Search

The interfacial behaviour of water remains a central question to fields as diverse as protein folding, friction and ice formation. While the properties of water at interfaces differ from those in the bulk, major gaps in our knowledge limit our understanding at the molecular level. Information concerning the microscopic motion of water comes mostly from computation and, on an atomic scale, is largely unexplored by experiment. Here, we provide a detailed insight into the behaviour of water monomers on a graphene surface. The motion displays remarkably strong signatures of cooperative behaviour due to repulsive forces between the monomers, enhancing the monomer lifetime ( ≈ 3 s at 125 K) in a free-gas phase that precedes the nucleation of ice islands and, in turn, provides the opportunity for our experiments to be performed. Our results give a molecular perspective on a kinetic barrier to ice nucleation, providing routes to understand and control the processes involved in ice formation., The dynamics of water molecules at interfaces controls natural and artificial processes, but experimental investigations have been challenging. Here the authors investigate water molecules on a graphene surface using helium spin-echo spectroscopy, and reveal a regime where freely mobile molecules undergo strong repulsive mutual interactions which inhibit ice nucleation.

Funder: Emmanuel College Avik Chakravarty Memorial Fund for Physics, Funder: AWE Defence Ordnance Safety Group, Science and Technology, The sodium D-line is often present in optical spectra of combustion due to its high prevalence and emissivity. Collision theory predicts the spectral peak to have a red-shift dependent on pressure, P, and temperature, T. Here we show that the conditions reached during deflagration of octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine (HMX) permit the red-shift of the sodium D-line to be calibrated to 1.5 GPa. Deflagration at these pressures is achieved using a split Hopkinson pressure bar apparatus, with temperatures of circa 2900 K from the greybody continuum away from spectral features. Lower deflagration pressures, of 0.5 to 0.9 GPa, are achieved in a fallhammer test, with temperatures of circa 4000 K. The red-shift exhibits the predicted PT−0.7 dependence with a constant of proportionality of (950 ± 30) GPa-1 · K0.7 · nm. Using the serendipitous presence of sodium, this optical technique allows for fast measurements of both pressure and temperature from the same light source in one measurement.

Chemical compound space refers to the vast set of all possible chemical compounds, estimated to contain 1060 molecules. While intractable as a whole, modern machine learning (ML) is increasingly capable of accurately predicting molecular properties in important subsets. Here, we therefore engage in the ML-driven study of even larger reaction space. Central to chemistry as a science of transformations, this space contains all possible chemical reactions. As an important basis for ‘reactive’ ML, we establish a first-principles database (Rad-6) containing closed and open-shell organic molecules, along with an associated database of chemical reaction energies (Rad-6-RE). We show that the special topology of reaction spaces, with central hub molecules involved in multiple reactions, requires a modification of existing compound space ML-concepts. Showcased by the application to methane combustion, we demonstrate that the learned reaction energies offer a non-empirical route to rationally extract reduced reaction networks for detailed microkinetic analyses., Application of machine-learning approaches to exploring chemical reaction networks is challenging due to need of including open-shell reaction intermediates. Here the authors introduce a density functional theory database of closed and open-shell molecules for machine-learning predictions of reaction energies.

Water molecules can arrange into a liquid with complex hydrogen-bond networks and at least 17 experimentally confirmed ice phases with enormous structural diversity. It remains a puzzle how or whether this multitude of arrangements in different phases of water are related. Here we investigate the structural similarities between liquid water and a comprehensive set of 54 ice phases in simulations, by directly comparing their local environments using general atomic descriptors, and also by demonstrating that a machine-learning potential trained on liquid water alone can predict the densities, lattice energies, and vibrational properties of the ices. The finding that the local environments characterising the different ice phases are found in water sheds light on the phase behavior of water, and rationalizes the transferability of water models between different phases., Molecular understanding of water is challenging due to the structural complexity of liquid water and the large number of ice phases. Here the authors use a machine-learning potential trained on liquid water to demonstrate the structural similarity of liquid water and that of 54 real and hypothetical ice phases.

The interfacial behaviour of water remains a central question to fields as diverse as protein folding, friction and ice formation. While the properties of water at interfaces differ from those in the bulk, major gaps in our knowledge limit our understanding at the molecular level. Information concerning the microscopic motion of water comes mostly from computation and, on an atomic scale, is largely unexplored by experiment. Here, we provide a detailed insight into the behaviour of water monomers on a graphene surface. The motion displays remarkably strong signatures of cooperative behaviour due to repulsive forces between the monomers, enhancing the monomer lifetime ( ��� 3 s at 125 K) in a free-gas phase that precedes the nucleation of ice islands and, in turn, provides the opportunity for our experiments to be performed. Our results give a molecular perspective on a kinetic barrier to ice nucleation, providing routes to understand and control the processes involved in ice formation.

The interfacial behaviour of water remains a central question to fields as diverse as protein folding, friction and ice formation. While the properties of water at interfaces differ from those in the bulk, major gaps in our knowledge limit our understanding at the molecular level. Information concerning the microscopic motion of water comes mostly from computation and, on an atomic scale, is largely unexplored by experiment. Here, we provide a detailed insight into the behaviour of water monomers on a graphene surface. The motion displays remarkably strong signatures of cooperative behaviour due to repulsive forces between the monomers, enhancing the monomer lifetime ( ≈ 3 s at 125 K) in a free-gas phase that precedes the nucleation of ice islands and, in turn, provides the opportunity for our experiments to be performed. Our results give a molecular perspective on a kinetic barrier to ice nucleation, providing routes to understand and control the processes involved in ice formation.