Preconditioners for Large-Scale Atomistic Simulations

Lead Research Organisation: STFC - Laboratories
Department Name: Scientific Computing Department


Atomistic simulations are an indispensible tool of modern materials science, solid state physics and chemistry, as they allow scientists to study individual atoms and molecules in a way that is impossible in laboratory experiments. Understanding atomistic processes opens up avenues for the manipulation of matter at the atomic scale in order to achieve superior material properties (e.g., electrical, chemical, mechanical, etc.) for applications in science, engineering, and technology. This proposal focuses on the development of efficient and robust numerical algorithms for large-scale atomistic simulations.

The main bottleneck in current state of the art algorithms are preconditioners. In the context of this research preconditioners can be understood as operators transforming the space of atomistic configurations in order to give it "better" properties that enable the formulation of more efficient and more reliable computational algorithms. The state of the art molecular modelling software uses general purpose preconditioners that are not specifically targeted at large-scale atomistic systems, and are not particularly effective.

We propose to combine the wide-ranging complementary expertise of the PIs in molecular modelling, numerical optimisation, analysis and numerics of differential equations, and multiscale modelling, to construct novel preconditioners targeted specifically for interatomic potentials used in materials science applications that will achieve significant improvements in efficiency and reliability of state of the art methods.

Similar challenges arise also in phase space sampling techniques such as Markov Chain Monte Carlo methods, Hybrid Monte Carlo methods, or in transition state search. We will modify existing algorithms to take advantage of the hessian information provided by the preconditioners we will develop.

The new algorithms we will develop will enable scientists to study more complex systems and obtain more reliable results from simulations. The applications of the project are primarily in academic and industrial materials science. Our ambitious aim is to apply the new algorithms to the study of nano-particles consisting of hundreds of atoms.


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Description We have developed a general method for approximating complicated
atomistic structures with the aim of speeding up computations by
replacing the "real" problem by a sufficiently good "approximation".
Exploitation Route The methods we have developed have been implemented in open source software, and there are "hooks" into many application programs.
Thus users of these will be all be able to benefit from our ideas.
Sectors Chemicals,Education,Electronics,Energy,Environment,Manufacturing, including Industrial Biotechology

Description Too early to say, but as atomistic calculations are at the core of much scientific & engineering design,and as we provide software, our hopes are high!
First Year Of Impact 2016
Title Atomic Simulation Environment (ASE) 
Description The Atomic Simulation Environment (ASE) is a set of tools and Python modules for setting up, manipulating, running, visualizing and analyzing atomistic simulations 
Type Of Technology Software 
Year Produced 2016 
Impact Interfaces are provided to a variety of external atomistic codes, such as VASP, CASTEP, CP2K and LAMMPS 
Description Fortran software for the minimization of various potential energy models for large-scale atomistic simulation. The software is at beta status at the moment; users and developers of QUIP have access, and have reported significant improvements in performance relative to the embedded QUIP optimization algorithm 
Type Of Technology Software 
Year Produced 2013 
Impact Please refer to physicist users of QUIP.