COOKIES: By using this website you agree that we can place Google Analytics Cookies on your device for performance monitoring. |

University of Cambridge > Talks.cam > Theory - Chemistry Research Interest Group > Second Harmonic Scattering: Atomistic Simulation and Machine Learning

## Second Harmonic Scattering: Atomistic Simulation and Machine LearningAdd to your list(s) Download to your calendar using vCal - Dr David M. Wilkins, Laboratory of Computational Science and Modelling, EPFL
- Wednesday 14 February 2018, 14:15-15:15
- Department of Chemistry, Cambridge, Unilever lecture theatre.
If you have a question about this talk, please contact Lisa Masters. Recent second-harmonic scattering (SHS) experiments on electrolyte solutions have shown evidence of long-ranged intermolecular correlations between solvent molecules, persisting on the ~10 nm length scale at sub-molar concentrations; SHS is extremely sensitive to these kinds of correlations, and an atomistic understanding of these complex experiments is desirable. I will discuss work in our group towards a computational framework for modelling SHS experiments of general condensed-phase systems. Firstly, I will show that the orientational correlations between molecules must be accounted for to fully model the SHS signal, at variance with common assumptions. Accurate computational modelling of SHS experiments depends also on an accurate calculation of molecular hyperpolarizabilities: this requires that we take into account the effects of environmental and (nuclear) quantum-mechanical fluctuations. The calculation of these response tensors is computationally very demanding, and I will describe a simple machine-learning model that can sidestep these calculations for systems in which a molecular axis system can be defined unambiguously. In general systems, this method will not work, and so I discuss finally a framework that we have recently developed allowing for the prediction of any kind of tensorial property by machine-learning, taking into account the covariance of these properties under a rigid rotation. This method, called symmetry-adapted Gaussian process regression (SA-GPR), has the potential, among many other applications, to allow the simulation of light-scattering and spectroscopic experiments on general condensed phase systems, without the cost of quantum chemical methods. This talk is part of the Theory - Chemistry Research Interest Group series. ## This talk is included in these lists:- All Talks (aka the CURE list)
- Department of Chemistry
- Department of Chemistry, Cambridge, Unilever lecture theatre
- Featured lists
- Lennard-Jones Centre
- School of Physical Sciences
- Theory - Chemistry Research Interest Group
Note that ex-directory lists are not shown. |
## Other listsInstitute of Astronomy Galaxies Discussion Group Gates Cambridge Annual Lecture 7 March 2017 Imagined Civities## Other talksStructured Deep Learning for Dialogue Management The Ambonese Rumphius and his inter-island information networks Cosmology and Astrophysics from CMB Measurements Is Demand Side Response a Womanâ€™s Work? Gender Dynamics Geophysical flows on Earth and Mars A sex-linked supergene controls sperm morphology and swimming speed in a songbird |