Work

Accelerating multicomponent phase-coexistence calculations with physics-informed neural networks

Year: 2024

Type: article

Abstract: Phase separation in multicomponent mixtures is of significant interest in both fundamental research and technology. Although the thermodynamic principles governing phase equilibria are straightforward, practical determination of equilibrium phases and constituent compositions for multicomponent systems is often laborious and computationally intensive. Here, we present a machine-learning workflow that simplifies and accelerates phase-coexistence calculations. We specifically analyze capabilities of neural networks to predict the number, composition, and relative abundance of equilibrium phases of systems described by Flory-Huggins theory. We find that incorporating physics-informed material constraints into the neural network architecture enhances the prediction of equilibrium compositions compared to standard neural networks with minor errors along the boundaries of the stable region. However, introducing additional physics-informed losses does not lead to significant further improvement. These errors can be virtually eliminated by using machine-learning predictions as a warm-start for a subsequent optimization routine. This work provides a promising pathway to efficiently characterize multicomponent phase coexistence.

Source: Molecular Systems Design & Engineering

DOI: 10.1039/d4me00168k

Authors: Satyen Dhamankar, Shengli Jiang, Michael A. Webb

Institution: Princeton University

Language: English

Topic: Phase Equilibria and Thermodynamics

Subfield: Biomedical Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

Funders: National Science Foundation, Princeton University, Division of Materials Research

Awards: CAREER: Multiscale Simulation and Machine Learning for Smart Polymer Design, G1757009270, G3221136969, Collaborative Research: DMREF: Machine Learning and Robotics for the Data-Driven Design of Protein-polymer Hybrid Materials, Equipment: MRI: Track 1 Acquisition of a GPU-Accelerated Computing Cluster for Advanced Optimization and Design in Multidisciplinary Research and Education