Physics-informed machine learning of the correlation functions in bulk fluids
Wenqian Chen, Peiyuan Gao, Panos Stinis
Abstract
The Ornstein–Zernike (OZ) equation is the fundamental equation for pair correlation function computations in the modern integral equation theory for liquids. In this work, machine learning models, notably physics-informed neural networks and physics-informed neural operator networks, are explored to solve the OZ equation. The physics-informed machine learning models demonstrate great accuracy and high efficiency in solving the forward and inverse OZ problems of various bulk fluids. The results highlight the significant potential of physics-informed machine learning for applications in thermodynamic state theory.
Topics & Concepts
PhysicsOrnstein–Zernike equationArtificial neural networkStatistical physicsEquation of stateOperator (biology)Artificial intelligenceIntegral equationPhysics educationFunction (biology)ComputationInverse temperatureApplied mathematicsMachine learningQuantum mechanicsAlgorithmMathematical analysisComputer scienceThermodynamicsMathematicsGeneRepressorChemistryBiologyTranscription factorBiochemistryEvolutionary biologyModel Reduction and Neural NetworksQuantum, superfluid, helium dynamicsNanofluid Flow and Heat Transfer