Deep learning and self-consistent field theory: a path towards accelerating polymer phase discovery

DOI10.1016/J.JCP.2021.110519OpenAlexW3171227572MaRDI QIDQ2132590

Hector D. Ceniceros, Glenn H. Fredrickson, Yao Xuan, Kris T. Delaney

Publication date: 28 April 2022

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jcp.2021.110519

zbMATH Keywords

Sobolev spaceself-consistent field theorymachine learningglobal shift-invariancesaddle density fields

Mathematics Subject Classification ID

Numerical methods for partial differential equations, initial value and time-dependent initial-boundary value problems (65Mxx) Circuits, networks (94Cxx) Applications of statistical mechanics to specific types of physical systems (82Dxx)

Cites Work

• Unnamed Item
• Efficient order-adaptive methods for polymer self-consistent field theory
• Numerical Solution of Polymer Self-Consistent Field Theory
• Approximation by superpositions of a sigmoidal function

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