Protonation Studies of a Mono-Dinitrogen Complex of Chromium Supported by a 12-Membered Phosphorus Macrocycle Containing Pendant Amines
Explore the source record for details and available documents.
bio-inspired↗
SEARCH · Engineering Papers
Results for “COMPUTATIONAL SCIENCE”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
An Implementation of Replica Exchange with Dynamical Scaling for Efficient Large-Scale Simulations
An implementation of the replica exchange with dynamical scaling (REDS) method in the commonly used molecular dynamics program GROMACS is presented. REDS is a replica exchange method that requires fewer replicas than conventional replica exchange while still providing data over a range of temperatures and can be used in either constant volume or constant pressure ensembles. Details for running REDS simulations are given, and an application to the human islet amyloid polypeptide (hIAPP) 11-25 fragment shows that the model efficiently samples conformational space.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
The melting temperature of liquid water with the effective fragment potential
Direct simulation of the solid-liquid water interface with the effective fragment potential (EFP) via the constant enthalpy and pressure (NPH) ensemble was used to estimate the melting temperature (Tm) of ice-Ih. Initial configurations and velocities, taken from equilibrated constant pressure and temperature (NPT) simulations at T = 300 K, 350 K and 400 K, respectively, yielded corresponding Tm values of 378±16 K, 382±14 K and 384±15 K. These estimates are consistently higher than experiment, albeit to the same degree with previously reported estimates using density functional theory (DFT)-based Born-Oppenheimer simulations with the Becke-Lee-Yang-Parr functional plus dispersion corrections (BLYP-D). KRB was supported by a Computational Science Graduate Fellowship from the Department of Energy. MSG was supported by a U.S. National Science Foundation Software Infrastructure (SI2) grant (ACI – 1047772). SSX acknowledges support from the US Department of Energy, Office of Science, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences and Biosciences. Pacific Northwest National Laboratory (PNNL) is a multiprogram national laboratory operated for DOE by Battelle.
Brorsen, Kurt R.↗
Building a Consistent and Reproducible Database for Adsorption Evaluation in Covalent–Organic Frameworks
We present a workflow that traces the path from the bulk structure of a crystalline material to assessing its performance in carbon capture from coal's postcombustion flue gases. This workflow is applied to a database of 324 covalent-organic frameworks (COFs) reported in the literature, to characterize their CO 2 adsorption properties using the following steps: (1) optimization of the crystal structure (atomic positions and unit cell) using density functional theory, (2) fitting atomic point charges based on the electron density, (3) characterizing the pore geometry of the structures before and after optimization, (4) computing carbon dioxide and nitrogen isotherms using grand canonical Monte Carlo simulations with an empirical interaction potential, and finally, (5) assessing the CO 2 parasitic energy via process modeling. The full workflow has been encoded in the Automated Interactive Infrastructure and Database for Computational Science (AiiDA). Both the workflow and the automatically generated provenance graph of our calculations are made available on the Materials Cloud, allowing peers to inspect every input parameter and result along the workflow, download structures and files at intermediate stages, and start their research right from where this work has left off. In particular, our set of CURATED (Clean, Uniform, and Refined with Automatic Tracking from Experimental Database) COFs, having optimized geometry and high-quality DFT-derived point charges, are available for further investigations of gas adsorption properties. We plan to update the database as new COFs are being reported.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Thermochemical and Mechanistic Studies of Electrocatalytic Hydrogen Production by Cobalt Complexes Containing Pendant Amines
Explore the source record for details and available documents.
bio-inspired↗
Hydroxide Degradation Pathways for Imidazolium Cations. A DFT Study
Imidazolium cations are promising candidates as covalently tetherable cations for application in anion exchange membranes. They have generated specific interest in alkaline membrane fuel cell applications where ammonium-based cations have been the most commonly applied but have been found to be susceptible to hydroxide attack. In the search for high stability cations, a detailed understanding of the degradation pathways and reaction barriers is required. In this work, we investigate imidazolium and benzimidazolium cations in the presence of hydroxide using density functional theory calculations for their potential in alkaline membrane fuel cells. Moreover, the dominant degradation pathway for these cations is predicted to be the nucleophilic addition–elimination pathway at the C-2 atom position on the imidazolium ring. Steric interferences, introduced by substitutions at the C-2, C-4, and C-5 atom positions, were investigated and found to have a significant, positive impact on calculated degradation energy barriers. Benzimidazolium cations, with their larger conjugated systems, are predicted to degrade much faster than their imidazolium counterparts. Our results provide important insight into designing stable cations for anion exchange membranes. Some of the molecules studied have significantly increased degradation energy barriers suggesting that they could possess significantly improved (several orders of magnitude) durability compared to traditional cations and potentially enable new applications.
30 DIRECT ENERGY CONVERSION↗
Impact of Distinct Origin Locations on the Life Cycles of Landfalling Atmospheric Rivers Over the U.S. West Coast
An atmospheric river (AR) event represents strong poleward moisture transport and is defined as a series of spatiotemporally connected instantaneous AR objects. Utilizing an AR tracking algorithm with a depth-first search (a widely used algorithm in computer science), we examine the life cycle characteristics of AR events that make landfall over the U.S. West Coast by their distinct origin locations. We report landfalling AR events from the Northwest Pacific (120°E–170°W, WLAR events) temporally last longer (5.3 versus 3.6 days on average) and have stronger intensity of integrated vapor transport (508 versus 388 kg m -1 s -1 on average) than those originating from the Northeast Pacific (125°W–170°W, ELAR events). A persistent tripole geopotential height anomaly pattern over the North Pacific modulates the origin locations and propagation of landfalling AR events. WLAR events are associated with anomalous highs over northeastern Asia and the Northeast Pacific and an anomalous low over the central North Pacific. This pattern provides favorable conditions for WLAR events to start, propagate northeastward, and make landfall in the northwestern West Coast. WLAR events contribute approximately 25% of the total winter precipitation over Washington and British Columbia. ELAR events are associated with a similar tripole pattern to the WLAR events with an eastward shift. The anomalous low over the Northeast Pacific helps ELAR events to start, propagate northeastward, and make landfall in the southwestern West Coast. Precipitation induced by ELAR events contributes up to 30% of total winter precipitation over California.
54 ENVIRONMENTAL SCIENCES↗
Hydrological Perspectives on Integrated, Coordinated, Open, Networked (ICON) Science
Hydrologic sciences depend on data monitoring, analyses, and simulations of hydrologic processes to ensure safe, sufficient, and equal water distribution. These hydrologic data come from but are not limited to primary (lab, plot, and field experiments) and secondary sources (remote sensing, UAVs, hydrologic models) that typically follow FAIR Principles (Findable, Accessible, Interoperable, and Reusable: (go-fair.org)). Easy availability of FAIR data has become possible because the hydrology-oriented organizations have pushed the community to increase coordination of the protocols for generating data and sharing model platforms. In addition, networking at all levels has emerged with an invigorated effort to activate community science efforts that complement conventional data collection methods. However, it has become difficult to decipher various complex hydrologic processes with increasing data. Machine learning, a branch of artificial intelligence, provide more accurate and faster alternatives to better understand different hydrological processes. The Integrated, Coordinated, Open, Networked (ICON) framework provides a pathway for water users to include and respect diversity, equity, and inclusivity. In addition, ICONs support the integration of peoples with historically marginalized identities into this professional discipline of water sciences. This article comprises three independent commentaries about the state of ICON principles in hydrology and discusses the opportunities and challenges of adopting them.
(ICON) principles to address↗
Variational quantum state eigensolver
Extracting eigenvalues and eigenvectors of exponentially large matrices will be an important application of near-term quantum computers. The variational quantum eigensolver (VQE) treats the case when the matrix is a Hamiltonian. Here, we address the case when the matrix is a density matrix ρ. We introduce the variational quantum state eigensolver (VQSE), which is analogous to VQE in that it variationally learns the largest eigenvalues of ρ as well as a gate sequence V that prepares the corresponding eigenvectors. VQSE exploits the connection between diagonalization and majorization to define a cost function C=Tr(ρ~H) where H is a non-degenerate Hamiltonian. Due to Schur-concavity, C is minimized when ρ~=VρV† is diagonal in the eigenbasis of H. VQSE only requires a single copy of ρ (only n qubits) per iteration of the VQSE algorithm, making it amenable for near-term implementation. We heuristically demonstrate two applications of VQSE: (1) Principal component analysis, and (2) Error mitigation.
97 MATHEMATICS AND COMPUTING↗
Accelerating the discovery of materials for clean energy in the era of smart automation
The discovery and development of novel materials in the field of energy are essential to accelerate the transition to a low-carbon economy. Bringing recent technological innovations in automation, robotics and computer science together with current approaches in chemistry, materials synthesis and characterization will act as a catalyst for revolutionizing traditional research and development in both industry and academia. This Perspective provides a vision for an integrated artificial intelligence approach towards autonomous materials discovery, which, in our opinion, will emerge within the next 5 to 10 years. The approach we discuss requires the integration of the following tools, which have already seen substantial development to date: high-throughput virtual screening, automated synthesis planning, automated laboratories and machine learning algorithms. In addition to reducing the time to deployment of new materials by an order of magnitude, this integrated approach is expected to lower the cost associated with the initial discovery. Furthermore, the price of the final products (for example, solar panels, batteries and electric vehicles) will also decrease. This in turn will enable industries and governments to meet more ambitious targets in terms of reducing greenhouse gas emissions at a faster pace.
36 MATERIALS SCIENCE↗
The United States COVID-19 Forecast Hub dataset
Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages.
60 APPLIED LIFE SCIENCES↗
Crowd cluster data in the USA for analysis of human response to COVID-19 events and policies
We provide data on daily social contact intensity of clusters of people at different types of Points of Interest (POI) by zip code in Florida and California. This data is obtained by aggregating fine-scaled details of interactions of people at the spatial resolution of 10 m, which is then normalized as a social contact index. We also provide the distribution of cluster sizes and average time spent in a cluster by POI type. This data will help researchers perform fine-scaled, privacy-preserving analysis of human interaction patterns to understand the drivers of the COVID-19 epidemic spread and mitigation. Current mobility datasets either provide coarse-level metrics of social distancing, such as radius of gyration at the county or province level, or traffic at a finer scale, neither of which is a direct measure of contacts between people. We use anonymized, de-identified, and privacy-enhanced location-based services (LBS) data from opted-in cell phone apps, suitably reweighted to correct for geographic heterogeneities, and identify clusters of people at non-sensitive public areas to estimate fine-scaled contacts.
60 APPLIED LIFE SCIENCES↗
Case study on climate change effects and food security in Southeast Asia
Agriculture, a cornerstone of human civilization, faces rising challenges from climate change, resource limitations, and stagnating yields. Precise crop production forecasts are crucial for shaping trade policies, development strategies, and humanitarian initiatives. This study introduces a comprehensive machine learning framework designed to predict crop production. We leverage CMIP5 climate projections under a moderate carbon emission scenario to evaluate the future suitability of agricultural lands and incorporate climatic data, historical agricultural trends, and fertilizer usage to project yield changes. Our integrated approach forecasts significant regional variations in crop production across Southeast Asia by 2028, identifying potential cropland utilization. Specifically, the cropland area in Indonesia, Malaysia, Philippines, and Viet Nam is projected to decline by more than 10% if no action is taken, and there is potential to mitigate that loss. Moreover, rice production is projected to decline by 19% in Viet Nam and 7% in Thailand, while the Philippines may see a 5% increase compared to 2021 levels. Our findings underscore the critical impacts of climate change and human activities on agricultural productivity, offering essential insights for policy-making and fostering international cooperation.
54 ENVIRONMENTAL SCIENCES↗
Information extraction from historical well records using a large language model
Not Available
54 ENVIRONMENTAL SCIENCES↗
Experimental discovery of structure–property relationships in ferroelectric materials via active learning
Emergent functionalities of structural and topological defects in ferroelectric materials underpin an extremely broad spectrum of applications ranging from domain wall electronics to high dielectric and electromechanical responses. Many of these functionalities have been discovered and quantified via local scanning probe microscopy methods. However, the search has until now been based on either trial and error, or using auxiliary information such as the topography or domain wall structure to identify potential objects of interest on the basis of the intuition of operator or pre-existing hypotheses, with subsequent manual exploration. Here we report the development and implementation of a machine learning framework that actively discovers relationships between local domain structure and polarization-switching characteristics in ferroelectric materials encoded in the hysteresis loop. The hysteresis loops and their scalar descriptors such as nucleation bias, coercive bias and the hysteresis loop area (or more complex functionals of hysteresis loop shape) and corresponding uncertainties are used to guide the discovery of these relationships via automated piezoresponse force microscopy and spectroscopy experiments. As such, this approach combines the power of machine learning methods to learn the correlative relationships between high-dimensional data, as well as human-based physics insights encoded into the acquisition function. For ferroelectric materials, this automated workflow demonstrates that the discovery path and sampling points of on- and off-field hysteresis loops are largely different, indicating that on- and off-field hysteresis loops are dominated by different mechanisms. Here, the proposed approach is universal and can be applied to a broad range of modern imaging and spectroscopy methods ranging from other scanning probe microscopy modalities to electron microscopy and chemical imaging.
36 MATERIALS SCIENCE↗
Machine learning of optical properties of materials – predicting spectra from images and images from spectra
Assembling the world's largest materials image and spectroscopy dataset enables training of machine learning models that learn hidden relationships in materials data, providing a key example of the data requirements to capitalize on recent advancements in computer science.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
On the validity of the basis set superposition error and complete basis set limit extrapolations for the binding energy of the formic acid dimer
We report the variation of the binding energy of the Formic Acid Dimer with the size of the basis set at the Coupled Cluster with iterative Singles, Doubles and perturbatively connected Triple replacements [CCSD(T)] level of theory, estimate the Complete Basis Set (CBS) limit, and examine the validity of the Basis Set Superposition Error (BSSE)-correction for this quantity that was previously challenged by Kalescky, Kraka, and Cremer (KKC) [J. Chem. Phys. 140, 084315 (2014)]. Our results indicate that the BSSE correction, including terms that account for the substantial geometry change of the monomers due to the formation of two strong hydrogen bonds in the dimer, is indeed valid for obtaining accurate estimates for the binding energy of this system as it exhibits the expected decrease with increasing basis set size. We attribute the discrepancy between our current results and those of KKC to their use of a valence basis set in conjunction with the correlation of all electrons (i.e., including the 1s of C and O). We further show that the use of a core-valence set in conjunction with all electron correlation converges faster to the CBS limit as the BSSE correction is less than half than the valence electron/valence basis set case. The uncorrected and BSSE-corrected binding energies were found to produce the same (within 0.1 kcal/mol) CBS limits. We obtain CCSD(T)/CBS best estimates for D{sub e} = − 16.1 ± 0.1 kcal/mol and for D{sub 0} = − 14.3 ± 0.1 kcal/mol, the later in excellent agreement with the experimental value of −14.22 ± 0.12 kcal/mol.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
How quantum are non-negative wavefunctions?
We consider wavefunctions which are non-negative in some tensor product basis. We study what possible teleportation can occur in such wavefunctions, giving a complete answer in some cases (when one system is a qubit) and partial answers elsewhere. We use this to show that a one-dimensional wavefunction which is non-negative and has zero correlation length can be written in a “coherent Gibbs state” form, as explained later. We conjecture that such holds in higher dimensions. Additionally, some results are provided on possible teleportation in general wavefunctions, explaining how Schmidt coefficients before measurement limit the possible Schmidt coefficients after measurement, and on the absence of a “generalized area law” [D. Aharonov et al., in Proceedings of Foundations of Computer Science (FOCS) (IEEE, 2014), p. 246; e-print arXiv.org:1410.0951] even for Hamiltonians with no sign problem. One of the motivations for this work is an attempt to prove a conjecture about ground state wavefunctions which have an “intrinsic” sign problem that cannot be removed by any quantum circuit. We show a weaker version of this, showing that the sign problem is intrinsic for commuting Hamiltonians in the same phase as the double semion model under the technical assumption that TQO-2 holds [S. Bravyi et al., J. Math. Phys. 51, 093512 (2010)].
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗