Using Constrained Density Functional Theory to Track Proton Transfers and to Sample Their Associated Free Energy Surface
Not Available
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
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.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Oxidation and Reduction of Ir(100) Studied by High-Energy Surface X-ray Diffraction
Not Available
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
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.
36 MATERIALS SCIENCE↗
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.
Hu, Quin H.↗
A Neural Network Based Model for Reactive Heavy Hydrocarbon Potential Energy Surfaces.
Abstract not provided.
Yang, Yoona↗
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.
36 MATERIALS SCIENCE↗
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.
54 ENVIRONMENTAL SCIENCES↗
Intrinsic Layer-Dependent Surface Energy and Exfoliation Energy of van der Waals Materials
Publication data for paper
Chemistry↗