Screw dislocation structure and mobility in body centered cubic Fe predicted by a Gaussian Approximation Potential

Abstract

The plastic flow behavior of bcc transition metals up to moderate temperatures is dominated by the thermally activated glide of screw dislocations, which in turn is determined by the atomic-scale screw dislocation core structure and the associated kink-pair nucleation mechanism for glide. Modeling complex plasticity phenomena requires the simulation of many atoms and interacting dislocations and defects. These sizes are beyond the scope of first-principles methods and thus require empirical interatomic potentials. Especially for the technological important case of bcc Fe, existing empirical interatomic potentials yield spurious behavior. Here, the structure and motion of the screw dislocations in Fe are studied using a new Gaussian Approximation Potential (GAP) for bcc Fe, which has been shown to reproduce the potential energy surface predicted by density-functional theory (DFT) and many associated properties. The Fe GAP predicts a compact, non-degenerate core structure, a single-hump Peierls potential, and glide on {110}, consistent with DFT results. The thermally activated motion at finite temperatures occurs by the expected kink-pair nucleation and propagation mechanism. The stress-dependent enthalpy barrier for screw motion, computed using the nudgedelastic-band method, follows closely a form predicted by standard theories with a zero-stress barrier of~1 eV, close to the experimental value of 0.84 eV, and a Peierls stress of~2 GPa consistent with DFT predictions of the Peierls potential.

De novo exploration and self-guided learning of potential-energy surfaces

“…Once generated, ML potentials enable accurate simulations that are orders of magnitude faster than the reference method. They can solve challenging structural problems, as has been demonstrated for the atomic-scale deposition and growth of amorphous carbon films 13 , for proton-transfer mechanisms 14 or dislocations in materials 15,16 , involving thousands of atoms in the simulation. More recently, it was shown that ML potentials can be suitable tools for global structure searches targeting crystalline phases [17][18][19][20] , clusters [21][22][23][24] , and nanostructures 25 .…”

A machine learning based intramolecular potential for a flexible organic molecule

“…We now briey outline the Gaussian Approximation Potential (GAP) 47 framework, and how we apply it to create a potential energy surface for 3BPA that reproduces quantum mechanical energies to within 1.0 kcal mol À1 root mean square (RMS) error. GAP has been applied to many different materials and compounds, [53][54][55][56][57][58][59][60][61][62][63] and has been described in detail elsewhere, 64,65 and so we summarize here only the main features. Although the probabilistic and linear regression viewpoints are entirely equivalent, we follow the latter here because it is likely to be more familiar, and we will not be making use of the uncertainty estimates and parameter optimization techniques that follow naturally from the former.…”

A Machine Learning Based Intramolecular Potential for a Flexible Organic Molecule

“…We now briefly outline the Gaussian Approximation Potential (GAP) 46 framework, and how we apply it to create a potential energy surface for 3BPA that reproduces quantum mechanical energies to within 1.0 kcal mol −1 root mean square (RMS) error. GAP has been applied to many different materials and compounds, [52][53][54][55][56][57][58][59][60][61][62] and has been described in detail elsewhere, 63,64 and so we summarize here only the main features. Although the probabilistic and linear regression viewpoints are entirely equivalent, we follow the latter here because it is likely to be more familiar, and we will not be making use of the uncertainty estimates and parameter optimization techniques that follow naturally from the former.…”