Minimum energy path calculations with Gaussian process regression
This work addresses computational bottlenecks in materials science and chemistry simulations, offering an incremental improvement over existing nudged elastic band methods.
The paper tackled the problem of reducing computational effort in minimum energy path calculations for atomic rearrangements by using Gaussian process regression, achieving a reduction in energy evaluations to less than a fifth for a test case involving a heptamer island on a crystal surface.
The calculation of minimum energy paths for transitions such as atomic and/or spin re-arrangements is an important task in many contexts and can often be used to determine the mechanism and rate of transitions. An important challenge is to reduce the computational effort in such calculations, especially when ab initio or electron density functional calculations are used to evaluate the energy since they can require large computational effort. Gaussian process regression is used here to reduce significantly the number of energy evaluations needed to find minimum energy paths of atomic rearrangements. By using results of previous calculations to construct an approximate energy surface and then converge to the minimum energy path on that surface in each Gaussian process iteration, the number of energy evaluations is reduced significantly as compared with regular nudged elastic band calculations. For a test problem involving rearrangements of a heptamer island on a crystal surface, the number of energy evaluations is reduced to less than a fifth. The scaling of the computational effort with the number of degrees of freedom as well as various possible further improvements to this approach are discussed.