SEARCH · Search NASA
Results for “Surface energy”
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.
Automated Reaction Kinetics of Gas-Phase Organic Species over Multiwell Potential Energy Surfaces
Automation of rate-coefficient calculations for gas-phase organic species became possible in recent years and has transformed how we explore these complicated systems computationally. Kinetics workflow tools bring rigor and speed and eliminate a large fraction of manual labor and related error sources. In this paper we give an overview of this quickly evolving field and illustrate, through five detailed examples, the capabilities of our own automated tool, KinBot. Here we bring examples from combustion and atmospheric chemistry of C-, H-, O-, and N-atom-containing species that are relevant to molecular weight growth and autoxidation processes. The examples shed light on the capabilities of automation and also highlight particular challenges associated with the various chemical systems that need to be addressed in future work.
Oxidation and Reduction of Ir(100) Studied by High-Energy Surface X-ray Diffraction
Not Available
Machine learning potential assisted exploration of complex defect potential energy surfaces
Abstract Atomic-scale defects generated in materials under both equilibrium and irradiation conditions can significantly impact their physical and mechanical properties. Unraveling the energetically most favorable ground-state configurations of these defects is an important step towards the fundamental understanding of their influence on the performance of materials ranging from photovoltaics to advanced nuclear fuels. Here, using fluorite-structured thorium dioxide (ThO 2 ) as an exemplar, we demonstrate how density functional theory and machine learning interatomic potential can be synergistically combined into a powerful tool that enables exhaustive exploration of the large configuration spaces of small point defect clusters. Our study leads to several unexpected discoveries, including defect polymorphism and ground-state structures that defy our physical intuitions. Possible physical origins of these unexpected findings are elucidated using a local cluster expansion model developed in this work.
Neural network potentials for reactive chemistry: CASPT2 quality potential energy surfaces for bond breaking
Neural network potentials achieve CASPT2 accuracy for reactive chemistry and molecular simulations. Using transfer learning, these potentials require minimal CASPT2 data on small systems to accurately predict bond dissociation in larger systems.
A Neural Network Based Model for Reactive Heavy Hydrocarbon Potential Energy Surfaces.
Abstract not provided.
Reversible Surface Energy Storage in Molecular-Scale Porous Materials
Forcible wetting of hydrophobic pores represents a viable method for energy storage in the form of interfacial energy. The energy used to fill the pores can be recovered as pressure–volume work upon decompression. For efficient recovery, the expulsion pressure should not be significantly lower than the pressure required for infiltration. Hysteresis of the wetting/drying cycle associated with the kinetic barrier to liquid expulsion results in energy dissipation and reduced storage efficiency. In the present work, we use open ensemble (Grand Canonical) Monte Carlo simulations to study the improvement of energy recovery with decreasing diameters of planar pores. Near-complete reversibility is achieved at pore widths barely accommodating a monolayer of the liquid, thus minimizing the area of the liquid/gas interface during the cavitation process. At the same time, these conditions lead to a steep increase in the infiltration pressure required to overcome steric wall/water repulsion in a tight confinement and a considerable reduction in the translational entropy of confined molecules. In principle, similar effects can be expected when increasing the size of the liquid particles without altering the absorbent porosity. While the latter approach is easier to follow in laboratory work, we discuss the advantages of reducing the pore diameter, which reduces the cycling hysteresis while simultaneously improving the stored-energy density in the material.
Observations of surface energy fluxes and meteorology in the seasonally snow-covered high-elevation East River Watershed during SPLASH, 2021-2023
Explore the source record for details and available documents.
Intrinsic Layer-Dependent Surface Energy and Exfoliation Energy of van der Waals Materials
Publication data for paper
Convectively Induced Secondary Circulations and Wind‐Driven Heat Fluxes in the Surface Energy Balance over Land
Explore the source record for details and available documents.
On the performance of composite schemes in determining equilibrium molecular structures
Determination of equilibrium molecular structures is an essential ingredient in predicting spectroscopic parameters that help in identifying molecular carriers of microwave transitions. Here, in this study, the performance of two different ab initio composite approaches for obtaining equilibrium structures, “energy scheme” and “geometry scheme,” is explored and compared to semi-experimental equilibrium structures. This study is performed for a set of 11 molecules which includes diatomics, linear triatomics, and a few non-linear molecules. The ab initio calculations were performed using three tiers of composite chemical recipes. The current results show that as the overall rigor of calculation is increased, the semi-experimental and the ab initio numbers agree to within 0.0003 Å for all molecules in the test set. The composite approach based on correcting the potential energy surface (energy scheme) and the one based on correcting the geometry directly (geometry scheme) show excellent agreement with each other. This work represents a step toward development of efficient and highly accurate procedures for computing ab initio equilibrium structures.
LAVA 1.0: A general-purpose python toolkit for calculation of material properties with LAMMPS and VASP
Here, we introduce LAVA, a general-purpose python toolkit to provide user-friendly and high throughput calculations for material properties using both LAMMPS and VASP. It contains a set of classes as well as pre-processing and post-processing functions to prepare, execute and extract information from LAMMPS/VASP simulations. An overview of the program structure is provided. The current version contains modules to calculate elastic and mechanical properties such as lattice constant, cohesive energy, cold curve, elastic constants, bulk and shear modulus, volume conserving/non-conserving deformation path, vacancy/interstitial formation energy, surface energy, stacking fault energy, melting point, radial distribution function, and thermal expansion. These functionalities are demonstrated for different interatomic potentials and DFT calculations in Al.