Neural Density Functional Theory of Liquid-Gas Phase Coexistence
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...
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| Format: | Article |
| Language: | English |
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American Physical Society
2025-01-01
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| Series: | Physical Review X |
| Online Access: | http://doi.org/10.1103/PhysRevX.15.011013 |
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| _version_ | 1832587239136165888 |
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| author | Florian Sammüller Matthias Schmidt Robert Evans |
| author_facet | Florian Sammüller Matthias Schmidt Robert Evans |
| author_sort | Florian Sammüller |
| collection | DOAJ |
| description | 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 g(r) 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 g(r) 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. |
| format | Article |
| id | doaj-art-aa8a9253ec3b4746a0edff2166ab58bc |
| institution | Kabale University |
| issn | 2160-3308 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | American Physical Society |
| record_format | Article |
| series | Physical Review X |
| spelling | doaj-art-aa8a9253ec3b4746a0edff2166ab58bc2025-01-24T15:01:50ZengAmerican Physical SocietyPhysical Review X2160-33082025-01-0115101101310.1103/PhysRevX.15.011013Neural Density Functional Theory of Liquid-Gas Phase CoexistenceFlorian SammüllerMatthias SchmidtRobert EvansWe 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 g(r) 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 g(r) 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.http://doi.org/10.1103/PhysRevX.15.011013 |
| spellingShingle | Florian Sammüller Matthias Schmidt Robert Evans Neural Density Functional Theory of Liquid-Gas Phase Coexistence Physical Review X |
| title | Neural Density Functional Theory of Liquid-Gas Phase Coexistence |
| title_full | Neural Density Functional Theory of Liquid-Gas Phase Coexistence |
| title_fullStr | Neural Density Functional Theory of Liquid-Gas Phase Coexistence |
| title_full_unstemmed | Neural Density Functional Theory of Liquid-Gas Phase Coexistence |
| title_short | Neural Density Functional Theory of Liquid-Gas Phase Coexistence |
| title_sort | neural density functional theory of liquid gas phase coexistence |
| url | http://doi.org/10.1103/PhysRevX.15.011013 |
| work_keys_str_mv | AT floriansammuller neuraldensityfunctionaltheoryofliquidgasphasecoexistence AT matthiasschmidt neuraldensityfunctionaltheoryofliquidgasphasecoexistence AT robertevans neuraldensityfunctionaltheoryofliquidgasphasecoexistence |