Role of Machine Learning in Inorganic and Coordination Chemistry: Applications and Challenges

Author(s)	Mukesh Rani
Country	India
Abstract	Machine learning (ML) is a branch of artificial intelligence. ML is revolutionizing various areas of chemistry by enhancing predictions of chemical properties, uncovering hidden patterns in complex data, and accelerating the discovery of new compounds. Machine learning is also accelerating research and development in the complex and limited data fields of coordination and inorganic chemistry. This paper examines the advancements in inorganic and coordination chemistry enabled by the application of ML. These advancements include the prediction of chemical properties, the design of effective catalysts, spectroscopy, and the analysis of compound structures. This paper discusses case studies where ML techniques are effectively applied to research and development in inorganic and coordination chemistry. In the end of this paper, the key challenges in the path of using ML techniques in fostering innovation in inorganic and coordination chemistry are highlighted.
Keywords	ML, inorganic chemistry, coordination chemistry, property prediction, catalyst design, spectroscopy, data scarcity, model interpretability.
Field	Chemistry
Published In	Volume 16, Issue 2, April-June 2025
Published On	2025-06-25
DOI	https://doi.org/10.71097/IJSAT.v16.i2.6541

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