Protonation Studies of a Mono-Dinitrogen Complex of Chromium Supported by a 12-Membered Phosphorus Macrocycle Containing Pendant Amines
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bio-inspired↗
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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.
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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↗
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↗
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↗
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↗
Graeme A. Bird
Few of those who read the 1963 research note by Graeme A. Bird in Physics of Fluids could have imagined that 50 years later the proposed new numerical technique would have become the dominant numerical technique in molecular gas dynamics. The introduction of the Direct Simulation Monte Carlo (DSMC) method not only has altered the field of molecular gas dynamics but also has influenced fields such as physical chemistry, mathematics, computer science, and aerothermodynamics. Further, the DSMC method has been used to probe into previously uninvestigated theoretical aspects of the Boltzmann equation and has also served as a platform for the development of nonequilibrium chemistry models. The DSMC method’s most noteworthy achievement is that molecular gas dynamics became a practical tool in the hands of aerospace engineers in many situations of spacecraft design and mission analysis.
99 GENERAL AND MISCELLANEOUS↗
Reproducibility of fixed-node diffusion Monte Carlo across diverse community codes: The case of water–methane dimer
Fixed-node diffusion quantum Monte Carlo (FN-DMC) is a widely trusted many-body method for solving the Schrödinger equation, known for its reliable predictions of material and molecular properties. Furthermore, its excellent scalability with system complexity and near-perfect utilization of computational power make FN-DMC ideally positioned to leverage new advances in computing to address increasingly complex scientific problems. Even though the method is widely used as a computational gold standard, reproducibility across the numerous FN-DMC code implementations has yet to be demonstrated. This difficulty stems from the diverse array of DMC algorithms and trial wave functions, compounded by the method’s inherent stochastic nature. Here, this study represents a community-wide effort to assess the reproducibility of the method, affirming that yes, FN-DMC is reproducible (when handled with care). Using the water–methane dimer as the canonical test case, we compare results from eleven different FN-DMC codes and show that the approximations to treat the non-locality of pseudopotentials are the primary source of the discrepancies between them. In particular, we demonstrate that, for the same choice of determinantal component in the trial wave function, reliable and reproducible predictions can be achieved by employing the T-move, the determinant locality approximation, or the determinant T-move schemes, while the older locality approximation leads to considerable variability in results. These findings demonstrate that, with appropriate choices of algorithmic details, fixed-node DMC is reproducible across diverse community codes—highlighting the maturity and robustness of the method as a tool for open and reliable computational science.
Della Pia, Flaviano [Univ. of Cambridge (United Ki↗
Transformational Challenge Reactor Safety Design and Radionuclide Retention Strategy
An integrated safety design and radionuclide (RN) retention strategy is developed to support the Transformational Challenge Reactor (TCR) demonstration. This demonstration aims to showcase viability for rapid deployment of a novel reactor by leveraging the advances in materials, manufacturing, and computational sciences through a highly integrated and agile design and development approach. This strategy provides a logical description and understanding of how RNs are contained within the facility. Rather than discussing fission product barriers individually between separate design and safety basis reports, this paper provides a consistent description and narrative to better facilitate regulatory interactions and focus safety design efforts. The principal barriers credited include the various coating layers in the tristructural isotropic (TRISO) fuel particle, the silicon carbide (SiC) matrix hosting the particles within the fuel element, the helium pressure boundary, and the confinement system. The choice and assumed performance of the credited barriers are highly conservative, which is a direct reflection of the low hazard that the TCR demonstration presents and the need to simplify and focus the safety review process accordingly. However, the strategy and the associated framework are generalized and may be adopted and tailored to support other advanced reactor demonstration efforts.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Modeling the role of preferential snow accumulation in through talik development and hillslope groundwater flow in a transitional permafrost landscape
Not Available
54 ENVIRONMENTAL SCIENCES↗