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This paper proposes a symbolic machine learning approach to discover interpretable corrections to the Peng-Robinson equation of state for predicting vapor-liquid equilibrium in hydrocarbon-nitrogen binary mixtures, improving accuracy over the original EOS.
An insightful explainer on the true meaning of entropy, contrasting the common 'disorder' metaphor with a probabilistic interpretation using dice rolling analogies.
This paper presents a novel framework for zero-shot Digital Twins that integrates real-time visual perception with a geometry-agnostic, physics-informed Graph Neural Network. The approach uses a Thermodynamics-Informed GNN to enforce energy conservation and entropy production, achieving physically accurate simulations on unseen geometries without retraining.
This tweet explores the relationship between statistical mechanics and artificial intelligence, citing a paper that proposes a thermodynamic theory for machine learning systems, introducing concepts like temperature, entropy, and energy, and treating the training process as a phase transition.
This paper proposes a thermodynamic measure of intelligence defined as 'rare-valid lift' and argues that recursive self-simulation is necessary and nearly sufficient for high thermodynamic intelligence, making intelligence measurable on a universal scale.
This is a popular science article of over 25,000 characters, starting from the origin of entropy, reviewing the development of dissipative system theory, and exploring a three-level analysis of whether AI belongs to dissipative systems (hardware level, training level, static model).
Proposes a hybrid framework coupling large language models with thermodynamic databases and simplified kinetic models for inorganic synthesis planning, using the niobium–oxygen system as a case study.