a fundamental redirection of global finance from fossil fuel infrastructure towards low carbon technologies. Bilateral finance plays an important role in the global energy transition to non-fossil energy, but an understanding of its impact is limited. Here, for the first time, we compare the influence of overseas finance from the three largest economies – United States, China, and Japan – on power generation development beyond their borders and evaluate the associated long-term CO2 emissions. We construct a new dataset of Japanese and U.S. overseas power generation finance between 2000-2018 by analyzing their national development finance institutions’ press releases and annual reports and tracking their foreign direct investment at the power plant level. Synthesizing this new data with previously developed datasets for China, we find that the three countries’ overseas financing concentrated in fossil fuel power technologies over the studied period. Financing commitments from China, Japan, and the United States facilitated 101 GW, 95 GW, and 47 GW overseas power capacity additions, respectively. The majority of facilitated capacity additions are fossil fuel plants (64% for China, 87% for Japan, and 66% for the United States). Each of the countries’ contributions to non-hydro renewable generation was less than 15% of their facilitated capacity additions. Together, we estimate that overseas fossil fuel power financing through 2018 from these three countries will lock in 24 Gt CO2 emissions by 2060. If climate targets are to be met, replacing bilateral fossil fuel financing with financing of renewable technologies is crucial.
There has been considerable recent interest in the high-pressure behavior of silicon carbide, a potential major constituent of carbon-rich exoplanets. In this work, the atomic-level structure of SiC was determined through in situ X-ray diffraction under laser-driven ramp compression up to 1.5 TPa; stresses more than seven times greater than previous static and shock data. Here we show that the B1-type structure persists over this stress range and we have constrained its equation of state (EOS). Using this data we have determined the first experimentally based mass-radius curves for a hypothetical pure SiC planet. Interior structure models are constructed for planets consisting of a SiC-rich mantle and iron-rich core. Carbide planets are found to be ~10% less dense than corresponding terrestrial planets.
Geyman, Emily C.; Wu, Ziman; Nadeau, Matthew D.; Edmonsond, Stacey; Turner, Andrew; Purkis, Sam J.; Howes, Bolton; Dyer, Blake; Ahm, Anne-Sofie C.; Yao, Nan; Deutsch, Curtis A.; Higgins, John A.; Stolper, Daniel A.; Maloof, Adam C.
Carbonate mud represents one of the most important geochemical archives for reconstructing ancient climatic, environmental, and evolutionary change from the rock record. Mud also represents a major sink in the global carbon cycle. Yet, there remains no consensus about how and where carbonate mud is formed. In this contribution, we present new geochemical data that bear on this problem, including stable isotope and minor and trace element data from carbonate sources in the modern Bahamas such as ooids, corals, foraminifera, and green algae.
The carbon isotopic (δ13C) composition of shallow-water carbonates often is interpreted to reflect the δ13C of the global ocean and is used as a proxy for changes in the global carbon cycle. However, local platform processes, in addition to meteoric and marine diagenesis, may decouple carbonate δ13C from that of the global ocean. To shed light on the extent to which changing sediment grain composition may produce δ13C shifts in the stratigraphic record, we present new δ13C measurements of benthic foraminifera, solitary corals, calcifying green algae, ooids, coated grains, and lime mud from the modern Great Bahama Bank (GBB). This survey of a modern carbonate environment reveals δ13C variability comparable to the largest δ13C excursions in the last two billion years of Earth history.
The history of organismal evolution, seawater chemistry, and paleoclimate is recorded in layers of carbonate sedimentary rock. Meter-scale cyclic stacking patterns in these carbonates often are interpreted as representing sea level change. A reliable sedimentary proxy for eustasy would be profoundly useful for reconstructing paleoclimate, since sea level responds to changes in temperature and ice volume. However, the translation from water depth to carbonate layering has proven difficult, with recent surveys of modern shallow water platforms revealing little correlation between carbonate facies (i.e., grain size, sedimentary bed forms, ecology) and water depth. We train a convolutional neural network with satellite imagery and new field observations from a 3,000 km2 region northwest of Andros Island (Bahamas) to generate a facies map with 5 m resolution. Leveraging a newly-published bathymetry for the same region, we test the hypothesis that one can extract a signal of water depth change, not simply from individual facies, but from sequences of facies transitions analogous to vertically stacked carbonate strata. Our Hidden Markov Model (HMM) can distinguish relative sea level fall from random variability with ∼90% accuracy. Finally, since shallowing-upward patterns can result from local (autogenic) processes in addition to forced mechanisms such as eustasy, we search for statistical tools to diagnose the presence or absence of external forcings on relative sea level. With a new data-driven forward model that simulates how modern facies mosaics evolve to stack strata, we show how different sea level forcings generate characteristic patterns of cycle thicknesses in shallow carbonates, providing a new tool for quantitative reconstruction of ancient sea level conditions from the geologic record.
Chronic hepatitis B (CHB), caused by hepatitis B virus (HBV), remains a major medical problem. HBV has a high propensity for progressing to chronicity and can result in severe liver disease, including fibrosis, cirrhosis and hepatocellular carcinoma. CHB patients frequently present with viral coinfection, including HIV and hepatitis delta virus. About 10% of chronic HIV carriers are also persistently infected with HBV which can result in more exacerbated liver disease. Mechanistic studies of HBV-induced immune responses and pathogenesis, which could be significantly influenced by HIV infection, have been hampered by the scarcity of immunocompetent animal models. Here, we demonstrate that humanized mice dually engrafted with components of a human immune system and a human liver supported HBV infection, which was partially controlled by human immune cells, as evidenced by lower levels of serum viremia and HBV replication intermediates in the liver. HBV infection resulted in priming and expansion of human HLA-restricted CD8+ T cells, which acquired an activated phenotype. Notably, our dually humanized mice support persistent coinfections with HBV and HIV which opens opportunities for analyzing immune dysregulation during HBV and HIV coinfection and preclinical testing of novel immunotherapeutics.
The prevalence of ooids in the stratigraphic record, and their association with shallow-water carbonate environments, make ooids an important paleoenvironmental indicator. Recent advances in the theoretical understanding of ooid morphology, along with empirical studies from Turks and Caicos, Great Salt Lake, and The Bahamas, have demonstrated that the morphology of ooids is indicative of depositional environment and hydraulic conditions. To apply this knowledge from modern environments to the stratigraphic record of Earth history, researchers measure the size and shape of lithified ooids on two-dimensional surfaces (i.e., thin sections or polished slabs), often assuming that random 2D slices intersect the nuclei and that the orientation of the ooids is known. Here we demonstrate that these assumptions rarely are true, resulting in errors of up to 35% on metrics like major axis length. We present a method for making 3D reconstructions by serial grinding and imaging, which enables accurate measurement of the morphology of individual ooids within an oolite, as well as the sorting and porosity of a sample. We also provide three case studies that use the morphology of ooids in oolites to extract environmental information. Each case study demonstrates that 2D measurements can be useful if the environmental signal is large relative to the error from 2D measurements. However, 3D measurements substantially improve the accuracy and precision of environmental interpretations. This study focuses on oolites, but errors from 2D measurements are not unique to oolites; this method can be used to extract accurate grain and porosity measurements from any lithified granular sample.
This dataset contains input files, training data and other files related to the machine learning models developed during the work by Muniz et al. In this work, we construct machine learning models based on the MB-pol many-body model. We find that the training set should include cluster configurations as well as liquid phase configurations in order to accurately represent both liquid and VLE properties. The results attest for the ability of machine learning models to accurately represent many-body potentials and provide an efficient avenue for water simulations.
Understanding the condensed-phase behavior of chiral molecules is important in biology, as well as in a range of technological applications, such as the manufacture of pharmaceuticals. Here, we use molecular dynamics simulations to study a chiral four-site molecular model that exhibits a second-order symmetry-breaking phase transition from a supercritical racemic liquid, into subcritical D-rich and L-rich liquids. We determine the infinite-size critical temperature using the fourth-order Binder cumulant, and we show that the finite-size scaling behavior of the order parameter is compatible with the 3D Ising universality class. We also study the spontaneous D-rich to L-rich transition at a slightly subcritical temperature T ~ 0.985 Tc and our findings indicate that the free energy barrier for this transformation increases with system size as N^2/3 where N is the number of molecules, consistent with a surface-dominated phenomenon. The critical behavior observed herein suggests a mechanism for chirality selection in which a liquid of chiral molecules spontaneously forms a phase enriched in one of the two enantiomers as the temperature is lowered below the critical point. Furthermore, the increasing free energy barrier with system size indicates that fluctuations between the L-rich and D-rich phases are suppressed as the size of the system increases, trapping it in one of the two enantiomerically-enriched phases. Such a process could provide the basis for an alternative explanation for the origin of biological homochirality. We also conjecture the possibility of observing nucleation at subcritical temperatures under the action of a suitable chiral external field.
This dataset contains input and output files to reproduce the results of the manuscript "Homogeneous ice nucleation in an ab initio machine learning model" by Pablo M. Piaggi, Jack Weis, Athanassios Z. Panagiotopoulos, Pablo G. Debenedetti, and Roberto Car (arXiv preprint https://arxiv.org/abs/2203.01376). In this work, we studied the homogeneous nucleation of ice from supercooled liquid water using a machine learning model trained on ab initio energies and forces. Since nucleation takes place over times much longer than the simulation times that can be afforded using molecular dynamics simulations, we make use of the seeding technique that is based on simulating an ice cluster embedded in liquid water. The key quantity provided by the seeding technique is the size of the critical cluster (i.e., a size such that the cluster has equal probabilities of growing or shrinking at the given supersaturation). Using data from the seeding simulations and the equations of classical nucleation theory we compute nucleation rates that can be compared with experiments.