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Results for “intermolecular forces”

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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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1967

The nature of intermolecular forces.

Intermolecular forces theory, considering hydrogen atom interaction through Born- Oppenheimer approximation and variational calculations

ATOMIC COLLISION↗
NASA NTRS1966

Long-range /retarded/ intermolecular forces.

Retarded intermolecular forces, discussing Casimir and Polder retarded dipole-dipole energy of interaction between two ground state atoms in terms of trigonometric integrals

ENERGY EXCHANGE↗
NASA NTRS1992

Analysis of hypersonic nozzles including vibrational nonequilibrium and intermolecular force effects

A computational fluid dynamics algorithm is developed for the study of high-pressure axisymmetric hypersonic nozzle flows. The effects of intermolecular forces and vibrational nonequilibrium are included in the analysis. The numerical simulation of gases with an arbitrary equation of state is discussed. Simulations for a high pressure nozzle (p(0) = 138 MPa) demonstrate that both intermolecular forces and vibrational nonequilibrium have a significant affect on the flow. These nonideal effects tend to increase the Mach number at the nozzle exit plane. Thus, they must be included in the design and analysis of high pressure hypersonic nozzles.

Canupp, Patrick W.↗
DOE OSTI2021

Learning intermolecular forces at liquid–vapor interfaces

By adopting a perspective informed by contemporary liquid-state theory, we consider how to train an artificial neural network potential to describe inhomogeneous, disordered systems. Here, we find that neural network potentials based on local representations of atomic environments are capable of describing some properties of liquid-vapor interfaces but typically fail for properties that depend on unbalanced long-ranged interactions that build up in the presence of broken translation symmetry. These same interactions cancel in the translationally invariant bulk, allowing local neural network potentials to describe bulk properties correctly. By incorporating explicit models of the slowly varying long-ranged interactions and training neural networks only on the short-ranged components, we can arrive at potentials that robustly recover interfacial properties. We find that local neural network models can sometimes approximate a local molecular field potential to correct for the truncated interactions, but this behavior is variable and hard to learn. Generally, we find that models with explicit electrostatics are easier to train and have higher accuracy. We demonstrate this perspective in a simple model of an asymmetric dipolar fluid, where the exact long-ranged interaction is known, and in an ab initio water model, where it is approximated.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗