Preconditioners
Presentations
14/03/2018, Cambridge, Invenia
28/06/2018, London, United Kingdom Car-Parrinello Consortium (UKCP)
Articles
Preconditioners for the geometry optimisation and saddle point search of molecular systemsL Mones, C Ortner and G Csanyi
2018, Scientific reports 8 (1), 1-11
Abstract: A class of preconditioners is introduced to enhance geometry optimisation and transition state search of molecular systems. We start from the Hessian of molecular mechanical terms, decompose it and retain only its positive definite part to construct a sparse preconditioner matrix. The construction requires only the computation of the gradient of the corresponding molecular mechanical terms that are already available in popular force field software packages. For molecular crystals, the preconditioner can be combined straightforwardly with the exponential preconditioner recently introduced for periodic systems. The efficiency is demonstrated on several systems using empirical, semiempirical and ab initio potential energy surfaces.
Some remarks on preconditioning molecular dynamics
H AlRachid, L Mones and C Ortner
2018, The SMAI journal of computational mathematics 4, 57-80
Abstract: We consider a Preconditioned Overdamped Langevin algorithm that does not alter the invariant distribution (up to controllable discretisation errors) and ask whether preconditioning improves the standard model in terms of reducing the asymptotic variance and of accelerating convergence to equilibrium. We present a detailed study of the dependence of the asymptotic variance on preconditioning in some elementary toy models related to molecular simulation. Our theoretical results are supported by numerical simulations.
A universal preconditioner for simulating condensed phase materials
D Packwood, J Kermode, L Mones, N Bernstein, J Woolley, N Gould, C Ortner, G Csanyi
2016, The Journal of Chemical Physics 144 (16), 164109
Abstract: We introduce a universal sparse preconditioner that accelerates geometry optimisation and saddle point search tasks that are common in the atomic scale simulation of materials. Our preconditioner is based on the neighbourhood structure and we demonstrate the gain in computational efficiency in a wide range of materials that include metals, insulators, and molecular solids. The simple structure of the preconditioner means that the gains can be realised in practice not only when using expensive electronic structure models but also for fast empirical potentials. Even for relatively small systems of a few hundred atoms, we observe speedups of a factor of two or more, and the gain grows with system size. An open source Python implementation within the Atomic Simulation Environment is available, offering interfaces to a wide range of atomistic codes.