In a review published in Engineering, scientists explore the burgeoning field of machine learning (ML) and its applications in chemistry. Titled 'Machine Learning for Chemistry: Basics and Applications,' this comprehensive review aims to bridge the gap between chemists and modern ML algorithms, providing insights into the potential of ML in revolutionizing chemical research.
Six popular machine learning models. Decision tree; feedforward neural network ; convolution neural network ; recurrent neural network; graph neural network; transformer neural network. Credit: Yun-Fei Shi et al., scientists explore the burgeoning field of machine learning and its applications in chemistry.
This review serves as an introductory guide to popular chemistry databases, two-dimensional and three-dimensional features used in ML models, and popular ML algorithms. It delves into three specific chemistry fields where ML has made significant progress: retrosynthesis in organic chemistry, ML-potential-based atomic simulation, and ML for heterogeneous catalysis.
The integration of ML models with the Internet offers a promising avenue for sharing ML predictions worldwide.
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