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Lionel M. Raff

Researcher at Oklahoma State University–Stillwater

Publications -  176
Citations -  4778

Lionel M. Raff is an academic researcher from Oklahoma State University–Stillwater. The author has contributed to research in topics: Ab initio & Monte Carlo method. The author has an hindex of 36, co-authored 176 publications receiving 4583 citations. Previous affiliations of Lionel M. Raff include University of Illinois at Urbana–Champaign.

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Molecular dynamics (MD) simulation of uniaxial tension of some single-crystal cubic metals at nanolevel

TL;DR: In this paper, the authors performed simulations of the tensile deformation of cubic cubic metals and found that the strain to fracture is lower with the BCC materials than the FCC materials and that the radius of the neck increases with an increase in the deformation and decreases as the ductility of the material decreases.
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M.D. Simulation of nanometric cutting of single crystal aluminum–effect of crystal orientation and direction of cutting

TL;DR: In this paper, the deformation in the work material (underneath the depth of cut region) was found to be along the cutting direction and the dislocations were relieved from the uncut region into the material underneath by elastic recovery.
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MD simulation of indentation and scratching of single crystal aluminum

TL;DR: Komanduri et al. as discussed by the authors investigated the anisotropy in hardness and friction coefficient of single crystal aluminum in various crystal orientations and directions of scratching, and found that the hardness is increased significantly as the indentation depth is reduced to atomic dimensions.
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Molecular dynamics simulation of atomic-scale friction

TL;DR: In this article, a simulation of nanoindentation followed by nanoscratching was conducted on a single crystal aluminum, with the crystal set up in the (001) [100] orientation and scratching performed in the [100]- direction.
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Ab initio potential-energy surfaces for complex, multichannel systems using modified novelty sampling and feedforward neural networks

TL;DR: A neural network/trajectory approach is presented for the development of accurate potential-energy hypersurfaces that can be utilized to conduct ab initio molecular dynamics and Monte Carlo studies of gas-phase chemical reactions, nanometric cutting, and nanotribology, and of a variety of mechanical properties of importance in potential microelectromechanical systems applications.