Deep Learning and Machine Learning and Their Application on Computational Chemistry

Abstract

Machine learning (ML), coined by Arthur Samuel in 1959, is now a versatile tool in various fields like data mining, computer vision, and medical diagnosis. It builds models from training data for predictions without explicit programming. Deep learning (DL), a subset of machine learning, utilizes neural networks like DNN, CNN, and RNN. In health and safety research, ML applications started in the mid-1990s, focusing on toxicity. Challenges in chemical health and safety research, such as primitive algorithms and skill requirements, limited its early use. Recent advancements in AI and computer science have highlighted the significance of ML and DL. Improved data, computing power, and algorithms have led to autonomous data analyses, transforming solutions in structural engineering (MPSE). In integrated computational materials engineering (ICME), ML and AI are assimilated into efforts like the materials genome initiative (MGI), accelerating autonomous experiments and improving data analytics.