Smart Chemical Engineering (SCE) is announcing a Call for Papers for the topic “Artificial Intelligence Coupled with Thermodynamics: From Fundamental Theory to Process Engineering”.

Thermodynamics provides the foundational principles governing energy, entropy, and equilibrium in physical and chemical systems. Artificial Intelligence (AI) is redefining the landscape of thermodynamics by enabling molecular-level insights, rapid property prediction, and complex process simulation and optimization. While AI accelerates thermodynamic research, thermodynamic laws simultaneously empower AI models by providing physical constraints and enhancing interpretability. Smart Chemical Engineering (SCE) hereby announces this Call for Papers, which will also be featured as an initiative by the Big Data and Intelligent Design Committee of the Chemical Industry and Engineering Society of China. We sincerely invite scholars to submit pioneering contributions that highlight the integration of AI and thermodynamics across multiple scales.

We welcome original research articles, reviews, and perspective papers that explore the application of machine learning and other AI techniques to advance both macroscopic and microscopic thermodynamics. Contributions may explore the synergy between AI and thermodynamics, which holds immense potential to revolutionize how we model, predict, optimize, and control complex processes—from molecular-scale simulations to industrial-scale process engineering.

Topics of interest include, but are not limited to:

• AI for thermodynamic property prediction and uncertainty quantification
• AI-enhanced modeling of equations of state, chemical equilibria, phase equilibria, and transport properties
• Physics-informed machine learning models embedding thermodynamic laws (e.g., conservation of energy, second law constraints)
• AI-driven discovery of novel materials with tailored thermodynamic behavior
• Applications in carbon capture, hydrogen economy, battery thermal management, sustainable manufacturing, renewable energy systems, pharmaceutical engineering, etc.

Keywords: artificial intelligence; thermodynamics; molecular simulation; physics-informed machine learning; process simulation and optimization

Academic Editor:

Prof. Hongliang Qian
China Pharmaceutical University

Submission Deadline: 10 May 2026
Office Contact: sce@sciltp.com
Submission Link: https://sciflux.org/authors/submissions