Call For Papers
Smart Chemical Engineering
Artificial Intelligence Coupled with Thermodynamics: From Fundamental Theory to Process Engineering
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
Smart Chemical Engineering
Deadline: 10 May 2026Artificial Intelligence for Chemical Product Engineering: From Process Regulation to Product Customization
Smart Chemical Engineering (SCE) is announcing a Call for Papers for the topic “Artificial Intelligence for Chemical Product Engineering: From Process Regulation to Product Customization” . Chemical engineering has evolved from traditional chemical process engineering toward a new era that integrates process and product engineering, with the goal of precisely customizing chemical products. Artificial intelligence (AI) is deeply permeating all aspects of chemical engineering, with applications extending beyond individual unit operations (e.g., separation and reaction) to integrated processes/systems, thereby advancing optimization and regulation of processes and the targeted customization of high-performance products. Smart Chemical Engineering (SCE) hereby announces this Call for Papers, to reflect the deep integration trend between AI and chemical engineering, as well as to highlight AI’s pivotal role in driving the shift from process-oriented to product-oriented in chemical engineering. We sincerely invite scholars worldwide to contribute pioneering findings that showcase frontier advances in AI-empowered chemical product engineering. We welcome original research articles, reviews and perspectives that explore the applications and breakthroughs of machine learning and other artificial intelligence technologies in the innovation development of chemical product engineering. Topics of interest include, but are not limited to: Correlation Mechanism of "Process Conditions-Product Structure-Performance" in AI-based Chemical Product Engineering AI-driven Multi-Objective Process Optimization, Regulation, and System Integration Focusing on Product Structure and Performance AI-assisted Multi-dimensional Regulation Mechanism and Action Principle for Chemical Products Core Theories, Key Methods and Innovations Algorithms of Artificial Intelligence for Chemical Product Engineering Keywords: artificial intelligence; machine learning; chemical product engineering; chemical process engineering; fundamental mechanism Academic Editors: Prof. Zheng-Hong Luo（Shanghai Jiaotong University/ Ningxia University） Dr. Jiantao Li（Ningxia University） Dr. Jie Jin（Shanghai Jiaotong University） Submission Deadline: 30 June 2026 Office Contact: sce@sciltp.com Submission Link: https://sciflux.org/authors/submissions
Smart Chemical Engineering
Deadline: 30 Jun 2026Artificial Intelligence in Chemical Reaction Engineering: From Reaction Kinetics to Industrial Applications
Artificial Intelligence (AI) is revolutionizing chemical reaction engineering by enabling deeper insights into reaction kinetics, computational fluid dynamics (CFD), and reactor design. In response to the growing need for smarter, more efficient, and scalable reaction processes, this special issue in Smart Chemical Engineering (SCE) invites pioneering contributions that highlight the integration of AI in understanding, modeling, and optimizing chemical reactions as well as multiphase reactors. We welcome original research articles, reviews, and perspective papers that explore the application of machine learning and all other AI techniques to advance reaction engineering. Contributions may address AI-accelerated modeling of reaction kinetics, AI-enhanced CFD simulations, intelligent reactor design and control, as well as AI-assisted scale-up and optimization of reaction processes. Topics of interest include, but are not limited t o : Machine learning and AI methodology for reaction kinetic modeling AI-enhanced CFD for multiphase and reactive flows Machine learning in reactor design, optimization, and control Intelligent scale-up and digital twins of chemical reactors Intelligent monitoring and fault detection Keywords: artificial Intelligence; reaction engineering; reaction kinetics; computational fluid dynamics (CFD); machine learning; reactor design; process optimization Prof. Mao Ye Affiliation: Dalian Institute of Chemical Physics, Chinese Academy of Sciences Homepage: https://people.ucas.ac.cn/~maoye?language=en
Smart Chemical Engineering
Deadline: 15 Feb 2026
Suite 4002 Level 4, 447 Collins Street, Melbourne, Victoria 3000, Australia
General Inquiries: info@sciltp.com