Spline-based neural network interatomic potentials: blending classical and machine learning models

no code implementations • 4 Oct 2023 • Joshua A. Vita, Dallas R. Trinkle

While machine learning (ML) interatomic potentials (IPs) are able to achieve accuracies nearing the level of noise inherent in the first-principles data to which they are trained, it remains to be shown if their increased complexities are strictly necessary for constructing high-quality IPs.

A variational principle for mass transport

no code implementations • 29 May 2018 • Dallas R. Trinkle

A variation principle for mass transport in solids is derived that recasts transport coefficients as minima of local thermodynamic average quantities.

Statistical Mechanics Materials Science

Automatic numerical evaluation of vacancy-mediated transport for arbitrary crystals: Onsager coefficients in the dilute limit using a Green function approach

no code implementations • 3 Aug 2016 • Dallas R. Trinkle

A general solution for vacancy-mediated diffusion in the dilute-vacancy/dilute-solute limit for arbitrary crystal structures is derived from the master equation.

Statistical Mechanics Materials Science Mathematical Physics Mathematical Physics

Diffusivity and derivatives for interstitial solutes: Activation energy, volume, and elastodiffusion tensors

no code implementations • 11 May 2016 • Dallas R. Trinkle

Computational atomic-scale methods continue to provide new information about geometry, energetics, and transition states for interstitial elements in crystalline lattices.

Materials Science