Symbolic Machine Learning for Vapor-Liquid Equilibrium Prediction in Cx-N2 Binary Mixtures

arXiv:2608.11255 · cs.AI, cs.LG, physics.comp-ph · Submitted 2026-08-06 · Read on arXiv

Bongseok Kim, Suman Chakraborty, Gary Huang, Mehek Mathur, Guang Lin, Li Qiao

cs.AI, cs.LG, physics.comp-ph

Submitted: 2026-08-06

Updated: 2026-08-13

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Composition — 52× in this paper · explained in 2 episodes
• Symbolic Regression — 45× in this paper · explained in 6 episodes
• Accuracy — 17× in this paper · explained in 1 episode
• Interpretability — 5× in this paper · explained in 15 episodes
• Generalization — 3× in this paper · explained in 14 episodes
• Evolutionary Search — 2× in this paper · explained in 3 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Editor — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Hybrid Model — 1× in this paper · explained in 2 episodes
• Parameterization — 1× in this paper · explained in 2 episodes

Sources

• Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl

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