Amazing stuff!
"It is an everyday experience that liquids form stable surfaces without evaporating further. Observe a glass of water. It is evident that the water exists in two distinct phases: liquid and gas. Even at room temperature, water molecules are constantly evaporating from the surface of the liquid water and passing into the gas phase. At the same time, some of the water molecules from the gas condense back into the liquid. The transition from one phase to the other depends on the temperature and pressure. Above a critical temperature, the simultaneous coexistence of gas and liquid disappears. The resulting supercritical fluid no longer forms an interface, important for industrial processes such as separation, cleaning and production. Predicting precisely the pressure and temperature, i.e. the boiling point, at which this basic phase transition occurs provides a comprehensive picture of the underlying physics and thus a deep understanding of the wide range of accompanying phenomena that also play a role in industry."
From the abstract:
"We use supervised machine learning together with the concepts of classical density functional theory to investigate the effects of interparticle attraction on the pair structure, thermodynamics, bulk liquid-gas coexistence, and associated interfacial phenomena in many-body systems.
Local learning of the one-body direct correlation functional is based on Monte Carlo simulations of inhomogeneous systems with randomized thermodynamic conditions, randomized planar shapes of the external potential, and randomized box sizes.
Focusing on the prototypical Lennard-Jones system, we test predictions of the resulting neural attractive density functional across a broad spectrum of physical behavior associated with liquid-gas phase coexistence in bulk and at interfaces. We analyze the bulk radial distribution function đť‘”(đť‘ź) obtained from automatic differentiation and the Ornstein-Zernike route and determine
(i) the Fisher-Widom line, i.e., the crossover of the asymptotic (large distance) decay of đť‘”(đť‘ź) from monotonic to oscillatory,
(ii) the (Widom) line of maximal correlation length,
(iii) the line of maximal isothermal compressibility, and
(iv) the spinodal by calculating the poles of the structure factor in the complex plane.
The bulk binodal and the density profile of the free liquid-gas interface are obtained from density functional minimization and the corresponding surface tension from functional line integration. We also show that the neural functional describes accurately the phenomena of drying at a hard wall and of capillary evaporation for a liquid confined in a slit pore. Our neural framework yields results that improve significantly upon standard mean-field treatments of interparticle attraction. Comparison with independent simulation results demonstrates a consistent picture of phase separation even when restricting the training to supercritical states only. We argue that phase coexistence and its associated signatures can be discovered as emerging phenomena via functional mappings and educated extrapolation."
Bayreuth and Bristol physicists use AI to resolve a 150 year-old problem (original news release) "Combining concepts from statistical physics with machine learning, researchers ... have shown that highly accurate and efficient predictions can now be made as to whether a substance will be liquid or gaseous under given conditions. ..."
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