Topic: AI for Chemistry

The integration of artificial intelligence (AI) into the chemical sciences is revolutionizing research, driving innovation and accelerating discovery across diverse subfields. Historically, chemical studies have been constrained by the complexity of vast parameter spaces, multi-dimensional optimization challenges, and the inefficiencies inherent in trial-and-error methodologies. AI is now providing solutions to these challenges, ushering in a new era of precision, predictive capabilities, and enhanced productivity.

This Special Issue, AI for Chemistry, aims to highlight the transformative power of AI in chemical research, showcasing advancements that are reshaping traditional practices and enabling new avenues of exploration in previously uncharted chemical domains. Key topics of focus include:
● AI-driven prediction of molecular and material properties;
● Machine learning-based mechanistic insights into reaction pathways;
● Development of autonomous systems for chemical synthesis and discovery;
● AI-guided design of catalysts and energy materials;
● Data-driven optimization in energy storage, pharmaceuticals, and sustainable chemistry;
● AI-driven computational methods for faster, more accurate simulations;
● Automated synthesis planning, including routes and conditions.

Beyond technical breakthroughs, this Special Issue will also examine the ethical and practical considerations of applying AI to chemical research, emphasizing best practices for ensuring responsible and impactful integration.

By featuring original research articles, comprehensive reviews, and forward-looking perspectives, this Special Issue seeks to foster interdisciplinary collaboration, promote the widespread adoption of AI methodologies, and inspire the development of intelligent tools for addressing critical challenges in the chemical sciences. AI for Chemistry aspires to be a nexus for transformative ideas, empowering the global research community to shape the future of chemistry with AI.

Artificial intelligence, machine learning, autonomous synthesis, catalyst design, property prediction, data-driven optimization, energy materials, ethical integration

25 Jun 2025