Chemical Physics seminars
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November 2026
Water’s rapidly changing hydrogen-bond network links molecular interactions to its unusual bulk properties. The talk introduces correlated vibrational spectroscopy (CVS), which combines higher-order optical measurements, symmetry selection and angular polarimetry to isolate signals from interacting molecules and directly examine low-frequency hydrogen-bond stretching. CVS measurements reveal electronic charge transfer and nuclear quantum effects in water and acidic or basic solutions. Hydroxide donates roughly 8% more negative charge to the network than water, while protons reduce transfer by less than 4%. Nuclear quantum effects weaken hydrogen bonds in pure water by about 10% and change charge transfer in acidic, but not basic, solutions. In acids, charge is accepted locally by a proton and reduced by its delocalization. In bases, donation extends across several hydration shells, beyond the region affected by proton delocalization. The presentation explains how these measurements probe intermolecular correlations and concludes with further applications of CVS.
Wed, Nov 18 · 15:00 UTC · Online
Recent recordings
2 recorded talksInertial active soft matter
Hartmut Löwen· universität düsseldorf
Active particles which are self-propelled by converting energy into mechanical motion represent an expanding research realm in physics and chemistry. For micron-sized particles moving in a liquid (``microswimmers''), most of the basic features have been described by using the model of overdamped active Brownian motion [1]. However, for macroscopic particles or microparticles moving in a gas, inertial effects become relevant such that the dynamics is underdamped. Therefore, recently, active particles with inertia have been described by extending the active Brownian motion model to active Langevin dynamics which include inertia [2]. In this talk, recent developments of active particles with inertia (``microflyers'', ``hoppers'' or ``runners'') are summarized including: inertial delay effects between particle velocity and self-propulsion direction [3], tuning of the long-time self-diffusion by the moment of inertia [3], the influence of inertia on motility-induced phase separation and the cluster growth exponent [4], and the formation of active micelles (“rotelles”) by using inertial active surfactants. References [1] C. Bechinger, R. di Leonardo, H. Löwen, C. Reichhardt, G. Volpe, G. Volpe, Reviews of Modern Physics 88, 045006 (2016). [2] H. Löwen, Journal of Chemical Physics 152, 040901 (2020). [3] C. Scholz, S. Jahanshahi, A. Ldov, H. Löwen, Nature Communications 9, 5156 (2018). [4] S. Mandal, B. Liebchen, H. Löwen, Physical Review Letters 123, 228001 (2019). [5] C. Scholz, A. Ldov, T. Pöschel, M. Engel, H. Löwen, Surfactants and rotelles in active chiral fluids, will be published
Recorded
Neural network-like collective dynamics in molecules
Arvind Murugan· University of Chicago
Neural networks can learn and recognize subtle correlations in high dimensional inputs. However, neural networks are simply many-body systems with strong non-linearities and disordered interactions. Hence, many-body physical systems with similar interactions should be able to show neural network-like behavior. Here we show neural network-like behavior in the nucleation dynamics of promiscuously interacting molecules with multiple stable crystalline phases. Using a combination of theory and experiments, we show how the physics of the system dictates relationships between the difficulty of the pattern recognition task solved, time taken and accuracy. This work shows that high dimensional pattern recognition and learning are not special to software algorithms but can be achieved by the collective dynamics of sufficiently disordered molecular systems.
Recorded
Video · 1 h 22 minCondensed Matter PhysicsStatistical PhysicsSeries: Imperial College Physics of Life Network Seminars