STM Journals Peer-reviewed research from Consortium e-Learning Network

Journal of Artificial Intelligence Research & Advances Review Article

AI – Driven Smart Laboratory for Synthetic Organic Chemistry – A Literature Review

  1. Shaikh Sanobar Department of Engineering Chemistry, Al-Kabir Polytechnic
  2. Altaf Ahmad Department of Engineering Chemistry, Al-Kabir Polytechnic

Abstract

The traditional chemistry laboratory is often subjective, time consuming and skill intensive that may lead to inconsistencies in the desired results. The advent of Artificial Intelligence (AI) and its speedy incorporation into the field of chemistry is transforming traditional synthetic laboratories into intelligent, automated systems that combine hardware, software and AI into a unified framework. This paper presents a comprehensive overview of AI – driven laboratory setup for organic synthesis, highlighting the key features such as automation, adaptability, and effective coordination of experimental processes. These laboratories can operate with enhanced precision and flexibility using standardized experimental workstations with intelligent scheduling and concerted resource management. Making use of multimodal data platforms and advanced AI approaches together with Large Language Models (LLM) and Machine Learning, it is feasible to create closed – loop experimental workflows. These workflows allow prediction analysis of reaction outcomes, designation of retrosynthetic pathways and optimization of reaction parameters. The study also focuses onto a cloud – based platform that facilitates resource sharing, intelligent management of laboratory equipment, and the integration of large scale data. The model supports collaborative data accumulation and knowledge exchange which accelerates the progress of intelligent organic synthesis. The paper also includes real-world examples that describe practical approaches for building next generation automated and AI – enabled chemical laboratories.

Keywords

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