Accurate interatomic force fields via machine learning with covariant kernels
Not Available
Glielmo, Aldo↗
Engineering topics
De Vita, Alessandro
Publications and source records attributed to De Vita, Alessandro.
Low Speed Crack Propagation via Kink Formation and Advance on the Silicon (110) Cleavage Plane
Not Available
Kermode, James R.↗
Molecular Dynamics with On-the-Fly Machine Learning of Quantum-Mechanical Forces
Not Available
Li, Zhenwei↗
Accuracy of buffered-force QM/MM simulations of silica
We report comparisons between energy-based quantum mechanics/molecular mechanics (QM/MM) and buffered force-based QM/MM simulations in silica. Local quantities—such as density of states, charges, forces, and geometries—calculated with both QM/MM approaches are compared to the results of full QM simulations. We find the length scale over which forces computed using a finite QM region converge to reference values obtained in full quantum-mechanical calculations is ∼10 Å rather than the ∼5 Å previously reported for covalent materials such as silicon. Electrostatic embedding of the QM region in the surrounding classical point charges gives only a minor contribution to the force convergence. While the energy-based approach provides accurate results in geometry optimizations of point defects, we find that the removal of large force errors at the QM/MM boundary provided by the buffered force-based scheme is necessary for accurate constrained geometry optimizations where Si–O bonds are elongated and for finite-temperature molecular dynamics simulations of crack propagation. Moreover, the buffered approach allows for more flexibility, since special-purpose QM/MM coupling terms that link QM and MM atoms are not required and the region that is treated at the QM level can be adaptively redefined during the course of a dynamical simulation.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗