Equations of state cannot be entirely replaced by machine learning models due to the lack of guaranteed thermodynamic consistency and extrapolation limits.
the verdict
CONTESTED
the evidence cuts both ways
confidence 35/100
While purely data-driven machine learning models often struggle with thermodynamic consistency and extrapolation limits, emerging physics-informed and hybrid modeling frameworks are increasingly able to incorporate equations of state and physical laws directly into neural networks.
The evidence we hold leans leans supported
How this was weighed
official record 3x · fact-check 2x · hedged 1x · crowd & reference 1x
Physics-informed deep learning for molecular solubility pred · peer-reviewed · supports · weight 1 · 2026
Interpretable and extrapolation-stable model for predicting · peer-reviewed · supports · weight 1 · 2026
Physics-informed neural network modeling of shock waves by a · peer-reviewed · supports · weight 1 · 2026
AI for atmosphere-ocean sciences: advancements, challenges a · peer-reviewed · supports · weight 1 · 2026
Explainable Deep Learning for Research on the Synergistic Me · peer-reviewed · supports · weight 1 · 2026
Development of a Rapid Deep-Learning-Assisted Multiphase Mul · peer-reviewed · refutes · weight 1 · 2026
Integrating experimental data and machine learning models fo · peer-reviewed · refutes · weight 1 · 2025
Evidence for · 5
Physics-informed deep learning for molecular solubility prediction: integrating thermodynamic constraints with neural network architectures.
2026 · cited by 0
Paper 1 notes that standard machine learning models often violate basic thermodynamic principles, highlighting the need for physics-informed constraints to ensure thermodynamic consistency.
Evidence against · 2
Development of a Rapid Deep-Learning-Assisted Multiphase Multicomponent Numerical Simulation Protocol.
2026 · cited by 0
Paper 2 demonstrates that a deep-learning-based workflow can successfully replace iterative flash calculations in compositional simulations while maintaining thermodynamic consistency.
See more details
More for · 4
Interpretable and extrapolation-stable model for predicting nanofluid thermal conductivity.
2026 · cited by 0
Paper 3 notes that existing data-driven approaches face trade-offs between predictive accuracy and physical interpretability, necessitating physics-guided hybrid modeling.
Physics-informed neural network modeling of shock waves by appropriately incorporating equation of state.
2026 · cited by 0
Paper 8 demonstrates that incorporating equations of state directly into neural network loss functions is necessary to accurately capture thermodynamic consistency and shock waves.
AI for atmosphere-ocean sciences: advancements, challenges and ways forward.
2026 · cited by 0
Paper 9 emphasizes that combining AI with foundational physical laws is critical to ensure generalizability and causal consistency, acknowledging the limits of purely data-driven models.
Explainable Deep Learning for Research on the Synergistic Mechanisms of Multiple Pollutants: A Critical Review.
2026 · cited by 0
Paper 10 points out that purely data-driven models suffer from black-box limitations and unresolved conflicts with physical laws, requiring physical mechanism guidance.
More against · 1
Integrating experimental data and machine learning models for solubility prediction of yellow 23 in supercritical carbon dioxide.
2025 · cited by 0
Paper 7 shows that machine learning models can achieve impressive approximations of fluid solubility compared to empirical correlations.