05.25.2021 - Matteo Salvalaglio: Sampling, detecting, and analysing collective arrangements in atomistic simulations
10 May 2021





Dr Matteo Salvalaglio, Thomas Young Centre and Department of Chemical Engineering, University College London.

Host: Alin Marin Elena, Computational Chemistry, SCD

​Tuesday 25 May at 14:00-15:00

This seminar will take place via Zoom webinar – Registration required https://www.ccp5.ac.uk/semreg


Sampling, detecting, and analysing collective arrangements in atomistic simulations.

The synthesis of crystalline materials by precipitation from solution is a phenomenon at the heart of many technological and natural processes, ranging from biomineralisation to the production of active pharmaceutical ingredients. The properties of macroscopic crystals are inherently determined by their structure and morphology, two products of the atomistic-scale arrangement of building blocks emerging and propagating during crystal nucleation and growth. Molecular dynamics simulations offer a direct insight into the fundamental processes underpinning the assembly of building blocks (atoms or molecules) and into the stability of the structures emerging from such assembly. However, to exploit the potential of atomistic simulations, it is critical to overcome the timescale limitations associated with rare events, perform simulations in conditions resembling macroscopic systems (i.e. constant driving force), and systematically identify the structure of assemblies emerging from simulation.

In this seminar, I will discuss methods to sample, detect, and analyse the collective arrangement of crystal building blocks based on the definition of collective variables and on the application of clustering algorithms. To this aim, I will illustrate two applications, both featuring prominent use of PLUMED [Tribello et al. Computer Physics Communications 185 (2), 604-613, 2014], including i. the characterisation of dense liquid-like clusters at graphite-NaCl(aq) interfaces [Finney et al. arXiv 2021, https://arxiv.org/abs/2104.11773], and ii.  the systematic application of enhanced sampling and clustering algorithms to improve the prediction of polymorphism in molecular crystals [Francia et al. Crystal Growth and Design, 2020, https://pubs.acs.org/doi/abs/10.1021/acs.cgd.0c00918].

Registration required, and please help us by filling in the optional survey.


Contact: Lomas, Georgia (STFC,DL,SC)