Dr Deidre M. Cleland1, Miss Emily Fletcher1, Dr Manolo C. Per1
1Data61 CSIRO, Docklands, Australia
The properties and behaviour of biologically important molecules are highly dependent on the correlated behaviour of their constituent electrons. An accurate description of these quantum effects is therefore crucial for reliable molecular simulations. However, computational modelling of electron correlation is very computationally demanding. To take full advantage of existing and future computational resources, we require accurate modelling methods that will scale efficiently to enormous numbers of computing cores. The most practical way to achieve this is through stochastic techniques.
Method and Results
Quantum Monte Carlo (QMC) methods are a set of stochastic electronic structure techniques that have been shown to accurately describe a range of molecular systems, with near perfect scaling to thousands of high performance computing cores. However, the computational cost of calculations can still be significant. In this poster, we describe the development of the QMC algorithm towards a version that can be applied to larger biological systems, with a particular focus on DNA, and the goal of modelling the series of base pairs that make up a twist of the double helix structure. For this, the QMC algorithm is modified to promote size consistency, allowing energy differences to be calculated with substantially reduced computational cost. Additional developments focus on ensuring the algorithm scales efficiently to even larger number of parallel processors, with the goal of simulating large molecules in the cloud.
Through algorithmic developments to promote size consistency, and greater efficiency for parallelisability, QMC is developed for accurate modelling of biologically important molecules.
Dr Deidre Cleland is a Research Scientist in the Molecular and Materials Modelling group at Data61. Dr Cleland was awarded her PhD in 2012, for development of the initiator Full Configuration Interaction Quantum Monte Carlo method, in collaboration with her colleagues in the Alavi research group at the University of Cambridge.
Upon completion of her PhD, Dr Cleland spent 18 months working as a Data Analyst in London before joining CSIRO in 2014. Her research at Data61 is primarily focused on cheminformatics and Quantum Monte Carlo methods for accurate quantum-scale simulations of molecules.