AI-Driven Discovery of Low-Carbon Chemical Pathways for Sustainable Industrial Transformation

Richard West

AI-Driven Discovery of Low-Carbon Chemical Pathways for Sustainable Industrial Transformation

Author(s)	Prof. Richard West
Country	United States
Abstract	Industrial chemical production provides materials, fuels, fertilizers, polymers, pharmaceuticals, and intermediates that support modern economies. However, many established chemical pathways depend on fossil-derived feedstocks, energy-intensive reaction conditions, carbon-intensive electricity, hazardous solvents, and processes that generate substantial quantities of waste. Reducing these environmental burdens requires more than incremental plant-level efficiency improvements. It demands the discovery and implementation of alternative catalysts, feedstocks, solvents, reaction sequences, separation methods, and process configurations capable of delivering chemically viable products with lower life-cycle greenhouse gas emissions. Artificial intelligence offers a new approach to this discovery challenge by learning relationships among molecular structures, reaction conditions, catalyst properties, energy requirements, product yields, selectivity, and environmental performance. Machine learning, active learning, reaction-prediction systems, generative models, Bayesian optimization, and autonomous experimentation can help researchers navigate chemical spaces that are too extensive for conventional trial-and-error investigation. This paper examines how these capabilities can support low-carbon chemical-pathway discovery and sustainable industrial transformation. A conceptual review is integrated with an illustrative screening simulation comparing unguided candidate selection with AI-guided active learning. Within the simulated evaluation budget of 100 candidates, the AI-guided workflow identifies 30 promising pathways compared with 13 under unguided screening. These values are methodological illustrations and do not represent industrial experimental findings. The analysis indicates that AI can improve candidate prioritization, connect molecular discovery with process-level assessment, and support multi-objective decisions involving emissions, energy, yield, selectivity, toxicity, cost, and scalability. Its usefulness remains dependent on reliable chemical data, mechanistic validation, uncertainty quantification, life-cycle boundaries, experimental confirmation, and human oversight. The study proposes that responsible AI-guided pathway discovery should combine data-driven prediction with chemical theory, high-throughput experimentation, process simulation, techno-economic assessment, and life-cycle analysis. Such an integrated framework can accelerate industrial decarbonization while reducing the risk that apparently efficient reactions transfer emissions or environmental burdens to other stages of the value chain.
Keywords	artificial intelligence; low-carbon chemistry; sustainable chemical pathways; catalyst discovery; active learning; industrial decarbonization; green chemistry; process systems engineering.
Field	Engineering
Published In	Volume 8, Issue 4, July-August 2026
Published On	2026-08-06

View / Download PDF File

About AJAMRI Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+1-872-302-3851	editor@ajamri.com

American Journal of Advanced Multidisciplinary Research and Innovation

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

AI-Driven Discovery of Low-Carbon Chemical Pathways for Sustainable Industrial Transformation

Share this