A strategy is outlined to reduce the number of training points required to model intermolecular potentials using Gaussian processes, without reducing accuracy. An asymptotic function is used at long range and the cross-over distance between this model and the Gaussian process is learnt from the training data. Results are presented for different implementations of this procedure, known as boundary optimisation, across the following dimer systems: CO-Ne, HF-Ne, HF-Na + , CO 2 -Ne and (CO 2 ) 2 . The technique reduces the number of training points, at fixed accuracy, by up to ∼ 49 %, compared to our previous work based on a sequential learning technique. The approach is readily transferable to other statistical methods of prediction or modelling problems.
A strategy is presented to implement Gaussian process potentials in molecular simulations through parallel programming. Attention is focused on the three-body nonadditive energy, though all algorithms extend straightforwardly to the additive energy. The method to distribute pairs and triplets between processes is general to all potentials. Results are presented for a simulation box of argon, including full box and atom displacement calculations, which are relevant to Monte Carlo simulation. Data on speed-up are presented for up to 120 processes across four nodes. A 4-fold speed-up is observed over five processes, extending to 20-fold over 40 processes and 30-fold over 120 processes.
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