Aims & Scope

Aims:

The Journal of AI Chemistry (JAIC) is a gold open-access, peer-reviewed journal that aims to serve as a core hub for academic exchange, achievement transformation, and innovative methodology dissemination in the interdisciplinary field of artificial intelligence (AI)-enabled chemistry. Amid the transitional period of academic paradigms driven by the in-depth integration of AI technology and chemistry, the journal is committed to breaking the limitation of "one-way communication" in traditional academic publishing and establishing a robust "multi-way academic feedback" mechanism among journals, readers, and contributors. It focuses on connecting "unexplored questions" in basic chemical research, "technical bottlenecks" in industrial applications, and "innovative solutions" empowered by AI, thereby promoting the in-depth integration and collaborative evolution of interdisciplinary knowledge. The journal prioritizes technology implementation and paradigm innovation in AI-enabled chemistry, guiding the practical application of AI technologies (including algorithm models, simulation tools, data-driven methods, etc.) in key chemical scenarios such as molecular design, reaction prediction, process optimization, and performance evaluation, and driving the transformation of the discipline from "theoretical exploration" to "practical verification". Additionally, it adheres to a problem-oriented academic concept, exploring the academic value and application potential of cutting-edge issues, and cultivates an open, shared, and collaborative academic ecosystem by lowering participation thresholds for global scholars, industrial experts, and interdisciplinary researchers. It is published quarterly online by Scilight Press.

Scope:

The scope of Journal of AI Chemistry (JAIC) focuses on the interdisciplinary intersection of AI technology and chemistry, covering all key areas of AI-enabled chemistry research, application, and innovation. Specifically, it includes but is not limited to the following domains:

1) Basic Chemical Theory Exploration: Research on fundamental chemical issues that can be addressed through AI, such as the stable structure threshold of molecular clusters based on quantum chemical calculations and AI cluster analysis, accurate description of electron transfer processes in transition metal catalytic systems during AI potential energy surface construction, and theoretical exploration of complex molecular interaction mechanisms driven by data-driven methods.

2) Experimental Technology and Method Optimization: AI-driven optimization of chemical experimental technologies and analytical methods, including machine learning-based inversion of polymerization reaction kinetic parameters, AI real-time analysis algorithms for in-situ characterization data to improve the accuracy of catalytic reaction mechanism elucidation, and intelligent optimization of experimental design and parameter adjustment (e.g., Bayesian optimization, genetic algorithms).

3) Industrial Application Technical Bottlenecks: AI-based solutions for industrial chemical challenges, such as multi-objective optimization of lithium-ion battery electrolyte components (balancing ionic conductivity, interface stability, and low-temperature performance), AI process simulation for reducing energy consumption and pollutant emissions in chemical process intensification, and intelligent prediction of product quality and process reliability in chemical manufacturing.

4) Innovation in AI-Enabled Chemistry Methods: Development and improvement of AI models and methodologies for chemistry, including enhancing the generalization of graph neural networks (GNN) in complex molecular property prediction, application of Transformer and reinforcement learning in molecular design and reaction yield prediction, AI-assisted simulation combining molecular dynamics (GROMACS/LAMMPS) with potential energy surface optimization, and construction of high-quality chemical databases and AI-driven data mining technologies.

The journal also accepts non-AI-based solutions that demonstrate disruptive innovation (e.g., new chemical theory paradigms, breakthrough experimental methods) provided they elaborate on potential integration pathways with AI technologies. It encompasses problem submissions, solution presentations, and academic discussions related to the above areas, promoting the scenario-based implementation of technical achievements and the collaborative development of the interdisciplinary field.